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

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	352-356
Number of pages	5
Journal	Sexually Transmitted Diseases
Volume	53
Issue number	6
DOIs	https://doi.org/10.1097/OLQ.0000000000002304
State	Published - Jun 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1097/OLQ.0000000000002304

Cite this

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title = "Evaluating the Performance of Mobile Machine-Learning Platforms for Syphilis Symptom Screening",

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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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

Abstract

UN SDGs

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