1. A system for diagnosing depressive disorder in females based on facial expressions, comprising: an image capture module configured to obtain facial images of the subject; a preprocessing unit designed to enhance and normalize said facial images; a deep learning model trained to identify facial expression patterns correlated with depressive disorders; a diagnosis output module to present the results; and a database storing labelled facial expression data for training and calibration.

2. The system of claim 1, wherein the deep learning model comprises a convolutional neural network tailored for facial expression analysis.

3. The system of claim 1, wherein the preprocessing unit further employs face detection algorithms to isolate facial regions from obtained images.

4. The system of claim 1, further comprising a feedback module that allows healthcare professionals to validate, correct, or augment diagnosis results.

5. The system of claim 1, wherein the database is continuously updated, and the deep learning model is periodically retrained to improve diagnosis accuracy.

6. A method for diagnosing depressive disorder in females using facial expressions, comprising the steps of: capturing facial images of a female subject; preprocessing and normalizing the captured images to enhance facial features; processing the pre-processed images using a deep learning model trained on facial expression data; determining the likelihood of depressive disorder based on model outcomes; and presenting the diagnosis results.

7. The method of claim 6, further comprising the step of updating the deep learning model with new labelled data and retraining to refine diagnosis capability.

8. The method of claim 6, wherein the step of processing involves a convolutional neural network analyzing spatial patterns in the facial images.

9. The method of claim 6, further including the steps of: receiving feedback from healthcare professionals on diagnosis accuracy; augmenting the dataset with this feedback; and recalibrating the deep learning model accordingly.

10. The method of claim 6, wherein the preprocessing step includes isolating facial regions from the captured images using face detection algorithms and then normalizing the isolated facial images. Deep Learning Based Diagnosis of Depressive Disorder in Females using Facial Expressions Abstract The invention presents a novel system tailored for diagnosing depressive disorders in females utilizing facial expressions as primary indicators. Central to the system is an image capture module proficient in obtaining high-definition facial images of subjects. Ensuing image acquisition, a preprocessing unit refines and standardizes these images, priming them for analysis. A sophisticated deep learning model, informed by a robust database of labeled facial expressions, deciphers subtle facial patterns and correlates them with depressive symptoms. The culmination of this process results in the diagnosis output module presenting a comprehensive analysis. The system's inherent adaptability and precision are further enhanced by its dynamic database, which facilitates continuous training and calibration, ensuring state-of-the-art diagnostic accuracy. , Claims:Claims :

1. A system for diagnosing depressive disorder in females based on facial expressions, comprising: an image capture module configured to obtain facial images of the subject; a preprocessing unit designed to enhance and normalize said facial images; a deep learning model trained to identify facial expression patterns correlated with depressive disorders; a diagnosis output module to present the results; and a database storing labelled facial expression data for training and calibration.

2. The system of claim 1, wherein the deep learning model comprises a convolutional neural network tailored for facial expression analysis.

3. The system of claim 1, wherein the preprocessing unit further employs face detection algorithms to isolate facial regions from obtained images.

4. The system of claim 1, further comprising a feedback module that allows healthcare professionals to validate, correct, or augment diagnosis results.

5. The system of claim 1, wherein the database is continuously updated, and the deep learning model is periodically retrained to improve diagnosis accuracy.

6. A method for diagnosing depressive disorder in females using facial expressions, comprising the steps of: capturing facial images of a female subject; preprocessing and normalizing the captured images to enhance facial features; processing the pre-processed images using a deep learning model trained on facial expression data; determining the likelihood of depressive disorder based on model outcomes; and presenting the diagnosis results.

7. The method of claim 6, further comprising the step of updating the deep learning model with new labelled data and retraining to refine diagnosis capability.

8. The method of claim 6, wherein the step of processing involves a convolutional neural network analyzing spatial patterns in the facial images.

9. The method of claim 6, further including the steps of: receiving feedback from healthcare professionals on diagnosis accuracy; augmenting the dataset with this feedback; and recalibrating the deep learning model accordingly.

10. The method of claim 6, wherein the preprocessing step includes isolating facial regions from the captured images using face detection algorithms and then normalizing the isolated facial images.

Description:Deep Learning Based Diagnosis of Depressive Disorder in Females using Facial Expressions
Field of the Invention
[0001] The present invention pertains to the interdisciplinary domain of medical diagnostics and artificial intelligence. Specifically, the invention is rooted in the application of deep learning techniques to psychiatric evaluations, focusing on the non-invasive diagnosis of depressive disorders in females based on facial expressions. By harnessing the intricate patterns and nuances inherent in facial gestures and movements, the system leverages advanced machine learning architectures to offer a timely and efficient means of detecting depressive tendencies, thereby facilitating early intervention and personalized therapeutic approaches.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] The accurate diagnosis of depressive disorders has long been a focal point of psychiatric research and clinical practice. Depressive disorders, particularly in females, present a myriad of both overt and subtle symptoms, with emotional and behavioral manifestations being among the most pronounced. Given the predominantly subjective nature of traditional diagnostic methods, which often rely on self-reporting or clinical observation, there has been a growing emphasis on devising objective, data-driven techniques to detect and diagnose depression.
[0004] Facial expressions, as a tangible reflection of underlying emotional states, have been a consistent area of interest in this quest. Historically, clinicians have been trained to identify specific facial cues, such as diminished expressiveness or prolonged periods of sadness, which may indicate depression. However, these observational methods are inherently limited by their subjectivity and potential for human error.
[0005] Enter the domain of machine learning, and more specifically, deep learning. With the advent of advanced computational capabilities and sophisticated algorithms, it became feasible to analyze intricate patterns in facial expressions with unprecedented precision. Deep learning, characterized by the use of neural networks with multiple layers (or "deep" networks), has been particularly transformative in this context due to its capability to learn and recognize complex, non-linear patterns from large datasets.
[0006] In the realm of prior art, several initiatives have harnessed deep learning for emotion recognition. For instance, convolutional neural networks (CNNs), a subtype of deep learning models optimized for image processing, have been employed to classify facial images into distinct emotional categories, such as happiness, sadness, anger, and so forth. While initially aimed at broader emotion recognition for applications like human-computer interaction, these models laid the groundwork for more specialized applications in mental health diagnostics.
[0007] Specifically focusing on depressive disorders in females, there have been exploratory studies utilizing deep learning to differentiate between facial expressions of clinically diagnosed depressed individuals and those without depression. One notable example leveraged a CNN model trained on thousands of labeled images to detect micro-expressions – fleeting, involuntary facial movements – that are often overlooked in clinical settings but may be indicative of underlying depressive tendencies.
[0008] Another pioneering approach in the prior art combined deep learning with temporal analysis. Recognizing that depression isn't just about the specific expressions but also their duration and frequency, recurrent neural networks (RNNs), adept at handling sequential data, were integrated to track the progression of facial expressions over time. This provided a dynamic view of an individual's emotional state, offering richer insights than static images alone.
[0009] While these approaches have shown promise, there are also inherent challenges associated with using deep learning for this purpose. The potential for bias in model training, given the cultural, ethnic, and individual variability in facial expressions, has been a recurring concern. Moreover, the interpretability of deep learning models, often dubbed as "black boxes", raised questions about their applicability in clinical settings where understanding the rationale behind a diagnosis is crucial.
[00010] In summation, the intersection of deep learning and psychiatric diagnostics represents a burgeoning field with immense potential. The prior art signifies substantial strides in harnessing technology to aid the objective diagnosis of depressive disorders in females using facial expressions. Yet, it also underscores the need for continued research, not only to refine the accuracy and reliability of these methods but to ensure they are culturally sensitive, transparent, and ethically sound.
[00011] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
[00012] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
Summary
[00013] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00014] The following paragraphs provide additional support for the claims of the subject application.
[00015] The present invention pertains to the interdisciplinary domain of medical diagnostics and artificial intelligence. Specifically, the invention is rooted in the application of deep learning techniques to psychiatric evaluations, focusing on the non-invasive diagnosis of depressive disorders in females based on facial expressions. By harnessing the intricate patterns and nuances inherent in facial gestures and movements, the system leverages advanced machine learning architectures to offer a timely and efficient means of detecting depressive tendencies, thereby facilitating early intervention and personalized therapeutic approaches.
[00016] Depression, a pervasive mental health condition, often remains underdiagnosed due to its subtle manifestations and the varied ways it presents in individuals. The system described aims to enhance the detection rate of depressive disorders in females, specifically through the lens of facial expressions, which are known to hold a wealth of emotion-related information.
[00017] The system's initiation step involves capturing facial images using a dedicated image capture module. This module is adept at obtaining high-definition images, ensuring the intricate nuances of facial expressions are well-represented. Following image capture, the system employs a preprocessing unit. Beyond just basic image enhancements, this unit, leverages face detection algorithms to accurately isolate facial regions, removing any extraneous image data. This ensures the ensuing analysis is purely centered on the expressive components of the face.
[00018] The heart of the system lies in its deep learning model, responsible for decoding the emotional context of captured facial expressions. Elaborating on the intricacies of this model, revealing it to be a convolutional neural network (CNN) tailored for facial expression analysis. CNNs, celebrated for their prowess in image data interpretation, delve deep into the minute facial patterns, extracting features that may correlate with depressive disorders.
[00019] The results from this analytical phase are then funneled to a diagnosis output module. Here, users are provided with a clear, intuitive representation of the diagnosis, aiding both patients and healthcare professionals in understanding the results.
[00020] Recognizing the importance of continuous learning and the dynamic nature of mental health presentations, the system is backed by a robust database. This database harbours labelled facial expression data, serving dual purposes: training the deep learning model and calibrating it for improved accuracy. Furthermore, this database isn't static; it is periodically updated with new data. Correspondingly, the deep learning model undergoes periodic retraining, ensuring the system's diagnostic accuracy is consistently honed.
[00021] A significant feature is the system's feedback module. This interactive platform facilitates healthcare professionals in validating the diagnosis. They can correct or augment results based on their clinical judgment, ensuring the fusion of machine precision with human expertise.
[00022] In conclusion, this innovative system promises a paradigm shift in the diagnosis of depressive disorders in females. By synergizing advanced imaging, deep learning, and clinical feedback, it provides a holistic, accurate, and dynamic platform for detecting subtle signs of depression through facial expressions, bridging the gap between technology and mental health care.
[00023] Depressive disorders, with their myriad and often nuanced presentations, require innovative and precise diagnostic methods to ensure timely intervention and care. The outlined method aims to harness the intricate language of facial expressions, specifically in females, to diagnose depressive disorders more accurately.
[00024] Initiating the process, facial images of the female subject are captured. This foundational step is paramount to ensure that the subsequent analysis is built on high-quality, representative data. Following this, the captured images undergo a rigorous preprocessing phase. As emphasized, this isn't just a superficial image enhancement. The method employs face detection algorithms to pinpoint and isolate facial regions meticulously, ensuring extraneous elements are eliminated. Subsequent to this isolation, normalization is executed on these facial images, sharpening and standardizing them for the next phase.
[00025] The deep learning model then takes center stage. The backbone of this processing step is a convolutional neural network (CNN). Renowned for its prowess in image analytics, the CNN delves into the spatial patterns of the facial images, identifying and interpreting patterns that may signify the presence of depressive symptoms. Given that facial expressions hold a labyrinth of emotional cues, the CNN's depth and complexity ensure these cues are not missed but rather intricately analyzed.
[00026] Once the CNN completes its analysis, the method transitions to an evaluation phase. The outcomes of the model are scrutinized to gauge the likelihood of a depressive disorder in the subject. This determination is not merely a binary outcome but is presented in a nuanced manner that underscores the likelihood and severity.
[00027] A salient feature of this method is its adaptive nature. Recognizing that medical diagnosis is an evolving field and that feedback is pivotal, the method integrates a feedback loop. Healthcare professionals can provide insights regarding the accuracy of diagnoses, enriching the dataset with their clinical observations. This feedback is then used to recalibrate the deep learning model, ensuring its continually refined for higher precision. Additionally, the deep learning model periodically gets updated with new labeled data, fortifying its training and enhancing its diagnostic capabilities.
[00028] In essence, this method epitomizes the seamless amalgamation of technology and healthcare. By leveraging advanced neural networks, refining processes through expert feedback, and grounding it all in the profound language of facial expressions, the method promises a revolutionary stride in the accurate diagnosis of depressive disorders in females.
Brief Description of the Drawings
[00029] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00030] FIG. 1 represents an architectural overview of a system for diagnosing depressive disorder in females based on facial expressions, according to some embodiments of the present disclosure.
[00031] FIG. 2 shows an exemplary detailed schematic flow diagram of a method for diagnosing depressive disorder in females based on facial expressions, according to some embodiments of the present disclosure.
Detailed Description
[00032] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a

limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00033] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00034] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00035] The present invention pertains to the interdisciplinary domain of medical diagnostics and artificial intelligence. Specifically, the invention is rooted in the application of deep learning techniques to psychiatric evaluations, focusing on the non-invasive diagnosis of depressive disorders in females based on facial expressions. By harnessing the intricate patterns and nuances inherent in facial gestures and movements, the system leverages advanced machine learning architectures to offer a timely and efficient means of detecting depressive tendencies, thereby facilitating early intervention and personalized therapeutic approaches.
[00036] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00037] With advancements in the digital realm, AI-based solutions have emerged for predicting, monitoring, and treating mental health disorders. The relevance of such digital methods was especially highlighted during the COVID-19 pandemic, when direct interactions between patients and doctors were challenging. AI not only assists in diagnosing mental health issues but also offers personalized interventions, including feedback via virtual reality (VR). However, VR requires specialized equipment, which is often inaccessible to many.
[00038] In contrast, smartphones are widespread and allow for ecological momentary assessments (EMA). EMA is an innovative technique that facilitates behavioural interventions via mobile phones, engaging patients in their daily lives. The World Health Organization states that 5% of adults globally suffer from depressive disorders, which can sometimes lead to severe consequences like suicide. Mental health disorders can vary in intensity and require timely intervention. Despite the prevalence of these disorders in both urban and rural areas, rural India, in particular, is riddled with myths surrounding mental health.
[00039] This invention aims to benefit both urban and rural India by emphasizing accessible and effective solutions, especially for rural communities. The initiative seeks to establish a platform that combines questionnaires with facial expression analysis to assist those in need. In rural India, especially among women, the situation is dire due to enduring myths. Women often refrain from seeking professional help because of societal fears and instead resort to local, mostly religious remedies. Alarmingly, limited research exists on women's mental health in the Indian context, underlining the need for comprehensive solutions.
[00040] Depressive disorders are a prevalent mental health concern affecting millions of individuals worldwide. Traditional methods of diagnosis primarily rely on subjective patient reports and clinical interviews, which can be prone to biases and inaccuracies. This disclosure introduces a novel approach for diagnosing depressive disorder in females based on facial expressions using a sophisticated system 100 comprising an image capture module 102, preprocessing unit 104, deep learning model 106, diagnosis output module 108, and a labelled facial expression database 110. The proposed system 100 leverages advanced technology to objectively identify facial expression patterns correlated with depressive disorders, potentially revolutionizing the field of mental health diagnostics.
[00041] Depressive disorders pose a substantial burden on society, leading to significant personal suffering and economic costs. Timely and accurate diagnosis is crucial for effective treatment and intervention. According to a pictorial portrayal in FIG. 1, illustrating an architectural setup of the system 100 aims to enhance the diagnostic process by utilizing facial expression analysis, a potential biomarker for depressive disorders. This system 100 capitalizes on recent advancements in deep learning and computer vision to automatically detect subtle changes in facial expressions that might indicate the presence of depressive symptoms.
[00042] In an embodiment, the system 100 starts by obtaining facial images of the subject using a variety of imaging devices, such as smartphones, webcams, or dedicated cameras. These images serve as the input for subsequent analysis. High-quality image capture is essential to ensure accurate facial expression analysis.
[00043] Once facial images are captured, they undergo preprocessing to enhance and normalize the images. The preprocessing unit employs techniques such as noise reduction, contrast adjustment, and illumination normalization to improve the quality of the input images. Additionally, face detection algorithms are applied to isolate facial regions, eliminating extraneous information and improving the accuracy of subsequent analysis.
[00044] At the core of the system lies a deep learning model, specifically a convolutional neural network (CNN) tailored for facial expression analysis. CNNs are well-suited for image-based tasks due to their ability to automatically learn hierarchical features from raw pixel data. The deep learning model is trained on a vast amount of labeled facial expression data collected from individuals with and without depressive disorders.
[00045] The training process involves exposing the model to a diverse range of facial expressions, including neutral, happy, sad, and others. By learning the subtle differences in expression patterns, the model becomes capable of identifying features that may indicate the presence of depressive symptoms. The dataset used for training comprises a broad spectrum of ages, ethnicities, and other relevant factors to ensure the model's robustness and generalizability.
[00046] After the deep learning model analyzes the input facial image, it generates a diagnosis output. This output is a prediction of the likelihood that the individual exhibits facial expression patterns correlated with depressive disorders. The output may include a confidence score or a binary classification result indicating the presence or absence of depressive symptoms.
[00047] The system's accuracy and reliability hinge on the quality and diversity of the labeled facial expression data used for training and calibration. The database stores this data, which includes facial images annotated with corresponding labels indicating emotional states. To maintain the system's effectiveness over time, the database is continuously updated with new data. Periodic retraining of the deep learning model using the updated database ensures that the model remains up-to-date and adaptive to changes in facial expression patterns.
[00048] Summarizing the working of the system 100, facial images of the subject are captured using appropriate devices. Captured images undergo preprocessing to enhance quality and isolate facial regions. The pre-processed images are fed into the deep learning model for facial expression analysis. The model generates a diagnosis output, indicating the presence or absence of depressive symptoms. A feedback module enables healthcare professionals to review, validate, correct, or augment diagnosis results if necessary. The labeled facial expression database is continuously updated with new data, and the deep learning model is periodically retrained to improve diagnosis accuracy.
[00049] The proposed system offers several advantages. The system reduces reliance on subjective patient reports, enhancing diagnostic objectivity. Early detection of depressive symptoms allows for timely intervention, improving patient outcomes. The system can be used for remote monitoring, providing continuous support to individuals at risk.
[00050] Facial expression analysis may reduce stigma associated with discussing mental health issues. The feedback module fosters collaboration between the system and healthcare professionals, ensuring accurate diagnoses. For example, a young woman signs up for the system, concerned about her recent mood changes. She captures facial images daily using her smartphone. The system's deep learning model consistently detects subtle changes in her facial expressions, indicating the possibility of developing depressive symptoms. The system alerts her to consider seeking professional help, leading to an early intervention that prevents the progression of her condition.
[00051] In yet another epitome of illustration, a mental health professional uses the system in her practice. She inputs facial images of her patients and receives diagnosis outputs from the system. In cases where the system identifies potential depressive symptoms, the healthcare professional reviews the results. She can either validate the diagnosis or provide corrections based on her clinical expertise, refining the system's accuracy over time.
[00052] Referring to one or more preceding embodiments, the proposed system 100 for diagnosing depressive disorder in females based on facial expressions presents a cutting-edge solution to an important healthcare challenge. By leveraging advancements in deep learning, computer vision, and facial expression analysis, the system has the potential to revolutionize the field of mental health diagnostics. It offers an objective and technology-driven approach to identify individuals at risk of depressive disorders, enabling timely intervention and improved patient outcomes. Continuous database updates and periodic model retraining ensure the system's adaptability and accuracy over time, making it a valuable tool for both individuals and healthcare professionals.
[00053] The present invention pertains to a method 200 for diagnosing depressive disorder in females using facial expressions. The method 200 comprises a series of steps designed to objectively and accurately determine the likelihood of depressive disorder based on facial expression patterns.
[00054] Figuratively depicted in FIG. 2, representing a flow diagram of the method 200 for diagnosing depressive disorder in females using facial expressions, comprising the steps of (at step 202) capturing facial images of a female subject, (at step 204) preprocessing and normalizing the captured images to enhance facial features, (at step 206) processing the pre-processed images using a deep learning model trained on facial expression data, (at step 208) determining the likelihood of depressive disorder based on model outcomes, and (at step 210) presenting the diagnosis results.
[00055] In this step, facial images of a female subject are captured using suitable imaging devices such as cameras, smartphones, or webcams. The images serve as input for subsequent analysis. For example, a female individual captures a series of facial images using her smartphone's front-facing camera.
[00056] The captured facial images are pre-processed and normalized to enhance facial features and improve analysis accuracy. Techniques such as noise reduction, contrast adjustment, and illumination normalization are applied to the images. Face detection algorithms are further utilized to isolate facial regions from the images, eliminating background noise. Subsequently, the isolated facial images are normalized to a standardized scale and format. As an example, a noise-reduction algorithm removes artifacts from the captured images, and a face detection algorithm identifies and isolates the facial regions.
[00057] In an embodiment, the preprocessed and normalized facial images are fed into a deep learning model that has been trained on a comprehensive dataset of labeled facial expression data. The deep learning model analyzes the images to identify subtle facial expression patterns associated with depressive disorders. The model's ability to learn these patterns is a result of its exposure to a diverse range of expressions and emotions during the training phase. A convolutional neural network (CNN) is an example of a deep learning model used for this purpose.
[00058] Based on the analysis performed by the deep learning model, the method determines the likelihood of depressive disorder in the female subject. The model's outcomes may include a confidence score or a binary classification result indicating the presence or absence of depressive symptoms. For instance, the deep learning model computes a confidence score that represents the likelihood of the female subject having depressive symptoms based on the input facial image.
[00059] In an embodiment, the results of the diagnosis are presented to the user or relevant healthcare professionals. This can be in the form of a visual output, textual information, or an audio notification. The presented results allow individuals and professionals to make informed decisions regarding further actions, such as seeking professional help or intervention. An example output might be a message stating, "The analysis indicates a moderate likelihood of depressive symptoms."
[00060] The method 200 described in the preceding embodiment can be further enhanced by incorporating a step to update and retrain the deep learning model. The deep learning model is updated periodically with new labeled facial expression data. As the labeled dataset continues to grow, the model's capability to accurately diagnose depressive disorders improves. The updated dataset may include newly collected facial images and feedback data provided by healthcare professionals. Subsequently, the deep learning model is retrained using this updated dataset to refine its diagnosis capabilities and adapt to changing expression patterns.
[00061] In an embodiment, the method 200 can also involve steps to integrate feedback from healthcare professionals and refine the deep learning model accordingly. Healthcare professionals have the option to provide feedback on the accuracy of the model's diagnoses. This feedback, based on their clinical expertise, can validate or correct the diagnosis outcomes. The feedback data is then incorporated into the labeled dataset, enriching it with real-world insights. The deep learning model is recalibrated and refined using this augmented dataset, enhancing its accuracy and reliability over time. This iterative process ensures that the model evolves to accurately diagnose depressive disorders. For instance, a healthcare professional reviews a diagnosis and provides feedback that the model misinterpreted a specific expression as indicative of depressive symptoms, leading to model adjustments.
[00062] In an embodiment, the preprocessing step can be elaborated further. The preprocessing step includes a two-fold process. First, face detection algorithms are applied to accurately locate and isolate facial regions within the captured images. This eliminates irrelevant background information and focuses exclusively on the facial features. Second, the isolated facial images are normalized to ensure consistent scale, orientation, and lighting conditions. The normalization process enhances the comparability of different facial expressions, enabling the deep learning model to accurately learn expression patterns.
[00063] Thus, the embodiments outlined herein describe a comprehensive method 200 for diagnosing depressive disorder in females based on facial expressions. By leveraging image capture, preprocessing, deep learning analysis, and diagnosis result presentation, this method offers an objective and technology-driven approach to mental health diagnostics. The incorporation of model update and retraining, feedback integration, and detailed preprocessing further enhances the accuracy and adaptability of the system. This innovative method has the potential to revolutionize mental health assessment, providing timely intervention and improved patient outcomes.
[00064] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00065] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
[00066] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00067] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00068] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00069] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Deep Learning Based Diagnosis of Depressive Disorder in Females using Facial Expressions
Field of the Invention
[0001] The present invention pertains to the interdisciplinary domain of medical diagnostics and artificial intelligence. Specifically, the invention is rooted in the application of deep learning techniques to psychiatric evaluations, focusing on the non-invasive diagnosis of depressive disorders in females based on facial expressions. By harnessing the intricate patterns and nuances inherent in facial gestures and movements, the system leverages advanced machine learning architectures to offer a timely and efficient means of detecting depressive tendencies, thereby facilitating early intervention and personalized therapeutic approaches.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] The accurate diagnosis of depressive disorders has long been a focal point of psychiatric research and clinical practice. Depressive disorders, particularly in females, present a myriad of both overt and subtle symptoms, with emotional and behavioral manifestations being among the most pronounced. Given the predominantly subjective nature of traditional diagnostic methods, which often rely on self-reporting or clinical observation, there has been a growing emphasis on devising objective, data-driven techniques to detect and diagnose depression.
[0004] Facial expressions, as a tangible reflection of underlying emotional states, have been a consistent area of interest in this quest. Historically, clinicians have been trained to identify specific facial cues, such as diminished expressiveness or prolonged periods of sadness, which may indicate depression. However, these observational methods are inherently limited by their subjectivity and potential for human error.
[0005] Enter the domain of machine learning, and more specifically, deep learning. With the advent of advanced computational capabilities and sophisticated algorithms, it became feasible to analyze intricate patterns in facial expressions with unprecedented precision. Deep learning, characterized by the use of neural networks with multiple layers (or "deep" networks), has been particularly transformative in this context due to its capability to learn and recognize complex, non-linear patterns from large datasets.
[0006] In the realm of prior art, several initiatives have harnessed deep learning for emotion recognition. For instance, convolutional neural networks (CNNs), a subtype of deep learning models optimized for image processing, have been employed to classify facial images into distinct emotional categories, such as happiness, sadness, anger, and so forth. While initially aimed at broader emotion recognition for applications like human-computer interaction, these models laid the groundwork for more specialized applications in mental health diagnostics.
[0007] Specifically focusing on depressive disorders in females, there have been exploratory studies utilizing deep learning to differentiate between facial expressions of clinically diagnosed depressed individuals and those without depression. One notable example leveraged a CNN model trained on thousands of labeled images to detect micro-expressions – fleeting, involuntary facial movements – that are often overlooked in clinical settings but may be indicative of underlying depressive tendencies.
[0008] Another pioneering approach in the prior art combined deep learning with temporal analysis. Recognizing that depression isn't just about the specific expressions but also their duration and frequency, recurrent neural networks (RNNs), adept at handling sequential data, were integrated to track the progression of facial expressions over time. This provided a dynamic view of an individual's emotional state, offering richer insights than static images alone.
[0009] While these approaches have shown promise, there are also inherent challenges associated with using deep learning for this purpose. The potential for bias in model training, given the cultural, ethnic, and individual variability in facial expressions, has been a recurring concern. Moreover, the interpretability of deep learning models, often dubbed as "black boxes", raised questions about their applicability in clinical settings where understanding the rationale behind a diagnosis is crucial.
[00010] In summation, the intersection of deep learning and psychiatric diagnostics represents a burgeoning field with immense potential. The prior art signifies substantial strides in harnessing technology to aid the objective diagnosis of depressive disorders in females using facial expressions. Yet, it also underscores the need for continued research, not only to refine the accuracy and reliability of these methods but to ensure they are culturally sensitive, transparent, and ethically sound.
[00011] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
[00012] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
Summary
[00013] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00014] The following paragraphs provide additional support for the claims of the subject application.
[00015] The present invention pertains to the interdisciplinary domain of medical diagnostics and artificial intelligence. Specifically, the invention is rooted in the application of deep learning techniques to psychiatric evaluations, focusing on the non-invasive diagnosis of depressive disorders in females based on facial expressions. By harnessing the intricate patterns and nuances inherent in facial gestures and movements, the system leverages advanced machine learning architectures to offer a timely and efficient means of detecting depressive tendencies, thereby facilitating early intervention and personalized therapeutic approaches.
[00016] Depression, a pervasive mental health condition, often remains underdiagnosed due to its subtle manifestations and the varied ways it presents in individuals. The system described aims to enhance the detection rate of depressive disorders in females, specifically through the lens of facial expressions, which are known to hold a wealth of emotion-related information.
[00017] The system's initiation step involves capturing facial images using a dedicated image capture module. This module is adept at obtaining high-definition images, ensuring the intricate nuances of facial expressions are well-represented. Following image capture, the system employs a preprocessing unit. Beyond just basic image enhancements, this unit, leverages face detection algorithms to accurately isolate facial regions, removing any extraneous image data. This ensures the ensuing analysis is purely centered on the expressive components of the face.
[00018] The heart of the system lies in its deep learning model, responsible for decoding the emotional context of captured facial expressions. Elaborating on the intricacies of this model, revealing it to be a convolutional neural network (CNN) tailored for facial expression analysis. CNNs, celebrated for their prowess in image data interpretation, delve deep into the minute facial patterns, extracting features that may correlate with depressive disorders.
[00019] The results from this analytical phase are then funneled to a diagnosis output module. Here, users are provided with a clear, intuitive representation of the diagnosis, aiding both patients and healthcare professionals in understanding the results.
[00020] Recognizing the importance of continuous learning and the dynamic nature of mental health presentations, the system is backed by a robust database. This database harbours labelled facial expression data, serving dual purposes: training the deep learning model and calibrating it for improved accuracy. Furthermore, this database isn't static; it is periodically updated with new data. Correspondingly, the deep learning model undergoes periodic retraining, ensuring the system's diagnostic accuracy is consistently honed.
[00021] A significant feature is the system's feedback module. This interactive platform facilitates healthcare professionals in validating the diagnosis. They can correct or augment results based on their clinical judgment, ensuring the fusion of machine precision with human expertise.
[00022] In conclusion, this innovative system promises a paradigm shift in the diagnosis of depressive disorders in females. By synergizing advanced imaging, deep learning, and clinical feedback, it provides a holistic, accurate, and dynamic platform for detecting subtle signs of depression through facial expressions, bridging the gap between technology and mental health care.
[00023] Depressive disorders, with their myriad and often nuanced presentations, require innovative and precise diagnostic methods to ensure timely intervention and care. The outlined method aims to harness the intricate language of facial expressions, specifically in females, to diagnose depressive disorders more accurately.
[00024] Initiating the process, facial images of the female subject are captured. This foundational step is paramount to ensure that the subsequent analysis is built on high-quality, representative data. Following this, the captured images undergo a rigorous preprocessing phase. As emphasized, this isn't just a superficial image enhancement. The method employs face detection algorithms to pinpoint and isolate facial regions meticulously, ensuring extraneous elements are eliminated. Subsequent to this isolation, normalization is executed on these facial images, sharpening and standardizing them for the next phase.
[00025] The deep learning model then takes center stage. The backbone of this processing step is a convolutional neural network (CNN). Renowned for its prowess in image analytics, the CNN delves into the spatial patterns of the facial images, identifying and interpreting patterns that may signify the presence of depressive symptoms. Given that facial expressions hold a labyrinth of emotional cues, the CNN's depth and complexity ensure these cues are not missed but rather intricately analyzed.
[00026] Once the CNN completes its analysis, the method transitions to an evaluation phase. The outcomes of the model are scrutinized to gauge the likelihood of a depressive disorder in the subject. This determination is not merely a binary outcome but is presented in a nuanced manner that underscores the likelihood and severity.
[00027] A salient feature of this method is its adaptive nature. Recognizing that medical diagnosis is an evolving field and that feedback is pivotal, the method integrates a feedback loop. Healthcare professionals can provide insights regarding the accuracy of diagnoses, enriching the dataset with their clinical observations. This feedback is then used to recalibrate the deep learning model, ensuring its continually refined for higher precision. Additionally, the deep learning model periodically gets updated with new labeled data, fortifying its training and enhancing its diagnostic capabilities.
[00028] In essence, this method epitomizes the seamless amalgamation of technology and healthcare. By leveraging advanced neural networks, refining processes through expert feedback, and grounding it all in the profound language of facial expressions, the method promises a revolutionary stride in the accurate diagnosis of depressive disorders in females.
Brief Description of the Drawings
[00029] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00030] FIG. 1 represents an architectural overview of a system for diagnosing depressive disorder in females based on facial expressions, according to some embodiments of the present disclosure.
[00031] FIG. 2 shows an exemplary detailed schematic flow diagram of a method for diagnosing depressive disorder in females based on facial expressions, according to some embodiments of the present disclosure.
Detailed Description
[00032] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a

limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00033] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00034] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00035] The present invention pertains to the interdisciplinary domain of medical diagnostics and artificial intelligence. Specifically, the invention is rooted in the application of deep learning techniques to psychiatric evaluations, focusing on the non-invasive diagnosis of depressive disorders in females based on facial expressions. By harnessing the intricate patterns and nuances inherent in facial gestures and movements, the system leverages advanced machine learning architectures to offer a timely and efficient means of detecting depressive tendencies, thereby facilitating early intervention and personalized therapeutic approaches.
[00036] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00037] With advancements in the digital realm, AI-based solutions have emerged for predicting, monitoring, and treating mental health disorders. The relevance of such digital methods was especially highlighted during the COVID-19 pandemic, when direct interactions between patients and doctors were challenging. AI not only assists in diagnosing mental health issues but also offers personalized interventions, including feedback via virtual reality (VR). However, VR requires specialized equipment, which is often inaccessible to many.
[00038] In contrast, smartphones are widespread and allow for ecological momentary assessments (EMA). EMA is an innovative technique that facilitates behavioural interventions via mobile phones, engaging patients in their daily lives. The World Health Organization states that 5% of adults globally suffer from depressive disorders, which can sometimes lead to severe consequences like suicide. Mental health disorders can vary in intensity and require timely intervention. Despite the prevalence of these disorders in both urban and rural areas, rural India, in particular, is riddled with myths surrounding mental health.
[00039] This invention aims to benefit both urban and rural India by emphasizing accessible and effective solutions, especially for rural communities. The initiative seeks to establish a platform that combines questionnaires with facial expression analysis to assist those in need. In rural India, especially among women, the situation is dire due to enduring myths. Women often refrain from seeking professional help because of societal fears and instead resort to local, mostly religious remedies. Alarmingly, limited research exists on women's mental health in the Indian context, underlining the need for comprehensive solutions.
[00040] Depressive disorders are a prevalent mental health concern affecting millions of individuals worldwide. Traditional methods of diagnosis primarily rely on subjective patient reports and clinical interviews, which can be prone to biases and inaccuracies. This disclosure introduces a novel approach for diagnosing depressive disorder in females based on facial expressions using a sophisticated system 100 comprising an image capture module 102, preprocessing unit 104, deep learning model 106, diagnosis output module 108, and a labelled facial expression database 110. The proposed system 100 leverages advanced technology to objectively identify facial expression patterns correlated with depressive disorders, potentially revolutionizing the field of mental health diagnostics.
[00041] Depressive disorders pose a substantial burden on society, leading to significant personal suffering and economic costs. Timely and accurate diagnosis is crucial for effective treatment and intervention. According to a pictorial portrayal in FIG. 1, illustrating an architectural setup of the system 100 aims to enhance the diagnostic process by utilizing facial expression analysis, a potential biomarker for depressive disorders. This system 100 capitalizes on recent advancements in deep learning and computer vision to automatically detect subtle changes in facial expressions that might indicate the presence of depressive symptoms.
[00042] In an embodiment, the system 100 starts by obtaining facial images of the subject using a variety of imaging devices, such as smartphones, webcams, or dedicated cameras. These images serve as the input for subsequent analysis. High-quality image capture is essential to ensure accurate facial expression analysis.
[00043] Once facial images are captured, they undergo preprocessing to enhance and normalize the images. The preprocessing unit employs techniques such as noise reduction, contrast adjustment, and illumination normalization to improve the quality of the input images. Additionally, face detection algorithms are applied to isolate facial regions, eliminating extraneous information and improving the accuracy of subsequent analysis.
[00044] At the core of the system lies a deep learning model, specifically a convolutional neural network (CNN) tailored for facial expression analysis. CNNs are well-suited for image-based tasks due to their ability to automatically learn hierarchical features from raw pixel data. The deep learning model is trained on a vast amount of labeled facial expression data collected from individuals with and without depressive disorders.
[00045] The training process involves exposing the model to a diverse range of facial expressions, including neutral, happy, sad, and others. By learning the subtle differences in expression patterns, the model becomes capable of identifying features that may indicate the presence of depressive symptoms. The dataset used for training comprises a broad spectrum of ages, ethnicities, and other relevant factors to ensure the model's robustness and generalizability.
[00046] After the deep learning model analyzes the input facial image, it generates a diagnosis output. This output is a prediction of the likelihood that the individual exhibits facial expression patterns correlated with depressive disorders. The output may include a confidence score or a binary classification result indicating the presence or absence of depressive symptoms.
[00047] The system's accuracy and reliability hinge on the quality and diversity of the labeled facial expression data used for training and calibration. The database stores this data, which includes facial images annotated with corresponding labels indicating emotional states. To maintain the system's effectiveness over time, the database is continuously updated with new data. Periodic retraining of the deep learning model using the updated database ensures that the model remains up-to-date and adaptive to changes in facial expression patterns.
[00048] Summarizing the working of the system 100, facial images of the subject are captured using appropriate devices. Captured images undergo preprocessing to enhance quality and isolate facial regions. The pre-processed images are fed into the deep learning model for facial expression analysis. The model generates a diagnosis output, indicating the presence or absence of depressive symptoms. A feedback module enables healthcare professionals to review, validate, correct, or augment diagnosis results if necessary. The labeled facial expression database is continuously updated with new data, and the deep learning model is periodically retrained to improve diagnosis accuracy.
[00049] The proposed system offers several advantages. The system reduces reliance on subjective patient reports, enhancing diagnostic objectivity. Early detection of depressive symptoms allows for timely intervention, improving patient outcomes. The system can be used for remote monitoring, providing continuous support to individuals at risk.
[00050] Facial expression analysis may reduce stigma associated with discussing mental health issues. The feedback module fosters collaboration between the system and healthcare professionals, ensuring accurate diagnoses. For example, a young woman signs up for the system, concerned about her recent mood changes. She captures facial images daily using her smartphone. The system's deep learning model consistently detects subtle changes in her facial expressions, indicating the possibility of developing depressive symptoms. The system alerts her to consider seeking professional help, leading to an early intervention that prevents the progression of her condition.
[00051] In yet another epitome of illustration, a mental health professional uses the system in her practice. She inputs facial images of her patients and receives diagnosis outputs from the system. In cases where the system identifies potential depressive symptoms, the healthcare professional reviews the results. She can either validate the diagnosis or provide corrections based on her clinical expertise, refining the system's accuracy over time.
[00052] Referring to one or more preceding embodiments, the proposed system 100 for diagnosing depressive disorder in females based on facial expressions presents a cutting-edge solution to an important healthcare challenge. By leveraging advancements in deep learning, computer vision, and facial expression analysis, the system has the potential to revolutionize the field of mental health diagnostics. It offers an objective and technology-driven approach to identify individuals at risk of depressive disorders, enabling timely intervention and improved patient outcomes. Continuous database updates and periodic model retraining ensure the system's adaptability and accuracy over time, making it a valuable tool for both individuals and healthcare professionals.
[00053] The present invention pertains to a method 200 for diagnosing depressive disorder in females using facial expressions. The method 200 comprises a series of steps designed to objectively and accurately determine the likelihood of depressive disorder based on facial expression patterns.
[00054] Figuratively depicted in FIG. 2, representing a flow diagram of the method 200 for diagnosing depressive disorder in females using facial expressions, comprising the steps of (at step 202) capturing facial images of a female subject, (at step 204) preprocessing and normalizing the captured images to enhance facial features, (at step 206) processing the pre-processed images using a deep learning model trained on facial expression data, (at step 208) determining the likelihood of depressive disorder based on model outcomes, and (at step 210) presenting the diagnosis results.
[00055] In this step, facial images of a female subject are captured using suitable imaging devices such as cameras, smartphones, or webcams. The images serve as input for subsequent analysis. For example, a female individual captures a series of facial images using her smartphone's front-facing camera.
[00056] The captured facial images are pre-processed and normalized to enhance facial features and improve analysis accuracy. Techniques such as noise reduction, contrast adjustment, and illumination normalization are applied to the images. Face detection algorithms are further utilized to isolate facial regions from the images, eliminating background noise. Subsequently, the isolated facial images are normalized to a standardized scale and format. As an example, a noise-reduction algorithm removes artifacts from the captured images, and a face detection algorithm identifies and isolates the facial regions.
[00057] In an embodiment, the preprocessed and normalized facial images are fed into a deep learning model that has been trained on a comprehensive dataset of labeled facial expression data. The deep learning model analyzes the images to identify subtle facial expression patterns associated with depressive disorders. The model's ability to learn these patterns is a result of its exposure to a diverse range of expressions and emotions during the training phase. A convolutional neural network (CNN) is an example of a deep learning model used for this purpose.
[00058] Based on the analysis performed by the deep learning model, the method determines the likelihood of depressive disorder in the female subject. The model's outcomes may include a confidence score or a binary classification result indicating the presence or absence of depressive symptoms. For instance, the deep learning model computes a confidence score that represents the likelihood of the female subject having depressive symptoms based on the input facial image.
[00059] In an embodiment, the results of the diagnosis are presented to the user or relevant healthcare professionals. This can be in the form of a visual output, textual information, or an audio notification. The presented results allow individuals and professionals to make informed decisions regarding further actions, such as seeking professional help or intervention. An example output might be a message stating, "The analysis indicates a moderate likelihood of depressive symptoms."
[00060] The method 200 described in the preceding embodiment can be further enhanced by incorporating a step to update and retrain the deep learning model. The deep learning model is updated periodically with new labeled facial expression data. As the labeled dataset continues to grow, the model's capability to accurately diagnose depressive disorders improves. The updated dataset may include newly collected facial images and feedback data provided by healthcare professionals. Subsequently, the deep learning model is retrained using this updated dataset to refine its diagnosis capabilities and adapt to changing expression patterns.
[00061] In an embodiment, the method 200 can also involve steps to integrate feedback from healthcare professionals and refine the deep learning model accordingly. Healthcare professionals have the option to provide feedback on the accuracy of the model's diagnoses. This feedback, based on their clinical expertise, can validate or correct the diagnosis outcomes. The feedback data is then incorporated into the labeled dataset, enriching it with real-world insights. The deep learning model is recalibrated and refined using this augmented dataset, enhancing its accuracy and reliability over time. This iterative process ensures that the model evolves to accurately diagnose depressive disorders. For instance, a healthcare professional reviews a diagnosis and provides feedback that the model misinterpreted a specific expression as indicative of depressive symptoms, leading to model adjustments.
[00062] In an embodiment, the preprocessing step can be elaborated further. The preprocessing step includes a two-fold process. First, face detection algorithms are applied to accurately locate and isolate facial regions within the captured images. This eliminates irrelevant background information and focuses exclusively on the facial features. Second, the isolated facial images are normalized to ensure consistent scale, orientation, and lighting conditions. The normalization process enhances the comparability of different facial expressions, enabling the deep learning model to accurately learn expression patterns.
[00063] Thus, the embodiments outlined herein describe a comprehensive method 200 for diagnosing depressive disorder in females based on facial expressions. By leveraging image capture, preprocessing, deep learning analysis, and diagnosis result presentation, this method offers an objective and technology-driven approach to mental health diagnostics. The incorporation of model update and retraining, feedback integration, and detailed preprocessing further enhances the accuracy and adaptability of the system. This innovative method has the potential to revolutionize mental health assessment, providing timely intervention and improved patient outcomes.
[00064] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00065] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
[00066] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00067] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00068] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00069] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims
I/We Claim:
1. A system for diagnosing depressive disorder in females based on facial expressions, comprising: an image capture module configured to obtain facial images of the subject; a preprocessing unit designed to enhance and normalize said facial images; a deep learning model trained to identify facial expression patterns correlated with depressive disorders; a diagnosis output module to present the results; and a database storing labelled facial expression data for training and calibration.
2. The system of claim 1, wherein the deep learning model comprises a convolutional neural network tailored for facial expression analysis.
3. The system of claim 1, wherein the preprocessing unit further employs face detection algorithms to isolate facial regions from obtained images.
4. The system of claim 1, further comprising a feedback module that allows healthcare professionals to validate, correct, or augment diagnosis results.
5. The system of claim 1, wherein the database is continuously updated, and the deep learning model is periodically retrained to improve diagnosis accuracy.
6. A method for diagnosing depressive disorder in females using facial expressions, comprising the steps of: capturing facial images of a female subject; preprocessing and normalizing the captured images to enhance facial features; processing the pre-processed images using a deep learning model trained on facial expression data; determining the likelihood of depressive disorder based on model outcomes; and presenting the diagnosis results.
7. The method of claim 6, further comprising the step of updating the deep learning model with new labelled data and retraining to refine diagnosis capability.
8. The method of claim 6, wherein the step of processing involves a convolutional neural network analyzing spatial patterns in the facial images.
9. The method of claim 6, further including the steps of: receiving feedback from healthcare professionals on diagnosis accuracy; augmenting the dataset with this feedback; and recalibrating the deep learning model accordingly.
10. The method of claim 6, wherein the preprocessing step includes isolating facial regions from the captured images using face detection algorithms and then normalizing the isolated facial images.

Deep Learning Based Diagnosis of Depressive Disorder in Females using Facial Expressions
Abstract
The invention presents a novel system tailored for diagnosing depressive disorders in females utilizing facial expressions as primary indicators. Central to the system is an image capture module proficient in obtaining high-definition facial images of subjects. Ensuing image acquisition, a preprocessing unit refines and standardizes these images, priming them for analysis. A sophisticated deep learning model, informed by a robust database of labeled facial expressions, deciphers subtle facial patterns and correlates them with depressive symptoms. The culmination of this process results in the diagnosis output module presenting a comprehensive analysis. The system's inherent adaptability and precision are further enhanced by its dynamic database, which facilitates continuous training and calibration, ensuring state-of-the-art diagnostic accuracy. , Claims:Claims
I/We Claim:
1. A system for diagnosing depressive disorder in females based on facial expressions, comprising: an image capture module configured to obtain facial images of the subject; a preprocessing unit designed to enhance and normalize said facial images; a deep learning model trained to identify facial expression patterns correlated with depressive disorders; a diagnosis output module to present the results; and a database storing labelled facial expression data for training and calibration.
2. The system of claim 1, wherein the deep learning model comprises a convolutional neural network tailored for facial expression analysis.
3. The system of claim 1, wherein the preprocessing unit further employs face detection algorithms to isolate facial regions from obtained images.
4. The system of claim 1, further comprising a feedback module that allows healthcare professionals to validate, correct, or augment diagnosis results.
5. The system of claim 1, wherein the database is continuously updated, and the deep learning model is periodically retrained to improve diagnosis accuracy.
6. A method for diagnosing depressive disorder in females using facial expressions, comprising the steps of: capturing facial images of a female subject; preprocessing and normalizing the captured images to enhance facial features; processing the pre-processed images using a deep learning model trained on facial expression data; determining the likelihood of depressive disorder based on model outcomes; and presenting the diagnosis results.
7. The method of claim 6, further comprising the step of updating the deep learning model with new labelled data and retraining to refine diagnosis capability.
8. The method of claim 6, wherein the step of processing involves a convolutional neural network analyzing spatial patterns in the facial images.
9. The method of claim 6, further including the steps of: receiving feedback from healthcare professionals on diagnosis accuracy; augmenting the dataset with this feedback; and recalibrating the deep learning model accordingly.
10. The method of claim 6, wherein the preprocessing step includes isolating facial regions from the captured images using face detection algorithms and then normalizing the isolated facial images.