Evaluating the Performance of Mobile Machine-Learning Platforms for Syphilis Symptom Screening

Lao Tzu Allan-Blitz; Kelika A. Konda; E. Michael Reyes-Diaz; Silver Vargas; Carlos F. Caceres; Jeffrey D. Klausner

doi:10.1097/OLQ.0000000000002304

Evaluating the Performance of Mobile Machine-Learning Platforms for Syphilis Symptom Screening

Lao Tzu Allan-Blitz
, Kelika A. Konda
, E. Michael Reyes-Diaz
, Silver Vargas
, Carlos F. Caceres
, Jeffrey D. Klausner

University of California
Keck School of Medicine of USC
Universidad Peruana Cayetano Heredia

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

Resumen

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.

Idioma original	Inglés
Páginas (desde-hasta)	352-356
Número de páginas	5
Publicación	Sexually Transmitted Diseases
Volumen	53
N.º	6
DOI	https://doi.org/10.1097/OLQ.0000000000002304
Estado	Publicada - jun. 2026

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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.",

author = "Allan-Blitz, \{Lao Tzu\} and Konda, \{Kelika A.\} and Reyes-Diaz, \{E. Michael\} and Silver Vargas and Caceres, \{Carlos F.\} and Klausner, \{Jeffrey D.\}",

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AU - Klausner, Jeffrey D.

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AB - 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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Evaluating the Performance of Mobile Machine-Learning Platforms for Syphilis Symptom Screening

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