SoK: Intelligent Detection for Polycystic Ovary Syndrome(PCOS)

O_FIG O_LINKSMALLFIG WIDTH=146 HEIGHT=200 SRC="FIGDIR/small/24319623v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@805345org.highwire.dtl.DTLVardef@db004dorg.highwire.dtl.DTLVardef@1f0cbfaorg.highwire.dtl.DTLVardef@1dfb41d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG HighlightsO_LIConducted a systematic review of the existing literature, focusing on Polycystic Ovary Syndrome intelligent detection, and constructed the comprehensive taxonomy for PCOS detection features to date, providing a standardized reference for future research. C_LIO_LISystematically evaluated the capabilities and limitations of current intelligent PCOS detection tools, offering valuable guidance for the development of more efficient and accurate tools. C_LIO_LIThoroughly analyzed the current status of 12 publicly available datasets used for PCOS detection, providing clear directions for future dataset development in this field. C_LIO_LIMade the analysis results publicly available, providing data resources and references for researchers, with the aim of advancing the field of intelligent PCOS detection. C_LI 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datasets, the overall coverage rate is only 52% compared to the known 110 features, with a lack of multimodal datasets, outdated updates and unclear license information.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.