Scholarlink

Background <p>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.</p> Methods <p>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.</p> Results <p>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%).</p> Conclusions <p>The DLS demonstrates satisfactory statistical accuracy that would have been inadequate for clinical use. Future work should evaluate the DLS’s performance across expanded populations and skin conditions from multiple clinics in different countries and determine how such tools could be used for the public good.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月3日

查看全文
  • Nyi Nyi Soe,
  • Phyu Mon Latt,
  • David M. Lee,
  • Zhen Yu,
  • Martina Schmidt,
  • Melanie Bissessor,
  • Ei Thu Aung,
  • Zongyuan Ge,
  • Rashidur Rahman,
  • Eric P. F. Chow,
  • Jason J. Ong,
  • Christopher K. Fairley,
  • Lei Zhang

摘要

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 statistical accuracy that would have been inadequate for clinical use. Future work should evaluate the DLS’s performance across expanded populations and skin conditions from multiple clinics in different countries and determine how such tools could be used for the public good.