Evaluating the Performance of Mobile Machine-Learning Platforms for Syphilis Symptom Screening
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Published on: October 27, 2023
Lao-Tzu Allan-Blitz1, Kelika A Konda2,3, E Michael Reyes-Diaz2
1From the Department of Medicine, Division of Global Health Equity, Brigham and Women's Hospital, Boston, MA; Division of Infectious Diseases, Department of Medicine. University of California Los Angeles, Los Angeles, CA.
Abstract:
We evaluated the performance of 3 machine-learning models for classifying 39 cases of primary and secondary syphilis using associated meta-data and clinical images. All 3 models correctly classified 33 images, with an overall precent agreement of 84.6% (95% confidence interval: 69.5%-94.1%). Machine-learning models may support patient-driven symptom screening.

