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Evaluating the Performance of Mobile Machine-Learning Platforms for Syphilis Symptom Screening
0
Zitationen
6
Autoren
2026
Jahr
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.
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