DESC:FIELD OF INVENTION:
The present invention in general, relates to a system for acquisition of medical visual media used in the medical imaging industry, and the process for standardization of the same. Particularly, the present invention relates to a standardized system for acquisition of medical visual media and the use of such standardized medical visual media to aid screening, diagnosis and follow up of (diseases, conditions).
BACKGROUND OF THE INVENTION:
Medical imaging has revolutionized healthcare by providing non-invasive methods to visualize internal structures of the human body, aiding in diagnosis, treatment planning, and monitoring. Technologies such as X-ray, ultrasound, MRI, CT scans, and PET scans have become indispensable tools in modern medicine. However, as healthcare systems evolve, so do the challenges associated with medical imaging. These challenges include high costs, logistical limitations, accessibility issues, and the need for precision in specific diagnostic scenarios.
Traditional imaging systems are often bulky, expensive, and require specialized infrastructure. For example, MRI and CT scanners involve substantial upfront costs and significant ongoing maintenance expenses along with expert supervision. Their usage is typically confined to hospitals and diagnostic centers, creating logistical hurdles for patients in remote or underserved areas. Furthermore, the reliance on trained personnel to operate these machines further compounds the barriers to widespread adoption.
To address these challenges, innovative solutions are being developed. For instance, researchers at the University of Queensland are trialing an AI-driven 3D skin imaging project aimed at improving early melanoma detection in rural Australia. This initiative allows residents in remote areas to receive skin checks without traveling long distances to healthcare facilities, significantly improving early detection and treatment outcomes (Soyer et al., 2024). AI has been used to differentiate patients with inflammatory arthritis from healthy controls, with excellent accuracy (Phatak et al, 2024). Such AI algorithms could detect when particular joints were inflamed, with reasonable accuracy (Phatak et al, 2023). Such examples underscore the potential of advanced AI-powered imaging solutions to bridge the accessibility gap and deliver critical healthcare services to underserved populations.
In recent years, advances in computer vision and artificial intelligence (AI) have led to transformative innovations in the medical imaging industry. AI-powered tools now assist in automating the analysis of complex medical images, reducing diagnostic time, and improving accuracy. The integration of deep learning models, such as convolutional neural networks (CNNs), has significantly enhanced the ability to detect anomalies, classify diseases, and predict treatment outcomes.
The miniaturization of imaging devices offers transformative benefits, such as portability and accessibility, particularly in remote or underserved regions. However, it introduces several critical limitations that must be addressed to ensure reliability and accuracy in medical diagnostics. Miniaturized systems often compromise on image quality, as reducing the size of components like lenses and sensors can lead to lower resolution and decreased clarity. For instance, smaller imaging devices may struggle to provide the high-detail visuals necessary for detecting subtle anomalies, which are crucial in fields like oncology or cardiology.
The lack of standardization across imaging devices further amplifies this issue, particularly in portable or low-cost systems. Variations in hardware capabilities, imaging protocols, and environmental conditions result in disparate data quality, making it challenging to train and validate AI models effectively. This reinforces the need for stringent data standardization protocols that ensure uniformity and reliability, irrespective of the imaging device or setting. Incorporating mechanisms to pre-process and enhance data such as noise reduction algorithms and AI-based feature extraction can help mitigate these challenges, enabling accurate and consistent diagnostics.
Despite these advancements, several limitations persist. Existing systems often struggle with consistency when applied to varied imaging conditions, such as lighting, patient movement, or image quality. This inconsistency can impede their reliability and adoption in critical diagnostic workflows. Another emerging trend is the miniaturization of imaging devices and their compatibility with smartphones and other portable platforms. Technologies enabling clear and consistent imaging under diverse environmental conditions are gaining prominence, particularly in fields like dermatology and point-of-care diagnostics. For example, handheld ultrasound devices and smartphone-based imaging tools offer promising solutions for primary care settings, telemedicine, and fieldwork. However, these advancements face challenges such as suboptimal image quality, limited computational power in portable systems, and difficulties in standardizing results across devices and environments.
In light of these challenges, there is a pressing need for a standardized, cost-effective, portable, and user-friendly imaging system that can be integrated with advanced AI models for the purpose of diagnosis. Such a system should address logistical challenges, ensure consistent imaging quality across diverse settings, and enhance the diagnostic capabilities of healthcare professionals. Additionally, there is a demand for systems capable of reducing noise, enhancing relevant features, and eliminating areas irrelevant to diagnosis, thereby improving image clarity and ensuring diagnostic precision.
To address the challenges in standardized medical imaging and diagnosis, the present invention aims to develop an innovative, cost-effective, portable, and AI-integrated imaging system that ensures accessibility across diverse environments, including remote and underserved areas. By leveraging advanced AI algorithms for noise reduction, feature enhancement, and diagnostic precision, the system will overcome inconsistencies associated with varied imaging conditions and patient characteristics that may affect accurate diagnosis. Furthermore, its standardized and user-friendly design and compatibility with portable platforms will empower healthcare providers to deliver quicker and more accurate diagnoses, ultimately bridging the gap between advanced medical imaging technologies and equitable healthcare delivery. The present invention aspires to redefine imaging standards, making high-quality diagnostics universally accessible.
It is therefore an object of this invention to overcome at least one of the shortcomings listed above.
OBJECT OF THE INVENTION:
It is the primary object of the present invention to develop a standardized, cost-effective and portable imaging system capable of delivering consistent and high-quality diagnostic imaging in diverse environmental and clinical conditions.
Another object of the present invention is to create an imaging system compatible with portable devices such as smartphones and tablets, thereby increasing accessibility in remote and underserved regions.
Yet another object of the present invention is to design a standardized medical imaging system capable of functioning in resource-limited settings by utilizing lightweight and compact hardware components that require minimal specialized infrastructure.
Yet another object of the present invention is to enable seamless telemedicine capabilities by allowing the transmission of diagnostic-quality images to healthcare professionals for remote consultation and analysis.
Yet another object of the present invention is to address regulatory and standardization challenges by incorporating features that ensure compliance with diverse healthcare imaging standards and data collection protocols.
Yet another object of the present invention is to ensure acquisition of consistent and unbiased medical media to aid accurate diagnosis for patients of varying skin tones, addressing the challenge of skin color variations in imaging systems to provide consistent and accurate outcomes for all demographics.
Yet another object of the present invention is to minimize the impact of background biases in acquired medical visual media by incorporating design elements and standardized imaging protocols that ensure the imaging system consistently focuses on the anatomical region of interest, regardless of environmental or background variations, thereby enhancing diagnostic reliability across diverse clinical and non-clinical settings.
Yet another object of the present invention is to maintain patient confidentiality and data security by anonymizing acquired standardized medical visual media, allowing for secure access and utilization in further processing and diagnosis. The present invention safeguards personally identifiable information through robust anonymization and de-identification measures, ensuring compliance with data privacy regulations while enabling the efficient and secure use of medical visual media across diverse clinical and non-clinical settings.
Yet another object of the present invention is to provide a user-friendly system that reduces the dependency on highly trained personnel for its operation, thereby facilitating its use in primary care settings and non-specialist environments.
Yet another object of the present invention is to develop an imaging system capable of adapting to a wide range of diagnostic scenarios, including but not limited to general radiology, dermatology, and point-of-care diagnostics, thereby broadening its applicability.
These and other objects, features, and advantages of the present invention will become apparent to those skilled in the field of medical imaging and diagnostics in light of the detailed description of the various illustrated embodiments and implementations. The objects enlisted are included in a non-exhaustive and non-limiting manner.
SUMMARY OF THE INVENTION:
Before the present system and process is described, it is to be understood that this application is not limited to the particular disclosure and details described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only and is not intended to limit the scope of the present application. This summary is provided to introduce concepts related to the formulation and processes of making standardized medical imaging and diagnostic systems, which are further described below in the detailed description. This summary is not intended to identify essential features of the subject matter nor is it intended for use in limiting the scope of the subject matter.
An aspect of the present invention relates to a standardized medical imaging system comprising one or more imaging components, which may be integrated with portable devices such as smartphones or tablets, capable of providing standardized, high-quality diagnostic imaging across diverse environmental conditions.
Yet another aspect of the present invention relates to a system designed to acquire standardized medical media to ensure equal and unbiased diagnosis, with specific adaptations that enable consistent diagnostic results for patients of varying skin tones, addressing challenges related to skin color in imaging systems.
Yet another aspect of the present invention relates to anonymization of the acquired standardized medical visual media.
Yet another aspect of the present invention relates to secured storage of the acquired standardized anonymized medical media and its classification and organization based on predefined parameters.
Yet another aspect of the present invention relates to ensuring restricted access to the acquired standardized anonymized stored classified medical media.
Yet another aspect of the present invention relates to the reduction of diagnostic time through streamlined workflows, from acquisition of standardized medical media to automated analysis of such acquired standardized medical media, allowing healthcare professionals to make quick and accurate decisions.
Yet another aspect of the present invention relates to a medical imaging system that is cost-effective and compatible with low-resource settings, thereby increasing the accessibility of advanced diagnostic tools in underserved and remote areas.
BRIEF DESCRIPTION OF DRAWINGS:
The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
The detailed description is given with reference to the accompanying figures.
Figure 1 – illustrates the exemplary operational workflow of the standardized acquisition component of the system used for standardized acquisition of medical visual media, in accordance with an embodiment of the present disclosure.
Figure 2A – illustrates the front view of the assembled standardized acquisition component of the system, showing the front panel in an open position for the standardized acquisition of medical visual media, in accordance with an embodiment of the present disclosure.
Figure 2B – illustrates the side-rear view of the assembled standardized acquisition component of the system used for the standardized acquisition of medical visual media, in accordance with an embodiment of the present disclosure.
Figure 2C – illustrates the side-rear view of the assembled standardized acquisition component of the system, showing the back panel in an open position for standardized acquisition of medical visual media, in accordance with an embodiment of the present disclosure.
Figure 3 – illustrates the method of acquisition, anonymization, storage and classification of standardized medical visual media by the system, in accordance with an embodiment of the present disclosure.
Figure 4A – illustrates schema of workflow to deploy image processing and convolutional neural networks (CNNs) on smartphone photographs for inflammatory arthritis detection; top: using a screening CNN on uncropped photos; bottom: using joint specific CNNs on cropped photos.
Figure 4B – illustrates the performance of joint-specific Convolutional Neural Networks (CNN) for three joints – middle finger proximal interphalangeal joint (MFPIP), index finger proximal interphalangeal joint (IFPIP), and wrist, showing representative two-by-two tables with calculated test metrics, bootstrap distributions of accuracy, and Receiver Operating Characteristic (ROC) curves, in accordance with an embodiment of the present disclosure. Accuracy denotes the overall probability of correct patient classification, with PPV (positive predictive value) and NPV (negative predictive value) also presented.
DETAILED DESCRIPTION OF THE PRESENT INVENTION:
Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in practice, the exemplary, systems and methods are now described. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms. Industry specific terms such as ‘medical visual media’, ‘standardized acquisition’, ‘unit’, ‘storage’, ‘organization’, ‘anonymization’, ‘image capturing device’, ‘modular bracket’ etc. shall be read in a non-limiting and an all-encompassing manner in the document.
Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skilled in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
Various embodiments of the present invention relate to a system for standardized acquisition, storage, and systematic organization of medical visual media. The system as a whole ensures a uniform and replicable method of capturing high-quality medical visual media and provides robust mechanisms for storage and retrieval, enabling enhanced diagnostic accuracy and streamlined workflows.
An embodiment of the present invention discloses a system (100) for standardized acquisition, anonymization, classification and structural management of medical visual media comprising of at least one standardized acquisition component (102) and at least one classification component (104).
In an embodiment of the present invention discloses a system (100) for standardized acquisition and classification of medical visual media, wherein the standardized acquisition component (102) is a unit assembled using an upper panel (106), at least one side panel (108), a front panel (110), a back panel (112), at least one light source (114) and a power storage unit (116). The unit is designed as a controlled imaging environment ensuring consistency in medical visual media acquisition. Each structural component is precisely positioned to create a stable, enclosed space that facilitates uniform acquisition of images, allowing for accurate and repeatable medical assessments. Overall construction of the standardized acquisition component (102) is optimized to accommodate various body parts such as hands, feet, knees, etc. while maintaining an ergonomic design for ease of operation.
Yet another embodiment of the present invention discloses a system (100), wherein the surface of the upper panel (106), at least one side panel (108), the front panel (110), and the back panel (112) of the standardized acquisition component (102) are non-reflective in nature. The use of non-reflective surfaces prevents glare, reflections, and light distortions, ensuring clear and accurate medical images. By absorbing light, the system (100) enhances contrast and detail, mitigating the impact of varying camera angles and lighting conditions. This design ensures consistency across imaging sessions, eliminating ambient light variations that could affect diagnostic reliability.
Yet another embodiment of the present invention discloses a system (100), wherein the upper panel (106) and the at least one side panel (108) of the standardized acquisition component (102) comprise of at least one modular bracket (118) configured to expose the inner section of the unit for acquisition of standardized medical media using an image capturing device (120). The modular bracket (118) allows controlled exposure of specific sections of the standardized acquisition component (102) without compromising its enclosed nature, allowing controlled exposure and enhancing the reproducibility.
Another embodiment of the present invention discloses a system (100), wherein the light source (114) of the standardized acquisition component (102) is configured to provide uniform illumination within the unit, preventing shadows, glare and overexposure to ensure consistent standardized image quality. This is crucial for phone cameras lacking automatic optical correction mechanisms to compensate for inconsistent lighting. The illumination provided by the light source (114) in conjunction with the non-reflective surfaces maintains an optimal imaging environment, thereby reducing the need for post-processing corrections which allows the acquired standardized medical media to be clear and detailed, and rendering them immediately usable for classification and analysis.
Another embodiment of the present invention discloses a system (100), wherein the standardized acquisition component (102) is powered by a power storage unit (116).
Yet another embodiment of the present invention discloses a system (100), wherein standardized medical visual media using the standardized acquisition component (102) is acquired using the following steps:
- opening the front panel (110) of the assembled unit to place the body part to be captured within the of the assembled unit
- opening the back panel (112) of the assembled unit for accommodating longer body parts, ensuring the body part to be captured is positioned within the of the assembled unit
- securing the body part within the assembled unit by closing the front panel (110) and/or back panel (112)
- adjusting the modular bracket (118) of upper panel (106) or at least one side panel (108) to expose the inner section of the assembled unit to the image capturing device (120)
- activating the light source (114) to provide uniform illumination within the assembled unit
- capturing the image of the body part within the assembled unit using the image capturing device (120)
With these conditions precisely regulated, the standardized acquisition component (102) reliably acquires uniform and diagnostically viable images, eliminating the variability introduced by user-dependent camera angles, inconsistent lighting, fluctuating power conditions, and external environmental factors, thereby facilitating a standardized and diagnostically accurate acquisition process.
Another embodiment of the present invention discloses a system (100), wherein the classification component (104) is connected with the standardized acquisition component (102) either deployed on the same computer or via computer network-based interface accessible via internet or via private network. This integration allows for real-time or near-real-time or subsequent-to-ingress synchronization of acquired standardized medical visual media, ensuring that they are immediately and perpetually available for further anonymization, processing and diagnosis. Anonymization is performed through an automated de-identification process, wherein each acquired medical visual media is assigned a unique identifier, with all personally identifiable information or patient identifiers being separated and stored in a secure, access-controlled environment. If deployed within a computing system network, the computing system network-based connectivity allows scalability, security, and accessibility, ensuring that acquired standardized medical visual media can be retrieved and utilized from any location in a timely manner after being deployed on a computing system accessible via internet or private networks.
Yet another embodiment of the present invention discloses a system (100), wherein the classification component (104) embeds a data organization module (122) and an access management module (124).
Yet another embodiment of the present invention discloses a system (100), wherein the classification component (104) embeds a data organization module (122) configured to structurally anonymize, store, preprocess and classify the acquired standardized medical visual media based on predefined parameters, ensuring that acquired standardized medical visual media are systematically categorized for efficient retrieval and analysis. Classification criteria may include anatomical region, imaging conditions, patient-specific identifiers, etc., enabling structured storage and seamless searchability.
Another embodiment of the present invention discloses a system (100), wherein the classification component (104) embeds an access management module (124) configured to ensure restricted access to acquired standardized anonymized stored classified medical visual media based on predefined authentication mechanisms, ensuring only authorized personnel can access, retrieve, modify, or share the acquired standardized medical visual media.
Yet another embodiment of the present invention discloses a system (100), wherein the acquired standardized anonymized stored classified medical visual media having restricted access is stored in a vault (126) for future access and diagnosis
The classification component (104) of the system (100) disclosed in the present invention is designed to interface with proprietary diagnostic software and methodologies, allowing for automated analysis and diagnosis of the acquired standardized anonymized stored classified medical visual media having restricted access.
By ensuring that the acquired standardized anonymized stored classified medical visual media conform to predefined quality and format parameters, the system (100) enhances the accuracy and reliability of subsequent diagnostic assessments. The standardized acquisition and structured classification, interfaced with proprietary AI-assisted diagnostic software and methodologies, of medical visual media contribute to a comprehensive diagnostic workflow, aligning with proprietary advancements in medical imaging analysis and classification technologies. This integration of standardized image acquisition with advanced diagnostic tools represents a transformative approach to improving the accuracy and efficiency of disease detection. To validate the efficacy of this system, a pilot study was conducted to evaluate the performance of Convolutional Neural Networks (CNNs) in detecting synovitis and diagnosing inflammatory arthritis using standardized hand images acquired through the system (100) for standardized acquisition and classification of medical visual media, disclosed in the present invention. The findings from this pilot study provide critical insights into the practical application of the invention, underscoring its potential to revolutionize diagnostic workflows in rheumatology and beyond.
Pilot study conducted using the system (100) disclosed in the present invention:
This pilot study aimed to evaluate the effectiveness of Convolutional Neural Networks (CNNs) in detecting synovitis and diagnosing inflammatory arthritis using standardized hand images (medical media) acquired using the system (100) disclosed in the present invention. The study focused on three key joints: the wrist, the 2nd/index finger proximal interphalangeal (IFPIP), and the 3rd/middle finger proximal interphalangeal (MFPIP), selected based on their prevalence in the dataset. The use of widely available smartphones for image capture highlights the potential for improving point-of-care screening, enabling the triage of patients with painful hand syndromes to rheumatologists and facilitating follow-up for those diagnosed with inflammatory arthritis.
Patients and Methods:
The study enrolled 200 patients with inflammatory arthritis from the rheumatology department of King Edward Memorial (KEM) Hospital and a private rheumatology practice in Pune, India. Patients included those with rheumatoid arthritis (RA), connective tissue diseases, chronic viral arthritis, spondyloarthritis, psoriatic arthritis, and other inflammatory conditions, all with symptoms lasting less than two years to avoid confounding factors like joint deformities. Clinical details, including age, sex, disease duration, and markers of inflammation (ESR, CRP), were recorded. Two trained rheumatologists conducted clinical examinations, and synovitis was recorded as a binary (yes/no) outcome. Disagreements between two rheumatologists were resolved through consensus.
Standardized hand images were captured using an iPhone 11 as the image capturing device within the system for standardized acquisition and classification of medical visual media, as disclosed in the present invention. As patients with deformities were excluded, all patients were able to lay their hands flat inside the photo box. Joints of interest were cropped using MediaPipe, an open-source library, and images were anonymized using a unique identity generator. Two CNNs were developed: CNN 1 used uncropped whole-hand images of the dorsal surface of the hand, while CNN 2 focused on cropped images of the wrist, IFPIP, and MFPIP joints (areas of interest for this study). Both CNNs utilized the Inception-ResNet-v2 architecture, fine-tuned with data augmentation techniques and trained using the Adam optimizer in MATLAB.
Results:
The study included 200 patients (143 female, mean age 49.6 years) and 200 controls (129 female, mean age 37.8 years). The most commonly swollen joints were the wrist (43.2% of hands), followed by the MFPIP (33.5%) and IFPIP (32.0%). Both rheumatologists demonstrated good agreement in synovitis detection in 404 joints, with a Cohen’s kappa of 0.64.
CNN 1, which analyzed uncropped whole-hand images, achieved excellent performance with 98% accuracy, sensitivity, and specificity in distinguishing patients from controls. The positive predictive value (PPV) and negative predictive value (NPV) were 98.7% and 98.8%, respectively, given a 48% prevalence of disease in the dataset.
For joint-specific CNNs, the wrist model performed best with 80% accuracy, 88% sensitivity, and 72% specificity. The IFPIP and MFPIP models followed closely, with accuracies of 79% and 76%, sensitivities of 89% and 91%, and specificities of 73% and 70%, respectively.
Conclusion:
This study demonstrates the potential of CNNs to accurately detect synovitis and diagnose inflammatory arthritis using standardized hand images. The high performance of the whole-hand CNN suggests its utility as a screening tool, while the joint-specific CNNs provide targeted insights into individual joint involvement. These findings underscore the importance of standardization in medical image acquisition and highlight the feasibility of integrating smartphone-based imaging and AI-driven diagnostics into clinical practice. The results are highly relevant for the present patent application, as they provide robust evidence of the direct impact of standardized medical media on diagnostic accuracy. This study has been registered with the Clinical Trials Registry of India (CTRI/2020/08/027129). All permissions, waivers and agreements related to consent and ethics have been procured for this study. All patients signed an informed consent document with special permission for the storage of photographs in the photo repository for 30 years.

ADVANTAGES OF THE PRESENT INVENTION:
1. Cost-Effectiveness and Accessibility:
The invention leverages affordable, portable hardware (e.g., smartphones) and AI-driven automation, significantly reducing the cost of medical imaging systems. This makes advanced diagnostic tools accessible to rural, remote, and underserved areas where traditional imaging systems (e.g., MRI, CT scanners) are prohibitively expensive and logistically challenging to deploy.
2. Standardized Imaging Across Diverse Conditions:
The system ensures consistent, high-quality imaging by creating a controlled environment with uniform lighting, non-reflective surfaces, and modular brackets. This eliminates variability caused by environmental factors (e.g., lighting, patient movement) and ensures reliable diagnostic results, even in resource-limited settings.
3. AI-Powered Diagnostic Precision:
Advanced AI algorithms, such as Convolutional Neural Networks (CNNs), when deployed to the standardized medical media acquired using the system disclosed in the present invention for noise reduction, feature enhancement, and automated anomaly detection, improve diagnostic accuracy, reduces diagnostic time, and minimizes the need for specialized personnel, making it suitable for primary care and non-specialist settings.
4. Enhanced Early Detection and Screening:
When the standardized, high-quality images acquired using the system disclosed in the present invention are combined with AI-assisted diagnostic tools, it enables early detection of conditions like melanoma, inflammatory arthritis, and other diseases. For example, the aforementioned pilot study demonstrated 98% accuracy in detecting synovitis using smartphone-captured hand images, highlighting its potential for widespread screening and triage.
5. Scalability and Telemedicine Integration:
If deployed within a computer network, the computer network-based interface of the system disclosed in the present invention allows for real-time or near-real-time or subsequent-to-ingress synchronization, storage, and remote access to diagnostic-quality images. This facilitates telemedicine, enabling healthcare professionals to provide remote consultations and analyses, further bridging the gap between advanced diagnostics and underserved populations.
6. Security of Sensitive Personal Data:
The system disclosed in the present invention incorporates a data organization module and an access management module, ensuring the secure classification, storage, and retrieval of medical visual media. The access management module enforces restricted access based on predefined authentication mechanisms, safeguarding sensitive patient data from unauthorized access or breaches. This robust security framework not only protects patient privacy but also ensures compliance with healthcare data regulations, making the system reliable for handling confidential medical information in diverse settings.
,CLAIMS:We Claim:
[Claim 1] A system (100) for standardized acquisition, anonymization, classification and structural management of medical visual media, comprising:
one or more standardized acquisition component (102) configured to capture standardized medical visual media under controlled imaging conditions, and
one or more classification component (104) configured to anonymize, store, classify and manage acquired standardized medical visual media by performing automated structural anonymization, storage and classification to enable seamless retrieval and access control
[Claim 2] The system (100) as claimed in claim 1, wherein the standardized acquisition component (102) is a unit assembled using an upper panel (106), at least one side panel (108), a front panel (110), a back panel (112), at least one light source (114), and a power storage unit (116).
[Claim 3] The system (100) as claimed in claim 1 and 2, wherein the upper panel (106), at least one side panel (108), the front panel (110) and the back panel (112) are non-reflective surfaces.
[Claim 4] The system (100) as claimed in claims 1-3, wherein the upper panel (106) and the at least one side panel (108) comprise of at least one modular bracket (118) configured to expose the inner section of the unit for acquisition of standardized medical media using an image capturing device (120).
[Claim 5] The system (100) as claimed in claims 1 and 2, wherein the light source (114) is configured to provide uniform illumination within the unit.
[Claim 6] The system (100) as claimed in claims 1 and 2, wherein the unit is powered by a power storage unit (116).
[Claim 7] The system (100) as claimed in claims 1-6, wherein standardized medical visual media is acquired by the standardized acquisition component (102) by:
- opening the front panel (110) of the assembled unit to place the body part to be captured within the assembled unit
- opening the back panel (112) of the assembled unit for accommodating longer body parts, ensuring the body part to be captured is positioned within the assembled unit
- securing the body part within the assembled unit by closing the front panel (110) or back panel (112)
- adjusting the modular bracket (118) of upper panel (106) or at least one side panel (108) to expose the inner section of the assembled unit to the image capturing device (120)
- activating the light source (114) to provide uniform illumination within the assembled unit
- capturing the image of the body part within the assembled unit using the image capturing device (120)

[Claim 8] The system (100) as claimed in claim 1, wherein the classification component is connected with the standardized acquisition component either deployed on the same computing device or via computing device network-based interface accessible via internet or via private network.
[Claim 9] The system (100) as claimed in claims 1 and 8, wherein the classification component (104) embeds:
a data organization module (122) configured to perform feature-based indexing, metadata tagging, and structured classification of acquired medical visual media, and
an access management module (124) configured to enforce authentication-based retrieval and control access permissions dynamically based on user roles
[Claim 10] The system (100) as claimed in claims 1, 8 and 9, wherein the classification component (104) embeds a data organization module (122) configured to structurally anonymize, store, preprocess and classify acquired standardized medical visual media based on predefined parameters.
[Claim 11] The system (100) as claimed in claims 1, 8, 9 and 10, wherein the classification component (104) embeds an access management module (124) configured to ensure restricted access to acquired standardized anonymized stored classified medical visual media based on predefined authentication mechanisms.
[Claim 12] The system (100) as claimed in claims 1, 8, 9, 10 and 11, wherein the acquired standardized anonymized stored classified medical visual media having restricted access is stored in a vault (126) for future access and diagnosis.