Description:FIELD OF THE INVENTION
The present invention relates to healthcare technology, specifically in the field of pelvic health monitoring, assessment, diagnostics prognostics and predictive health as well as lifestyle intervention suggestions.
Particularly, the present invention encompasses the integration of artificial intelligence (AI), machine learning (ML), and advanced data analytics techniques to gather, analyze, and interpret diverse data sources related to pelvic health in an interactive manner with impact analysis.
BACKGROUND OF THE INVENTION
Pelvic health issues, encompassing conditions such as incontinence, sexual dysfunction, pelvic pain, and anatomical abnormalities, pose significant challenges to individuals’ well-being and healthcare systems worldwide. The complexity of diagnosing and managing pelvic health disorders stems from the multifaceted nature of these conditions, which often involve intricate interactions between physiological, anatomical, and psychosocial factors.
Traditionally, the assessment and treatment of pelvic health issues have relied on subjective clinical evaluations and limited diagnostic tools, leading to inconsistencies in diagnosis and suboptimal treatment outcomes. Healthcare providers face challenges in accurately stratifying patients based on their risk profiles, tailoring interventions to individual needs, and objectively tracking treatment efficacy over time.
Moreover, existing approaches to pelvic health management often lack integration across healthcare settings, hindering seamless coordination of care and exacerbating disparities in access to specialized services. Patients may encounter barriers in accessing timely and appropriate interventions, resulting in prolonged suffering, decreased quality of life, and increased healthcare costs associated with managing complications.
Furthermore, the absence of standardized metrics for assessing pelvic health status and treatment outcomes hampers efforts to compare interventions’ effectiveness across different populations and settings. This lack of standardized assessment tools limits the ability to conduct robust research and implement evidence-based practices in pelvic health management.
The statistics highlight a significant challenge in the current healthcare landscape, revealing that a substantial portion of patients dealing with chronic pain, neuro-muscular, endocrine, age related, genetic post-acute stroke, and pelvic floor disorders face persistent symptoms despite undergoing existing treatment modalities. Specifically, research indicates that over 65% of individuals grappling with these conditions fail to achieve full recovery through conventional treatments. This failure to attain optimal recovery not only impacts the quality of life for these patients but also contributes to increased healthcare utilization in the form of hospital readmissions and visits to the emergency room.
This inability to achieve full recovery underscores the complexity and multifaceted nature of these conditions. Despite advancements in medical interventions and therapies, a significant proportion of individuals continue to experience debilitating symptoms, which can severely impact their daily functioning and overall well-being. Similarly, individuals suffering from pelvic floor disorders face unique challenges that often require comprehensive management strategies to address symptoms effectively.
Pelvic floor dysfunction (PFD) encompasses various interrelated conditions affecting pelvic organ function, including urinary and fecal incontinence, pelvic organ prolapse, and sexual dysfunction, often coexisting due to shared risk factors. PFD affects over 50% of women and is attributed to multifactorial causes such as childbirth, obesity, and anatomical factors. Despite its low mortality, PFD poses significant medical, social, and economic burdens.
Diagnostic methods for PFD include clinical, urodynamic, imaging, and neurophysiological assessments, with neurophysiological studies like pudendal nerve terminal motor latency and pelvic floor electromyography playing a crucial role. Pelvic floor muscle training (PFMT), particularly biofeedback-assisted PFMT, is a frontline therapy for PFD, aiming to improve muscle function and symptom management. However, consensus on the efficacy of biofeedback-assisted PFMT remains elusive, with conflicting findings from randomized controlled studies.
Addressing the uncertainty surrounding the effectiveness of biofeedback-assisted PFMT is vital, given its higher cost compared to standard PFMT. Further research, , is necessary to standardize, quantify and objectification of the inputs, assessments, diagnosis, prognosis and outcomes analysis to identify factors influencing treatment outcomes and adherence.
The cycle of ineffective treatments and recurrent healthcare utilization underscores the urgent need for innovative approaches that can address the underlying causes of these conditions and improve patient outcomes.
Overall, the current state of the art in pelvic health assessment and management is characterized by fragmented care delivery, subjective diagnostic approaches, and limited tools for tracking treatment outcomes. Addressing these challenges requires innovative solutions that leverage advanced technologies and comprehensive data analytics to enhance the precision, efficiency, and effectiveness of pelvic health care delivery.
The present invention describes an artificial intelligence driven pelvic health monitoring and risk assessment system.
OBJECTS OF THE INVENTION
The primary object of the present invention is to develop a comprehensive healthcare system focused on pelvic health monitoring, assessment, and diagnostics.
Further object of the present invention is to integrate various data sources, including biosensors, wearables, clinical inputs, demographics, imaging modalities, multiomics and diagnostic measures, to gather extensive patient health data.
Further object of the present invention is to employ advanced machine learning models, such as deep neural networks, recurrent neural networks, support vector regression, and ensemble methods, to analyze and interpret the gathered data accurately.
Further object of the present invention is to design and implement a Pelvic Health Score computation module to derive a unified metric representing the overall pelvic health and risk profile of individual patients.
Further object of the present invention is to objectify the subjective assessment in order to standardize the analysis as well as management to assist in impact assessment via scoring system of all three i.e. input layer consist of patient data (physical biosensor, lab etc.), middle diagnostic layer consist of potential health conditions (multiple diagnosis like incontinence, pain etc.) and output layer consisting of various interventions to improve the said pelvic and health conditions (like teletherapy, robotics, ultrasonic therapy etc.)
Further object of the present invention is to integrate telemedicine platforms and assistive technologies, such as wearable sensors, rehabilitation robotics, virtual reality therapy, and brain-computer interfaces, to facilitate remote monitoring, real-time adjustments to treatment plans, and personalized rehabilitation assistance.
SUMMARY OF THE INVENTION
Embodiments of the present disclosure present technological improvements as a solution to one or more of the above-mentioned technical problems recognized by the inventor in existing techniques.
The present disclosure seeks to provide an artificial intelligence driven pelvic health risk assessment and health monitoring and risk assessment system.
According to an aspect of the invention, the present invention integrates an extensive range of data sources, including biosensors, wearables, clinical inputs, demographics, imaging modalities, multiomics and diagnostic measures. This amalgamation of data aims to gather a holistic view of each patient's pelvic health status. Advanced machine learning models, such as deep neural networks and support vector regression, are employed to analyze the collected data. These models provide accurate risk assessment and personalized care planning tailored to each patient's unique needs.
According to a preferred aspect, a significant feature of the invention is the Pelvic Health Score computation module. This module calculates a unified metric representing the overall pelvic health and risk profile of individual patients. By considering various factors such as patient demographics, physiological parameters, clinical examination findings, and patient-reported symptoms, the Pelvic Health Score offers a standardized and objective measure for assessing pelvic health status. The individual diagnostic scores of each condition with impact analysis shall help to design the individual score as well as the combined individual score’s impact on overall pelvic score.
According to further aspect, the invention objectify the subjective assessment in order to standardize the analysis as well as management to assist in impact assessment via scoring system of all three i.e. input layer consist of patient data (physical biosensor, lab etc.), middle diagnostic layer consist of potential health conditions (multiple diagnosis like incontinence, pain etc.) and output layer consisting of various interventions to improve the said pelvic and health conditions (like teletherapy, robotics, ultrasonic therapy etc.).
According to further aspect, the invention integrates telemedicine platforms and assistive technologies, including wearable sensors, rehabilitation robotics, virtual reality therapy, and brain-computer interfaces. This integration enables remote monitoring, real-time adjustments to treatment plans, and personalized rehabilitation assistance.
The objects and the advantages of the invention are achieved by the process elaborated in the present disclosure.

BRIEF DESCRIPTION OF DRAWINGS
The foregoing Summary, as well as the following detailed description of preferred embodiments of the invention, will be better understood when read in conjunction with the drawings as well as experimental results. The accompanying drawings constitute a part of this specification and illustrate one or more embodiments of the invention. Preferred embodiments of the invention are described in the following with reference to the drawings, which are for the purpose of illustrating the present preferred embodiments of the invention and not for the purpose of limiting the same. The objects and advantages of the present invention will become apparent when the disclosure is read in conjunction with the following figures, wherein
Figure 1presents a Data Matrix of the Artificial Intelligence Driven Pelvic Risk Assessment and Health Monitoring System. It depicts how various data inputs, including biosensors, wearable devices, clinical inputs, demographics, imaging modalities, and diagnostic measures, are collected and organized for further analysis;
Figure 2illustrates the Data Flow in accordance with the embodiment of the present invention. It shows the process from data collection through various sources, preprocessing, analysis using machine learning models, and generation of actionable insights and recommendations;
Figure 3represents a Decision Tree as per the embodiment of the present invention. It visualizes the decision-making process of the AI system in assessing pelvic health risks and recommending treatment plans based on input data and predictive models;
Figure 4 shows graphical representation display of the Pelvic Pain Score on the user interface. It shows how the pain score is derived from various contributing factors and displayed for both patient and clinician review;
Figure 5 provides a graphical representation of the Incontinence Score on the display. It illustrates how the incontinence score is calculated based on input data and the patient’s reported symptoms and displayed to aid in clinical decision-making;
Figure 6 shows the Overall Sexual Function Score on the display. It aggregates various factors affecting sexual function and presents an overall score to guide treatment strategies;
Figure 7 is a comprehensive graphical representation which shows the Overall Pelvic Health Scores along with contributing parameters on the display. It combines various health metrics into a unified score to provide a holistic view of the patient’s pelvic health;
Figure 8 represents the AI/ML Driven Therapy Impact Matrix in accordance with the present invention. It shows how different therapeutic interventions are evaluated and scored based on their impact, enabling the optimization of treatment plans for individual patients.
DETAILED DESCIPTION OF THE INVENTION
The following detailed description illustrates embodiments of the present disclosure and ways in which the disclosed embodiments can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
The present invention provides an artificial intelligence driven pelvic health monitoring and risk assessment system.
According to an embodiment of the present invention, the system primarily includes of various sources to gather the data regarding patient’s health condition, the sources include biosensors, wearables, clinical inputs, demographics, imaging modalities like ultrasound CT/MRI, and other diagnostic measures like omics. A comprehensive array of data sources is provided to optimize treatment customization and delivery, particularly in the context of telemedicine and assistive technologies such as wearable sensors, rehabilitation robotics, virtual reality therapy, and brain-computer interfaces (BCIs) besides physical and interventional therapies using gadgets like ultrasound, NMR electrical stimulation infra-red etc.
According to further embodiment of the present invention, additional data from biosensors and wearable devices is received. The wearable devices include heart rate monitors, accelerometers, PPG, pulse oxyelectromyography sensors, electro cardio, electro-neuro, motor tone, and power analysis.
Further the medical conditions of patients like demographics, clinical examination findings, basic labs test data etc. are collected to track physiological parameters and movement patterns. Clinical inputs encompass patient-reported symptoms, medical history, and diagnostic test results besides omics date either through available data or though closest imputing using technology like KNN.
According to an embodiment of the present invention, the collected demographic information provides context for individualized care planning, while imaging modalities like ultrasound, CT and MRI scans offer insights into anatomical orientation (normal, abnormal and the spatial relationship) structures and pathophysiological conditions.
Data gathered through diverse technologies such as smartphones, iPads, desktop/personal computers, and servers is deployed either on-premises or on the cloud.
According to an embodiment of the present invention, data including age, gender, biosensor data from wearables, labs (CBC, RFT, LFT, TFT, Lipid profile, HbA1c, medical history, demographics, smoking, alcohol, substance abuse, education status, socio-economic status, muscle and skeletal markers, inflammatory markers, sleep pattern, HRV, temperature, menstrual health omics data wherever applicable) is stored in structured formats including CSV/Excel/JSON/XML.
According to an embodiment of the present invention, the collected data is stored in both main storage systems and auxiliary storage devices to ensure accessibility and redundancy. Organizing data is crucial for efficient processing and analysis, enhancing the effectiveness of subsequent stages in the pipeline.
According to an embodiment of the present invention, the organized data is preprocessed and cleared or filtered to enhance its quality and reliability. Filtering and preprocessing of data stored in databases is imperative as it significantly improves the performance of subsequent analytics and prediction processes, ensuring more accurate insights.
According to further embodiment of the present invention, preprocessing techniques including normalization, standardization, and outlier detection are applied on the stored data to enhance the quality of the data.
According to an embodiment of the present invention, the data including clinical notes, information about past medical history, surgeries, radiation, cancer, mental health issues, are treated as an unstructured data and stored in textual, audio, or video formats.
The unstructured data typically consists of –
? Laboratory test results, which are typically unstructured.
? Ultrasound scans and medical images which are stored in unstructured image formats.
According to an embodiment of the present invention, preprocessing of textual unstructured data involves techniques such as text tokenization, stop-word removal, stemming or lemmatization, and sentiment analysis.
Image data preprocessing may include resizing, normalization, and feature extraction using techniques like convolutional neural networks (CNNs) for image classification or segmentation.
According to an embodiment of the present invention, by applying preprocessing techniques to unstructured data, such as clinical notes and laboratory reports, it becomes feasible to extract relevant information and convert it into a structured format. This enables integration with other structured data types for comprehensive analysis and insights generation in pelvic health management.
According to an embodiment of the present invention, data cleaning process mainly includes removing inconsistencies, handling missing values, and standardizing formats.
According to further embodiment, the relevant features are extracted from raw data to enhance predictive power. Further the data is normalized and scaled ensuring uniformity and comparability of data across different features. Further the data is dimensionally reduced to reduce the number of features while preserving relevant information to improve computational efficiency and prevent overfitting.
And further the data is processed to analyze the distribution and summary statistics of variables to gain insights into the dataset.
According to an embodiment of the present invention the analyzed data is graphically visualized utilizing graphs, charts, and plots to visualize relationships and patterns within the data.
Correlation analysis is preformed to examine the correlations between variables to identify potential dependencies and interactions. Further similar data points are grouped together to uncover underlying structures within the dataset.
According to further embodiment, the labeled data is used to train models for predicting risk scores based on input features. A relationship between input variables and risk scores is stabilized to make continuous predictions.
According to an embodiment of the present invention, multiple models are included and executed to improve prediction accuracy and robustness of the system. A deep learning architecture is utilized in the system to leverage neural networks with multiple layers to capture complex patterns and relationships in the data.
According to further embodiment of the present invention, the system addresses the challenges inherent in medical data by running preprocessing algorithms that handle both structured and unstructured data. These algorithms clean and standardize the data before forwarding it to subsequent modules for further processing and analysis. The said preprocessing cleaning technique includes –
? imputation with mean or model-based approaches like multivariate regression for cleaning of structured data;
? Noise reduction involves removing erroneous data and outliers using multivariate approaches such as Mahalanobis and Cook’s distance;
? Addressing data inconsistency from various sources is achieved through correlation analysis to identify and rectify discrepancies.
For cleaning of unstructured data, a different technique is utilized as follows –
? Stop words, punctuations, and non-ASCII characters are eliminated using regular expression scripts;
? Stemming reduces words to their base or root form, improving analysis, and is performed by dedicated modules;
? Lemmatization considers context to reduce words to their base form, aiding in identifying clinical and biological entities in notes or reports for the unstructured data in text format;
? Image resizing and normalization ensure consistency across different dimensions using methods like nearest neighbor and neural networks;
? Noise reduction techniques, including suppression of Pepper, Gaussian, and Poisson noise, are implemented using neural networks-based modules;
? Blur reduction is addressed with kernel filters like Gaussian blur and deep neural networks to enhance image quality for unstructured data in the image format.
According to an embodiment of the present invention, based on the data input and analysis, a pelvic health score is derived and computed using the AI/ML model. The steps involving the derivation of the health score are as follows –
? Computing the average probability of all model predictions per patient in the training dataset;
? Determining the minimum and maximum probabilities across all patients in the training dataset;
? Calculating the overall Pelvic health score for each patient using the formula:
Overall Pelvic risk score = (average probability of all risks for the patient- min)/ (max- min)
According to further embodiment, the patient is then categorized into the risk-vise category depending upon the received score, primarily the patients are categorized into three categories – (i) The highest 20% of scores are categorized as High risk; (ii) The next 40% of scores are categorized as medium risk and (iii) The lowest 40% of scores are categorized as low risk. This method ensures that the scores are normalized and standardized across patients, enabling effective risk stratification for clinical decision-making and intervention planning.
According to an embodiment of the present invention, a unique AI model consisting of following mathematical model is used to analyze the data, the mathematical model is as follows –
? Given that the Input data from a patient: ??={??1,??2,...,????} where ???? represents various features such as medical records, clinical observations, historical data, biosensor readings, etc;
? Middle layer representing diagnostic impact: ??={??1,??2,...,????} where ???? denotes the propensity for a specific risk or condition based on the input data ??;
? And output representing therapy outcomes: ??={??1,??2,...,????} where ???? represents different therapy outcomes or pelvic conditions;
? The mathematical model can be mathematically represented as, mapping from input to middle layer: ????=??diagnosis(??1,??2,...,????) and mapping from middle layer to output: ????=??therapy(??1,??2,...,????);
? Wherein, ??therapy represents the mapping from the middle layer ?? to the output ??, capturing the overall impact of therapy outcomes. Each ???? represents the propensity or effectiveness of different therapy outcomes or pelvic conditions and both ?? and ?? contain multiple real values between 0 and 100, denoted as ???? and ????, respectively, representing the degree of propensity for risks or effectiveness of therapy outcomes.
According to an embodiment, the model employs sophisticated algorithms and machine learning techniques to learn these mappings from data, optimizing them to provide accurate predictions and insights into pelvic health risks and therapy outcomes.
According to further embodiment, the Deep neural network (DNN) model of the system, used for pelvic health risk ?? (e.g., incontinence) prediction is as follows –
? For each layer ??, the output ??[??]can be calculated using the formula:
??[??]=??(??[??]????[??-1]+??[??]);
? Where:
??[??-1] is the input to layer ??;
??[??] represents the weights associated with layer ??;
??[??]denotes the biases for layer ??;
??(·) is the activation function applied element-wise to the linear transformation;
? For the first hidden layer, ??[0]=??where ??represents the input data;
Depending on the position of each layer (hidden or output), an appropriate activation function is employed. Common activation functions include the sigmoid function: ??(??)=11+??-?? and the rectified linear unit (ReLU) function: ??(??)=max?(0,??);
? For each observation, the loss function ?? is calculated using:
??=-[??log?(??^)+(1-??);
? Where:
??^ represents the predicted probability output by the neural network;
?? is the true label;
? For the total of ?? observations, the cost function ??(??,??) is calculated as the average of the loss function over all observations: ??(??,??)=1?????=1????(????,????);
? During each iteration of training, the weights ?? and biases ?? are updated using gradient descent to minimize the cost function. The gradients are calculated using backpropagation:
???(??,??)???=???(??,??)???×??????×???????w?L(z,y)=?a?L(z,y)×?z?a×?w?z;
? Where:
???(??,??)????a?L(z,y) is the partial derivative of the loss function with respect to the activation of the current layer;
? ???????z?a is the derivative of the activation function;
? ???????w?z and ???????ß?z are the partial derivatives of the linear transformation with respect to the weights and biases, respectively.
According to an embodiment of the present invention, in the medical domain, where there may be uncertainty in inferring interactions among input data, leveraging deep neural networks offers a robust solution by automatically learning hierarchical representations from the data to make accurate predictions.
According to further embodiment, the Recurrent Neural Network (RNN) of the present system is expressed mathematically as –
? Considering ??1??as an RNN used for processing textual data capturing characteristics of women with pelvic pain. The RNN processes the textual data in sequences, where the input sequence ??=(??1...,????) of length ?? is transformed into a sequence of outputs ??=(??1,...,????) where ??m is the length of the output sequence;
? The RNN is trained on a dataset ??={(??(??),??(??))}?? of size ??, where each ??(??)represents a textual input sequence and ??(??) represents the corresponding output sequence;
? At each layer of the RNN, the propagation of layers in the network can be represented using the following equation:
??:??h×????×???????h
? Where:
???? represents the product space of the input;
??h represents the product space of the hidden states;
???? represents the product space of parameters ??.
In simpler terms, for any given hidden state h, input data ??, and estimated parameters ???, the function ??(h,??,??) produces an output in the product space ??h.
The RNN is distinct from a generic neural network in that at any ????h layer, the inputs come from the (??-1)??h hidden layer. The hidden states at each layer are processed by h??=??(h??-1,????,??). Prediction at the final layer is performed using ??^??=(??°??1)(h), where ????=????°?°??1 with each function ????:??h???h.
For every layer of the RNN that produces an output, the loss is computed. The total loss is computed at the final layer as the sum of losses incurred at each layer:
??(??)=???(??,??^??))
Where ??(??,??^??) is the loss between the true output ??and the predicted output ??^??.
Each value of the vector ?? denotes a clinical entity, including symptoms, medical history, age, race, test results, etc. This way, unstructured information in the documents is extracted and processed along with other characteristics for the prediction of risks.
According to further embodiment, the Support Vector Regression (SVR) model of the present system is expressed as follows –
? Considering ??4?? as an SVR model used to model the input and output data for the pelvic pain , where the generic mapping function is defined as:
??=??(??)=??????+??
According to the embodiment, In SVR, the model can deal with nonlinear data by mapping the input data to a higher-dimensional space through a kernel function, and then finding a hyperplane that best fits the training data. This hyperplane is typically more stable and less sensitive to small changes in the data characteristics. Wherein, the structure of the hyperplane is determined by the selection of the kernel function. Choosing a kernel function with higher dimensions may result in more support vectors, which can increase the training time.
During training, the model learns the support vectors of the hyperplane using the training data. The cost function ??(??) for SVR is defined as:
??(??)=?????=1??(????-??(????))+12||??||2
? Where:
??is the penalty parameter that controls the trade-off between maximizing the margin and minimizing the error;
?? is the number of training samples;
???? is the true output for the ith sample;
??(????) is the predicted output for the ????h sample;
?? is the weight vector;
The objective of optimization is to obtain the best estimates of the parameters ??w using unconstrained optimization. The modified cost function for optimization with slack variables is:
??(??)=?????=1??(????++????-)+12||??||2
? Where ????+?i+ and ????-?i- denote slack variables.
SVR learns a hyperplane to model the input-output relationship, with the flexibility to handle nonlinear data using kernel functions. The optimization process aims to minimize the cost function while considering the penalty parameter and slack variables, resulting in the best estimates of the parameters ??.
According to further embodiment of the present invention, the K model of the present system is given as follows –
? For a considered risk ?? (e.g., incontinence), the AI suite employs ??n number of machine learning algorithms to learn functions ??1??,??2??,??3??,...,?????? that can map input ?? to output ?? using the data points {(??1,??^1),(??2,??^2),...,(????,??^?? where ??i represents input data and ??^?? represents the corresponding predicted output or label.
Mathematically, this can be represented as:
??????:?????, where ??=1,2,...,??
Each ??????learns a mapping from input space ?? to output space ??specifically for the risk ??. These functions are trained using machine learning algorithms such as neural networks, decision trees, support vector machines, etc.
Once the functions are learned by training the algorithms, the AI suite selects the optimal function ????*:????? that best fits the training data. This selection is evaluated by considering a wide range of machine learning performance metrics such as accuracy, precision, recall, F1-score, area under the ROC curve, etc. The selected model ????*represents the best-performing model for predicting the propensity score ???? for risk ??.
Mathematically, this can be represented as:
????*(??')=????
? where ??'' represents real data, and ???? represents the predicted propensity score for risk ??;
Consider ?????? as a deep neural network architecture for predicting the risk ??k (e.g., pelvic pain). Each layer of the neural network is represented by ??, where ?? is the number of layers for the ????h observation.
The output of each layer, denoted as Figure ??[??] can be calculated using the formula:
Figure ??[??]=????[??]????[??-1]+????[??]
? Where:
????[??] represents the weights associated with the ????h layer for the ????h observation;
??[??-1]is the input layer. For the first hidden layer, ??[0]=?? where ?? represents the input data. For subsequent layers, ??[??-1] is the output of the previous layer;
????[??] denotes the biases for the ????h layer for the ????h observation;
Depending on the position of each layer (hidden or output), an appropriate activation function is employed. Common activation functions include the sigmoid function:
??(??)=11+??-??
And the rectified linear unit (ReLU) function:
??(??)=max?(0,??)
For each observation, the loss function ??is calculated using the formula:
??=-[??log?(Figure ??[??])+(1-??)log?(1-Figure ??[??])]
? Where:
?? is the true label for the observation;
Figure ??[??]is the predicted output for the observation;
For the total of ??m observations, the cost function ??(??,??) is calculated as the average of the loss function over all observations:
??(??,??)=1?????=1????(Figure ??[??],????)
During each iteration of training, the weights ??w and biases ?? are updated using gradient descent to minimize the cost function. The gradients are calculated using backpropagation to adjust the parameters ??w and ?? accordingly.
According to an embodiment of the present invention, the deep neural network iteratively learns to minimize the cost function by updating its weights and biases, enabling it to make accurate predictions for the risk ??.
According to an embodiment of the present invention, AI algorithms analyzes the data from wearable sensors to identify pressure points and areas of musculoskeletal tension, guiding the design of assistive devices and rehabilitation protocols to alleviate discomfort and prevent injury.
Further, periodicity and treatment scheduling are done based on the patterns of movement, physiological responses, and pain fluctuations analysis to optimize the timing and frequency of treatment sessions, ensuring maximal therapeutic benefit and patient adherence.
According to further embodiment, telemedicine integration is done using AI-driven analysis generates actionable insights and treatment recommendations, which can be communicated to healthcare providers and patients via telemedicine platforms. Remote monitoring capabilities allow for real-time adjustments to treatment plans based on ongoing data analysis and patient feedback.
According to further embodiment, AI algorithms enable robotic devices to adaptively respond to patient movement patterns and progress, providing personalized assistance and feedback during rehabilitation exercises.
Virtual reality therapy is provided where AI algorithms enhances and assists the Virtual environments that are dynamically customized based on real-time physiological and biomechanical data, optimizing the therapeutic impact of VR-based rehabilitation interventions.
Further, AI-powered BCIs decode neural signals to facilitate seamless interaction with assistive devices and virtual environments, enhancing the accessibility and efficacy of neurorehabilitation therapies.
The present system integrates various data parameters specific to each patient to compute the overall Pelvic health and risk score. This score serves as a comprehensive representation of the entirety of pelvic health risks, consolidating multiple factors into a single value. By quantifying these risks into a unified metric, the system facilitates the stratification of overall pelvic health risk, enabling healthcare providers to identify patients who may require intervention or monitoring more urgently.
Moreover, the system offers a unique and specific approach to predicting various pelvic health issues, including but not limited to, incontinence, chronic pelvic pain, and sexual health concerns. It achieves this by considering a diverse array of 16 comprehensive risk factors, covering a wide spectrum of pelvic conditions. These risk factors are meticulously selected to encompass various aspects of pelvic health, ensuring a holistic assessment and prediction model.
Working example:
Patient ABC is a 50-year-old woman with a medical history that includes two pregnancies, a sedentary lifestyle, and type 2 diabetes. She experiences moderate pelvic pain (5/10 on a pain scale), mild prolapse, and frequent urinary incontinence. Despite attempts at pelvic floor exercises, she has seen limited success. The application of the Pelvic Health Score Module provides an AI-driven comprehensive assessment and personalized treatment recommendations.
During the initial assessment, data from various sources are collected. Patient demographics include age (50) and BMI (30, indicating overweight). Physiological parameters include blood pressure (140/90, hypertensive) and blood glucose (180 mg/dL, poorly controlled diabetes). Clinical examination findings indicate moderate pelvic muscle weakness and stage 1 pelvic organ prolapse. Patient-reported symptoms are recorded, with pain level at 5/10 and incontinence frequency at 5 times/day. Biosensor data shows low daily physical activity, poor sleep, and frequent nocturnal bladder activity. Multiomics data reveals genetic markers associated with connective tissue disorders. Imaging data from an MRI indicates moderate prolapse and thinning of the pelvic floor muscles.
In the middle layer processing, the AI calculates various scores. The pain score is influenced by high BMI (+15%), poor glucose control (+25%), pelvic muscle weakness (+30%), and a sedentary lifestyle (+20%), resulting in a final pain score of 65 out of 100. The prolapse score is affected by high BMI (+20%), genetic markers (+15%), pelvic muscle weakness (+25%), and imaging data (+30%), leading to a final prolapse score of 60 out of 100. The incontinence score is impacted by poor glucose control (+30%), pelvic muscle weakness (+25%), sedentary lifestyle (+20%), and frequent nocturnal activity (+25%), culminating in a final incontinence score of 70 out of 100.
The output layer analysis provides an overall Pelvic Health Score of 65 out of 100. Detailed impact analysis visualizes the contributing factors for each condition. For pain, the contributing factors are poor glucose control (25%), pelvic muscle weakness (30%), high BMI (15%), and a sedentary lifestyle (20%). For prolapse, the contributing factors include high BMI (20%), genetic markers (15%), pelvic muscle weakness (25%), and imaging data (30%). For incontinence, the contributing factors are poor glucose control (30%), pelvic muscle weakness (25%), sedentary lifestyle (20%), and frequent nocturnal activity (25%).
The AI-driven treatment recommendations and prioritization are divided into three phases. Phase 1 focuses on pelvic muscle strengthening and glucose control. Intensive pelvic floor muscle training is recommended twice weekly, with expected improvements in pain score by 30% and incontinence score by 25% over 5 weeks. Diabetes management includes medication adjustments and consultation with a dietitian, expected to improve overall pelvic health by 20% over 8 weeks. Phase 2 addresses prolapse and continues strengthening with weekly ultrasound therapy sessions, expected to improve the prolapse score by 40% over 6 weeks. Wearable sensor monitoring tracks daily activity and nocturnal bladder activity, providing real-time feedback and expected to improve the incontinence score by 15% and pain score by 10% over 6 weeks. Phase 3 involves a comprehensive physical activity program and weight management, with a tailored fitness plan expected to improve overall pelvic health by 25% over 8 weeks. Teletherapy and virtual reality therapy offer remote monitoring, support, and biofeedback for pelvic floor exercises.
The what-if analysis provides scenarios to predict outcomes based on patient adherence. If ABC improves her glucose control and reduces BMI, the model predicts a 50% overall improvement in pelvic health within 12 weeks. If she strictly adheres to the physical therapy program but does not manage her glucose levels, the predicted improvement drops to 30%.
After following the AI-driven treatment plan, ABC experiences significant improvements. Pelvic pain is reduced to 2/10, incontinence episodes decrease to 1-2 times per day, and prolapse symptoms lessen, reducing the sensation of pressure. The overall Pelvic Health Score improves from 65 to 35. ABC uses the visualization tools to track her progress and understand the impact of her lifestyle choices on her pelvic health. The what-if analysis helps her stay motivated and adhere to her treatment plan.
The AI-driven score and detailed impact analysis allow ABC's physician to make data-driven decisions, prioritize treatment options, and adjust the plan based on real-time feedback and progress. The Pelvic Health Score computation module standardizes the assessment of pelvic health conditions, providing personalized, actionable insights for both patients and physicians. By leveraging AI and integrating diverse data sources, it enhances treatment efficacy and patient outcomes, transforming subjective assessments into objective, data-driven decisions.
The above description is only a preferred embodiment of the present invention, and it should be understood that the description of the above embodiments is only for helping to understand the method of the present invention and its core idea, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, etc. made within the spirit and scope of the present invention are intended to be included within the scope of the present invention. , C , C , C , Claims:We claim,
1. An artificial intelligence driven pelvic health monitoring and risk assessment system, the said system comprising:
- plurality of data sources including biosensors, wearables devices, clinical inputs, demographics, imaging modalities, and diagnostic measures omics data for gathering patient health data;
- data storage and preprocessing modules configured to organize and preprocess collected data to enhance quality, reliability, and accessibility;
- machine learning models, including deep neural networks, recurrent neural networks, support vector regression, and ensemble methods, for analyzing collected data and predicting pelvic health risks;
- an integrated decision making/ assisting tool with telemedicine platform for generating actionable insights and treatment recommendations based on AI-driven analysis, facilitating remote monitoring and real-time adjustments to treatment plans;
- assistive technologies such as wearable sensors, rehabilitation robotics, virtual reality therapy, and brain-computer interfaces for personalized assistance and feedback during rehabilitation exercises;
characterized by a Pelvic Health Score computation module configured to derive a comprehensive metric representing the overall pelvic health and individual diagnostic scores and risk profile of an individual patient based on multiple data sources and predictive models; and
the computed Pelvic Health Score serving as a unified metric for assessing the overall pelvic health and risk status as outcome of individual physiological pathological diagnostic conditions of an individual patient, facilitating patient categorization into risk categories and enabling personalized care planning, intervention prioritization, and treatment customization.
2. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the wearable devices consist of pelvic floor sensors, heart rate variability monitors, PPG data, pulse oxy, and electromyography sensors for capturing physiological data relevant to pelvic health NCV, ECG
3. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the clinical inputs include patient-reported symptoms, medical history records, and omics data obtained through interviews and questionnaires.
4. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the imaging modalities include ultrasound scans, CT scans, and MRI images to visualize pelvic anatomy and identify potential abnormalities.
5. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the data sources further comprise smartphones, iPads, desktop/personal computers, and servers, deployed either on-premises or on the cloud.
6. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the preprocessing module includes techniques such as normalization, standardization, outlier detection, text tokenization, stop-word removal, stemming, lemmatization, and image preprocessing using convolutional neural networks.
7. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the machine learning models are trained to predict pelvic health risks based on input features, and to derive an organ level/ diagnosis level/ symptom level as well as overall Pelvic Health Score representing the comprehensive assessment of pelvic health and risk.
8. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the Pelvic Health Score is computed based on a combination of input features including patient demographics, physiological parameters, clinical examination findings, and patient-reported symptoms.
9. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the computation of Pelvic Health Score involves analyzing the output of multiple machine learning models trained to predict various pelvic health risks, computing the average probability of all model predictions per patient, and normalizing the average probability to determine the diagnosis specific as well as overall Pelvic Health Score.
10. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the computed Pelvic Health Score serves as a unified metric for assessing the overall pelvic health and risk status of an individual patient, facilitating patient categorization into risk categories and enabling personalized care planning, intervention prioritization, and treatment customization.
11. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the computed Pelvic Health Score is stratified into high, medium, and low-risk categories based on predetermined thresholds, facilitating personalized care planning and intervention prioritization.
12. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the middle diagnostic layer comprises separate diagnostic scores for incontinence, sexual functionality, pain, pelvic anatomy, endometriosis, and somatic pain contributory score, prolapsed, Pelvic inflammatory and infection conditions derived from relevant input variables specific to each aspect.
13. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the output layer assigns effectiveness scores to interventions such as remote physiotherapy, robotic intervention, infrared therapy, ultrasound therapy, and virtual reality therapy, brain-computer interfaces, nerve stimulation ,nerve silencing, motor tone augmentation and relaxation techniques, neuromuscular coordination techniques, somatic pain management including relaxation, concentration and other psyco-active interventions, based on their impact on addressing pelvic health issues and patient preferences.
14. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the effectiveness scores assigned to interventions are dynamically adjusted based on patient feedback and real-world outcomes, ensuring continuous optimization of treatment strategies for pelvic health management
15. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein the virtual reality therapy is enhanced by AI algorithms to dynamically customize virtual environments based on real-time physiological and biomechanical data, optimizing therapeutic impact.
16. The pelvic health monitoring and risk assessment system as claimed in Claim 1, wherein brain-computer interfaces decode neural signals to facilitate seamless interaction with assistive devices and virtual environments, improving accessibility and efficacy of neurorehabilitation therapies.