Three-Parameter PCOS Screening Using AMH, BMI, and Cycle Length
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Solution Overview
Problem
Current diagnostic criteria for polycystic ovary syndrome (PCOS) are controversial and not easily accessible to general practitioners, leading to missed diagnoses and inadequate management of metabolic risks due to the unknown pathogenesis of the condition.
Innovation Solution
A system and method using anti-Mullerian hormone (AMH) levels, menstrual cycle duration, and Body Mass Index (BMI) to calculate the probability of PCOS, employing a logistic regression model to convert these variables into polytomous categories and apply a formula for risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If immunoassay-based chemiluminescence method is used to detect androstenedione, then PCOS screening can be performed, but the measurement accuracy is poor with correlation coefficient r2 less than 0.5
Solution Approach 1:
The invention extracts and removes the problematic androstenedione detection component from the PCOS screening system. By eliminating the reliance on immunoassay-based chemiluminescence method for androstenedione detection, the patent avoids the measurement accuracy issues while maintaining PCOS screening capability through alternative biomarkers with better diagnostic performance.
2Measurement precision
If multiple diagnostic criteria and indicators are used for PCOS diagnosis, then diagnostic accuracy may improve, but the system complexity increases making it difficult for general practitioners to implement
Solution Approach 1:
The invention extracts only the most critical and clinically accessible biomarkers (AMH, BMI, menstrual cycle characteristics) from the complex PCOS diagnostic criteria. This simplified subset maintains diagnostic accuracy while being easily implementable by general practitioners without requiring specialized equipment or complex evaluation protocols.
Solution Approach 2:
The invention creates a universal screening tool that can be applied across different clinical settings by general practitioners. The selected biomarkers (AMH, BMI, menstrual cycle data) are universally measurable and the calculation system can be implemented in various healthcare environments, making the diagnostic approach widely applicable rather than setting-specific.
3Reliability
If comprehensive PCOS evaluation with multiple parameters is performed, then diagnostic reliability improves, but the time and cost of testing increases
Solution Approach 1:
The invention extracts and focuses on only the three most essential parameters (AMH level, BMI, menstrual cycle characteristics) that provide the greatest diagnostic value. By eliminating less critical measurements, the system maintains high diagnostic reliability while significantly reducing the time and resources required for testing compared to comprehensive multi-parameter evaluations.
Data Source
AI summary
The present application relates to a system for screening for polycystic ovarian syndrome (PCOS). The system comprises: a data collection module, which is used for acquiring an anti-mullerian hormone (AMH) level of a subject, collecting an upper limit of the number of days of a menstrual cycle that is actively provided by the subject, and collecting data of the BMI of the subject; and a module for calculating the probability (p) of suffering from PCOS, wherein the module is used for performing calculation on data information acquired in the data collection module, so as to calculate p of the subject suffering from PCOS. By means of the system constructed in the present application, only three parameters, i.e., an AMH, the number of days of a menstrual cycle and the BMI, are used, such that the problem of androgen examination, by means of an immunization method, being inaccurate is prevented, thereby further reducing an examination time and the examination cost, and improving the examination efficiency and accuracy. Moreover, the applicant of the present application performs research in the real world, the sample volume is large, and it is presented by means of external verification that an optimized three-index PCOS model and the previous four-index model have the same prediction effect.