Advancing Polycystic Ovary Syndrome Detection with Artificial Intelligence Techniques

Abir Gorrab, Nourhène Ben Rabah, Isuri Kariyawasam, Bénédicte Le Grand

2025

Abstract

Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder that affects women of reproductive age. Diagnosis mainly relies on traditional methods, such as clinical evaluations or laboratory tests, which can be expensive and time-consuming and are often accompanied by complex imaging techniques. The integration of Artificial Intelligence (AI), namely Machine Learning (ML) and Deep Learning (DL), seems to offer promising opportunities, allowing for the analysis of large datasets to improve PCOS detection and management. This work conducts a systematic literature review and aims to explore how ML and DL can optimize PCOS diagnosis by analyzing the most used data and algorithms while following a rigorous methodology to ensure the validity of the results. It also discusses the explainability of AI methods to be used by healthcare professionals, who are always looking for reliable results to support the best possible diagnosis for their patients.

Paper Citation

in Harvard Style

Gorrab A., Ben Rabah N., Kariyawasam I. and Le Grand B. (2025). Advancing Polycystic Ovary Syndrome Detection with Artificial Intelligence Techniques. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1023-1030. DOI: 10.5220/0013249800003890

in Bibtex Style

@conference{icaart25,
author={Abir Gorrab and Nourhène Ben Rabah and Isuri Kariyawasam and Bénédicte Le Grand},
title={Advancing Polycystic Ovary Syndrome Detection with Artificial Intelligence Techniques},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1023-1030},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013249800003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Advancing Polycystic Ovary Syndrome Detection with Artificial Intelligence Techniques
SN - 978-989-758-737-5
AU - Gorrab A.
AU - Ben Rabah N.
AU - Kariyawasam I.
AU - Le Grand B.
PY - 2025
SP - 1023
EP - 1030
DO - 10.5220/0013249800003890
PB - SciTePress