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Related Experiment Video

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A Hyperandrogenic Mouse Model to Study Polycystic Ovary Syndrome

A Hyperandrogenic Mouse Model to Study Polycystic Ovary Syndrome

Published on: October 2, 2018

Intelligent detection for Polycystic Ovary Syndrome (PCOS): Taxonomy, datasets and detection tools.

Meng Li¹, Zanxiang He¹, Liyun Shi²

¹Shenzhen Technology University, Shenzhen, Ghuangdong, China.

Computational and Structural Biotechnology Journal

|April 28, 2025

Summary

Intelligent Polycystic Ovary Syndrome (PCOS) detection is hindered by data limitations and tool deficiencies. This study proposes a framework and taxonomy to guide future research for improved diagnostic accuracy.

Keywords:

Detection tool Intelligent detection Polycystic Ovary Syndrome (PCOS)Taxonomy

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Last Updated: May 5, 2026

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Published on: October 2, 2018

Evaluation of Hepatic Glucose Production in a Polycystic Ovary Syndrome Mouse Model

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Published on: March 5, 2022

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Published on: July 5, 2024

Area of Science:

Reproductive Endocrinology
Medical Informatics
Artificial Intelligence in Medicine

Background:

Intelligent algorithms are increasingly used for Polycystic Ovary Syndrome (PCOS) diagnosis.
Current diagnostic tools face challenges including lack of standardized features, limited datasets, and unclear tool capabilities.

Purpose of the Study:

To address gaps in intelligent PCOS detection research.
To introduce a novel analytical framework and a comprehensive 108-feature taxonomy for PCOS diagnostic research.
To analyze existing datasets and intelligent detection tools.

Main Methods:

Developed a comprehensive taxonomy of 108 features across 8 categories for PCOS diagnosis.
Analyzed 12 publicly accessible datasets for feature coverage and quality.
Assessed 42 intelligent PCOS detection tools for capabilities and limitations.

Main Results:

Publicly available datasets cover only 54% of the identified PCOS diagnostic features.
Datasets often lack multimodal integration, updates, and clear licensing.
Identified limitations in detection tools include high computational needs, poor multimodal processing, and insufficient clinical validation.

Conclusions:

Significant challenges exist in developing robust intelligent PCOS detection tools due to data and tool limitations.
Future research should focus on addressing data gaps, enhancing multimodal integration, and improving clinical validation of diagnostic tools.