Using deep learning systems for diagnosing common skin lesions in sexual health
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.
Authors
- Zongyuan Ge (ORCID: https://orcid.org/0000-0002-5880-8673)
- Melanie Bissessor (ORCID: https://orcid.org/0000-0002-3686-9184)
- Eric P. F. Chow (ORCID: https://orcid.org/0000-0003-1766-0657)
- Jason J. Ong (ORCID: https://orcid.org/0000-0001-5784-7403)
- Nyi Nyi Soe (ORCID: https://orcid.org/0009-0001-4554-3549)
- Rashidur Rahman (ORCID: https://orcid.org/0009-0001-9902-4066)
- Phyu Mon Latt (ORCID: https://orcid.org/0000-0001-5880-5731)
- Christopher Kincaid Fairley (ORCID: https://orcid.org/0000-0001-9081-1664)
- Ei Thu Aung (ORCID: https://orcid.org/0000-0002-2560-3233)
- David Lee (ORCID: https://orcid.org/0009-0006-5423-4525)
- Lei Zhang (ORCID: https://orcid.org/0000-0003-2343-084X)
- Martina Schmidt
Institutions
- The University of Melbourne (AU)
- Melbourne Sexual Health Centre (AU)
- Monash Health (AU)
- Second Affiliated Hospital of Xi'an Jiaotong University (CN)
- Alfred Health (AU)
- Monash University (AU)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-11-03
- DOI
- https://doi.org/10.1038/s43856-025-01144-7
- Citations
- 1
- Primary Topic
- Cutaneous Melanoma Detection and Management
- Type
- article
- Field-Weighted Citation Impact
- 1.05
Funders
- Monash University
- National Health and Medical Research Council