Recent research in the field of Polycystic Ovary Syndrome (PCOS) detection has increasingly utilized intelligent algorithms for automated diagnosis. These intelligent PCOS detection methods can assist doctors in diagnosing patients earlier and more efficiently, thereby improving the accuracy of diagnosis. However, there are notable barriers in the field of intelligent PCOS detection, including the lack of a standardized taxonomy for features, inadequate research on the current status of available datasets, and insufficient understanding of the capabilities of existing intelligent detection tools. To overcome these barriers, we propose for the first time an analytical framework for the current status of PCOS diagnostic research and construct a comprehensive taxonomy of detection features, encompassing 110 features across eight categories. This taxonomy has been recognized by industry experts. Based on this taxonomy, we analyze the capabilities of current intelligent detection tools and assess the status of available datasets. The results indicate that 12 publicly available datasets, the overall coverage rate is only 52% compared to the known 110 features, with a lack of multimodal datasets, outdated updates and unclear license information. These issues directly impact the detection capabilities of the tools. Furthermore, among the 45 detection tools require substantial computational resources, lack multimodal data processing capabilities, and have not undergone clinical validation. Based on these findings, we highlight future challenges in this domain. This study provides critical insights and directions for PCOS intelligent detection field.

The authors have declared no competing interest.

This work was partially supported by the NSFC International Collaboration and Exchange Progaram (No. W2412110), Science and Technology Program Project of Shenzhen(No. SZWD2021012), Natural Science Foundation of Top Talent of SZTU (grant no. GDRC202132), SZTU-Enterprise Cooperation Project(No. 20221061030002, and No. 20221064010094), Shenzhen Science and Technology Program (No. JCYJ20220818102215034).

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All data produced in the present study are available upon reasonable request to the authors