Using deep learning systems for diagnosing common skin lesions in sexual health

作者：Nyi Nyi Soe, Phyu Mon Latt, David Lee, Zhen Yu, Martina Schmidt, Melanie Bissessor, Ei T. Aung, Zongyuan Ge, Rashidur Rahman, Eric P. F. Chow, Jason J. Ong, Christopher K. Fairley, Lei Zhang · 发表于：Communications Medicine · 年份：2025 · DOI：10.1038/s43856-025-01144-7 · 被引用次数：3 · 研究领域：Cutaneous Melanoma Detection and Management、Nonmelanoma Skin Cancer Studies、Dermatological and COVID-19 studies

BACKGROUND: Early identification and treatment of sexually transmitted infections (STIs) prevents complications and improves STI control. However, there are obstacles to delivering accessible care, particularly for genital conditions. METHODS: We developed a deep learning system (DLS) using 15,891 clinical images from public repositories and the Melbourne Sexual Health Centre (MSHC) to classify 33 anogenital dermatological conditions, including STIs and non-STIs. We prospectively collected 336 images to evaluate the DLS's accuracy and compared it to the clinician diagnosis. We also evaluated whether DLS recommendations aligned with clinical urgency for seeking care based on the diagnosis. RESULTS: On the hold-out test dataset, the DLS achieves an accuracy of 59.2% (top-1) (standard deviation (SD) 0.7%) and the correct diagnosis is included in the top five diagnoses (top-5) with an accuracy of 82.1% (SD 13.3%). On the 8-month prospective dataset at MSHC, the DLS achieves a top-1 accuracy of 52.1%, top-3 of 73.8%, and top-5 of 89.9%. The performance varies across 33 diagnoses, with the majority (77%) of the diagnoses achieving over 80.0% for top-5 accuracy. The DLS recommendation based on top-5 diagnoses for seeking care maintains 100% sensitivity for urgent cases (e.g. syphilis) but a lower positive predictive value (59.5%). The recommendation based on top-1 diagnosis provides more balanced sensitivity (85.0%) and PPV (80.5%). CONCLUSIONS: The DLS demonstrates satisfactory sta...