Automated MRI Pelvic Organ Prolapse Prediction Model
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Solution Overview
Problem
Current methods for diagnosing pelvic organ prolapse (POP) using dynamic MRI are inadequate due to manual and time-consuming identification of reference points, lack of standardization, and limited correlation with clinical and surgical outcomes, particularly for posterior prolapse, and existing segmentation techniques face challenges with low contrast and high inhomogeneity in MRI images.
Innovation Solution
An automated method for predicting or diagnosing POP using MRI images, which involves identifying keypoints on the pubic bone, extracting features, and classifying them using a support vector machine (SVM) classifier, combined with clinical and demographic information to improve diagnosis accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of reference points is used for POP diagnosis, then diagnostic accuracy can be maintained through expert judgment, but the process becomes time-consuming and subjective
Solution Approach 1:
The system performs automated identification of reference points and measurement extraction without requiring manual expert intervention. The algorithm independently processes MRI images to identify pelvic bone structures, generate reference lines, and calculate POP measurements, eliminating the time-consuming manual process while maintaining diagnostic accuracy through validated algorithms
Solution Approach 2:
The patent replaces the manual mechanical process of expert measurement with an automated computational system. The algorithm substitutes human experts in identifying reference points and measuring distances, using image processing techniques to automatically detect anatomical landmarks and calculate POP-Q stage measurements from MRI images
2Productivity
If automated segmentation is implemented, then productivity and consistency are improved, but challenges arise with low contrast and high inhomogeneity in MRI images
Solution Approach 1:
The system transforms the detection problem by changing parameters - converting pixel intensity values into standardized measurement metrics. The algorithm adjusts for low contrast and inhomogeneity by applying intensity normalization and using relative position calculations rather than absolute intensity thresholds, enabling reliable identification despite image quality variations
Solution Approach 2:
The patent introduces intermediary reference structures (pelvic bone landmarks and reference lines) that serve as mediators between the raw MRI image data and the final POP measurements. These intermediaries provide stable geometric references that are less sensitive to contrast variations, facilitating automated detection through landmark-based coordinate systems
3Stability of the object's composition
If standardized measurement systems are established, then diagnostic consistency is improved, but adaptability to different prolapse types (especially posterior) is reduced
Solution Approach 1:
The system achieves universality by implementing a comprehensive measurement framework that handles multiple prolapse types through a single standardized algorithm. The automated system extracts measurements for anterior, apical, and posterior compartments using the same reference line system, enabling consistent diagnosis across different prolapse types without requiring separate specialized protocols
Data Source
AI summary
A system and methodology for the automated localization, extraction, and analysis of MRI-based features with clinical information to improve the diagnosis of pelvic organ prolapse (POP). The system can automatically identify reference points for pelvic floor measurements on MRI rapidly and consistent. It provides a prediction model that analyzes the correlation between current and new MRI-based features with clinical information to differentiate patients with and without POP. This system will enable the high throughput analysis of MR images for their correlation with clinical information to better detect POP. The presented system can also be applied to the automated localization and extraction of MRI features for the diagnosis of other diseases where clinical examination is not adequate.