Evaluating the Performance of Mobile Machine-Learning Platforms for Syphilis Symptom Screening
We evaluated the performance of three machine-learning models for classifying 39 cases of primary and secondary syphilis using associated meta-data and clinical images. All three models correctly classified 33 images, with an overall precent agreement of 84.6% (95% CI 69.5-94.1%). Machine-learning models may support patient-driven symptom screening.
Authors
- Kelika Anne Konda (ORCID: https://orcid.org/0000-0003-2836-0174)
- Silver Keith Vargas (ORCID: https://orcid.org/0000-0002-8188-9598)
- Lao-Tzu Allan-Blitz
- Carlos F Caceres
- Jeffrey D. Klausner
- E. Michael Reyes-Diaz
Institutions
- University of Southern California (US)
- Brigham and Women's Hospital (US)
- Universidad Peruana Cayetano Heredia (PE)
Publication Details
- Journal
- Sexually Transmitted Diseases
- Published
- 2026-03-26
- DOI
- https://doi.org/10.1097/olq.0000000000002304
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00