AI Menopause Prediction from a Single FSH/Estradiol Blood Sample
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Solution Overview
Problem
Current methods lack an accurate and efficient way to predict the onset of menopause using a single blood draw, leading to delayed diagnoses and inappropriate treatments, which can result in severe health issues.
Innovation Solution
A computer system utilizing an AI model trained on the ratio between follicle-stimulating hormone (FSH) and estradiol levels, along with demographic data, to predict the time to final menstrual period (FMP) from a single blood sample, incorporating features like race and geographical location for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional menopause diagnosis methods are used (multiple appointments, repeated blood draws), then diagnostic accuracy may be improved through longitudinal observation, but patient burden and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by developing and validating the FSH/Estradiol ratio algorithm before clinical implementation, creating a predictive tool that can estimate time to final menstrual period (FMP) in advance. This allows clinicians to predict menopause timing proactively rather than waiting for retrospective confirmation through multiple appointments, thereby reducing the time to diagnosis while maintaining accuracy.
Solution Approach 2:
The patent transforms the diagnostic approach by changing from monitoring absolute hormone levels over time to calculating the FSH/Estradiol ratio from a single blood draw. This parameter transformation enables accurate prediction with one-time measurement, eliminating the need for repeated sampling while maintaining or improving diagnostic precision.
2Measurement precision
If repeated blood draws are performed to monitor hormone levels over time, then the accuracy of menopause prediction improves, but patient discomfort and healthcare costs increase
Solution Approach 1:
The patent fundamentally changes the measurement parameter from absolute hormone concentrations requiring longitudinal sampling to the FSH/Estradiol ratio that can be accurately determined from a single blood draw. This parameter transformation maintains prediction accuracy while dramatically improving sampling convenience by eliminating repeated punctures.
Solution Approach 2:
The patent extracts the essential predictive information (FSH/Estradiol ratio) from the complex longitudinal hormone profile, concentrating the diagnostic value into a single calculable metric from one blood sample. This extraction allows accurate prediction without requiring the full longitudinal dataset, thereby reducing patient burden.
3Device complexity
If menopause diagnosis is delayed or misdiagnosed, then fewer invasive procedures are performed, but health outcomes deteriorate due to untreated hormonal imbalances
Solution Approach 1:
The patent changes the diagnostic parameter to FSH/Estradiol ratio, which provides a reliable and simple metric for predicting time to FMP. This single ratio calculation from routine blood work enables accurate diagnosis without complex protocols, ensuring timely intervention and reliable health outcomes while maintaining diagnostic simplicity.
4Adaptability or versatility
If individualized hormone replacement therapy is prescribed without accurate prediction, then treatment may be started too early or too late, but implementing personalized medicine requires complex monitoring protocols
Solution Approach 1:
The patent uses the FSH/Estradiol ratio parameter to provide a simple, quantitative predictor of time to FMP that enables personalized treatment timing without complex monitoring. This single ratio metric allows clinicians to tailor HRT initiation to each patient's predicted menopause timing, achieving treatment personalization through a straightforward calculation rather than complex protocols.
Data Source
AI summary
An Artificial Intelligence (AI) system trained by a plurality of training data. The AI system is trained by relationships between a time to final menstrual period and ratios between follicle-stimulating hormone levels and estradiol levels, anti-mullerian hormone, race, cholesterol levels, and a presence of one or more contraceptives. The AI system is configured to accept data from only one sample from a user as input and generate a prediction of a time to final menstrual period for the user as output.