miRNA Panel Detection of Endometriosis Without Laparoscopy
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Solution Overview
Problem
Current methods for diagnosing endometriosis are invasive and inefficient, often leading to delayed diagnosis due to reliance on laparoscopic examination, which is costly and time-consuming, and there is a need for minimally-invasive, accurate, and efficient detection and monitoring techniques.
Innovation Solution
The use of a panel of miRNAs, including miR-342 and miR451a, with a machine learning algorithm to detect and diagnose endometriosis by analyzing bodily fluid samples, providing a non-invasive method for detection, diagnosis, and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laparoscopic examination is used to diagnose endometriosis, then diagnostic accuracy is improved, but invasiveness and time consumption increase
Solution Approach 1:
The patent replaces the mechanical surgical intervention of laparoscopy with a biochemical detection system. Machine learning algorithms analyze patterns in blood test results, hormone levels, and patient symptoms to diagnose endometriosis non-invasively, substituting mechanical surgical tools with computational and biochemical methods while maintaining diagnostic accuracy
Solution Approach 2:
The patent introduces an intermediary diagnostic system consisting of machine learning models and biochemical markers that mediate between patient symptoms and definitive diagnosis. This intermediary layer processes complex patterns in hormone levels, inflammatory markers, and symptom profiles to provide accurate diagnosis without requiring direct surgical visualization
2Measurement precision
If laparoscopic examination is used to diagnose endometriosis, then diagnostic accuracy is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary diagnostic assessment through non-invasive blood tests and symptom analysis using machine learning algorithms before considering surgical intervention. This preliminary action filters out cases that can be diagnosed through less invasive means, reducing the overall time and resource consumption while maintaining high diagnostic accuracy for cases requiring surgery
3Measurement precision
If machine learning algorithm with multiple miRNA panel is used for detection, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex diagnostic task into distinct analytical components: individual machine learning models are trained on specific subsets of miRNA markers, hormone levels, and symptom patterns. This segmentation allows the system to process complex multi-dimensional data through specialized sub-models, improving detection accuracy while managing computational complexity through modular architecture
Data Source
AI summary
Described herein are improved methods for the detection of endometriosis. Generally, the methods include, but are not limited to, applying machine learning algorithm to miRNA levels in order to detect, predict, diagnose, or monitor the presence or absence of endometriosis.