ArticleJournal of medical Internet research2024
Using AI to Differentiate Mpox From Common Skin Lesions in a Sexual Health Clinic: Algorithm Development and Validation Study.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of AI in Identification of Sexually Transmitted Infections and Anogenital Dermatoses: A Systematic Review and Meta-Analysis.JAMA network open · 2025Pooled it
- A three-generation mpox cluster in Chongqing, 2024: transmission chain investigation and phylogenetic characterization.Infection · 2026Article
- Diagnostic Accuracy of Commercial Large Language Models for Anogenital Skin Lesion Images: A Comparative Study of Gemini, Claude, and ChatGPT.The Journal of infectious diseases · 2026Article
- Using deep learning systems for diagnosing common skin lesions in sexual health.Communications medicine · 2025Article
- Beyond behavioural change: prioritising structural solutions to control bacterial sexually transmitted infections.EClinicalMedicine · 2025Review
- Impact of Result Displays in an Anogenital Symptom Checker App on Health-seeking Behaviours: A Cross-sectional, Vignette-based Study.Open forum infectious diseases · 2025Article
- What Do People Want from an AI-Assisted Screening App for Sexually Transmitted Infection-Related Anogenital Lesions: A Discrete Choice Experiment.The patient · 2025Article
- Urogenital Manifestations in Mpox (Monkeypox) Infection: A Comprehensive Review of Epidemiology, Pathogenesis, and Therapeutic Approaches.Infection and drug resistance · 2025Review
- Accuracy of symptom checker for the diagnosis of sexually transmitted infections using machine learning and Bayesian network algorithms.BMC infectious diseases · 2024Article
- Evaluation of artificial intelligence-powered screening for sexually transmitted infections-related skin lesions using clinical images and metadata.BMC medicine · 2024Article
- Harnessing Artificial Intelligence and Innovative Vaccines for Mpox Diagnosis and Control: A Comprehensive Narrative Review.Journal of primary care & community healthReview
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 2022 global outbreak of mpox has significantly impacted health facilities, and necessitated additional infection prevention and control measures and alterations to clinic processes. Early identification of suspected mpox cases will assist in mitigating these impacts.
objectiveWe aimed to develop and evaluate an artificial intelligence (AI)-based tool to differentiate mpox lesion images from other skin lesions seen in a sexual health clinic.
methodsWe used a data set with 2200 images, that included mpox and non-mpox lesions images, collected from Melbourne Sexual Health Centre and web resources. We adopted deep learning approaches which involved 6 different deep learning architectures to train our AI models. We subsequently evaluated the performance of each model using a hold-out data set and an external validation data set to determine the optimal model for differentiating between mpox and non-mpox lesions.
resultsThe DenseNet-121 model outperformed other models with an overall area under the receiver operating characteristic curve (AUC) of 0.928, an accuracy of 0.848, a precision of 0.942, a recall of 0.742, and an F
conclusionsOur study demonstrated it was possible to use an AI-based image recognition algorithm to accurately differentiate between mpox and common skin lesions. Our findings provide a foundation for future investigations aimed at refining the algorithm and establishing the place of such technology in a sexual health clinic.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.