Digital Twin for Pelvic Floor Diagnosis and Treatment
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Solution Overview
Problem
Current diagnostic and treatment methods for female pelvic floor disorders are inadequate, lacking comprehensive biomechanical characterization and predictive capabilities, leading to delayed diagnoses and ineffective interventions.
Innovation Solution
A digital twin framework that integrates heterogeneous health data, machine learning, and 3D modeling to provide personalized diagnosis, treatment planning, and long-term monitoring of pelvic floor conditions, incorporating tactile and ultrasound imaging for enhanced anatomical and functional insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinical examination alone is used to diagnose pelvic floor disorders, then the examination process is simple and quick, but the diagnostic accuracy is insufficient
Solution Approach 1:
The patent combines multiple diagnostic modalities including clinical examination, ultrasound imaging, and machine learning analysis into an integrated diagnostic system. This merging of different examination methods enables comprehensive biomechanical characterization of pelvic floor tissues while maintaining clinical workflow efficiency.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary that processes ultrasound imaging data and clinical examination results to generate predictive insights about pelvic floor tissue behavior. This intermediary layer translates complex imaging data into clinically actionable information, enhancing diagnostic accuracy without requiring clinicians to directly interpret complex datasets.
2Measurement precision
If comprehensive biomechanical measurements are implemented for pelvic floor characterization, then the diagnostic accuracy improves, but the time and resources required increase
Solution Approach 1:
The patent performs preliminary biomechanical assessment through machine learning analysis of ultrasound images and clinical data before proceeding to more time-consuming diagnostic procedures. This preliminary characterization identifies patients who require comprehensive biomechanical measurement, allowing clinicians to prioritize cases efficiently and reduce overall diagnostic time.
Solution Approach 2:
The patent replaces direct mechanical measurement of pelvic floor tissue properties with machine learning-based prediction models that analyze ultrasound imaging data. This substitution eliminates the need for complex mechanical testing devices and procedures, achieving accurate biomechanical characterization through computational methods instead of physical measurement.
3Reliability
If digital twin framework with machine learning is used for diagnosis and treatment planning, then treatment personalization and predictive accuracy improve, but the system complexity and computational requirements increase
Solution Approach 1:
The patent creates a digital twin—a virtual copy—of the patient's pelvic floor tissue mechanics based on ultrasound imaging and clinical data. This digital replica allows clinicians to simulate different treatment scenarios and predict outcomes without interfering with the actual patient's condition. The copying approach enables safe, reversible experimentation with treatment plans while maintaining system manageability.
Solution Approach 2:
The patent develops a universal machine learning framework that can be applied across different pelvic floor conditions and patient populations. This multi-functional system handles diverse diagnostic and predictive tasks using a common computational architecture, reducing overall system complexity compared to developing separate specialized models for each condition.
4Loss of time
If early diagnosis methods are implemented for conditions like endometriosis and adenomyosis, then disease progression can be prevented and fertility preserved, but the diagnostic cost and complexity increase
Solution Approach 1:
The patent implements preliminary screening using machine learning analysis of routine ultrasound imaging and clinical data to identify patients at risk for endometriosis, adenomyosis, and other pelvic floor disorders before symptoms become severe. This early identification allows for timely intervention while using already-available clinical resources, avoiding the need for complex specialized diagnostic procedures.
Solution Approach 2:
The patent replaces invasive surgical exploration and complex imaging procedures with machine learning-based analysis of standard ultrasound images and clinical examination data for early detection of pelvic floor pathologies. This substitution enables early diagnosis using non-invasive, cost-effective methods that are already widely available in clinical settings.
Data Source
AI summary
The present invention delineates a comprehensive methodology for the characterization, diagnosis, and treatment decision-making concerning conditions of the female pelvic floor, leveraging a digital twin paradigm. The method entails the acquisition of heterogeneous health data, including but not limited to gynecological, obstetrical, and imaging data, which is subsequently transferred to a remote database. This data repository encompasses various clinical cases and pelvic pathological conditions. A remote machine learning data-processing engine analyzes the collected data to establish a medical diagnosis, predict treatment outcomes, and generate a tailored three-dimensional anatomical model of the patient's pelvic floor. The processed information is returned to the clinician for visualization and serves as the basis for informed treatment decisions. This methodology accommodates multiple imaging modalities, such as Tactile, Ultrasound, Magnetic Resonance, and X-ray Imaging, and considers a wide array of possible treatments and pelvic pathologies. The invention provides the facility for storing all relevant data, diagnosis, treatment probabilities, and the adapted model in a digital twin, facilitating future consultations and treatment planning.