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Infertility in Females

Infertility in Females

Female infertility is defined as the inability to conceive after a year of regular, unprotected intercourse and affects about 10–15% of couples worldwide. The primary cause of female infertility is ovulatory disorders, which hinder the release of eggs. These disorders can be classified as hypothalamic amenorrhea, polycystic ovarian syndrome (PCOS), premature ovarian failure, and hyperprolactinemic anovulation disorders.
Endometriosis, a condition characterized by abnormal growth of...

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AI-quantum framework for accurate infertility risk classification in PCOS patients using EHR data.

T Sarath¹, K Brindha¹

¹School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Frontiers in Artificial Intelligence

|April 9, 2026

Summary

This summary is machine-generated.

This study introduces an AI-quantum hybrid framework to predict infertility risk in women with Polycystic Ovary Syndrome (PCOS). The novel approach enhances diagnostic accuracy by analyzing complex clinical data, improving early detection for personalized reproductive healthcare.

Keywords:

LSTM artificial intelligence electronic health record infertility polycystic ovary syndrome quantum computing

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Area of Science:

Artificial Intelligence
Quantum Computing
Reproductive Health

Background:

Infertility affects 1 in 6 couples globally, with ovulatory disorders and Polycystic Ovary Syndrome (PCOS) being significant contributors to female infertility.
Traditional diagnostic methods for PCOS-related infertility often overlook valuable insights within comprehensive patient health records, including BMI, AMH levels, and menstrual irregularities.

Purpose of the Study:

To develop and evaluate a novel AI-quantum hybrid framework for improved infertility risk prediction in women with PCOS.
To leverage advanced AI and quantum-inspired models to analyze complex clinical data for enhanced diagnostic accuracy.

Main Methods:

Integration of classical deep learning (LSTM) with quantum-inspired models (Quantum LSTM) in a hybrid framework.
Training and evaluation of the framework using a PCOS dataset encompassing diverse clinical attributes relevant to reproductive health.

Main Results:

The AI-quantum hybrid framework demonstrated superior infertility risk classification compared to traditional machine learning methods.
The model effectively identified complex relationships within heterogeneous and incomplete clinical data, showing robust predictive performance.

Conclusions:

The proposed AI-quantum framework shows significant potential for early infertility risk detection in PCOS patients.
This approach can aid clinicians in efficient patient identification, contributing to personalized reproductive healthcare and improved diagnostic outcomes.