Performance and clinical utility of image-based machine learning models for the diagnosis of neglected tropical diseases in low- and middle-income countries: a systematic review.

backgroundNeglected tropical diseases (NTDs) disproportionately affect populations in low- and middle-income countries (LMICs), where diagnostic capacity is often limited. Image-based machine learning (ML) has emerged as a potential tool to support diagnosis, but its clinical utility remains unclear.

aimTo systematically evaluate the diagnostic performance and clinical utility of image-based machine learning models for NTD diagnosis in LMICs.

methodsThis review was registered on PROSPERO and conducted in accordance with PRISMA guidelines. Searches were performed in PubMed/MEDLINE, Embase, Scopus, Web of Science, and IEEE Xplore from January 2010 up until January 31st, 2026. Peer-reviewed primary studies evaluating image-based ML models for NTD diagnosis in LMICs and reporting diagnostic performance metrics were included. Risk of bias was assessed using QUADAS-2, and findings were synthesised narratively.

resultsEight studies met the inclusion criteria. Microscopy-based ML models demonstrated consistently high performance, with reported sensitivities and specificities frequently above 90%, particularly for malaria and helminth infections. Clinical image-based models for skin NTDs showed more variable accuracy. External validation and implementation evaluation were inconsistently reported, limiting generalisability and clinical applicability.

conclusionImage-based ML models show strong diagnostic potential for NTDs in LMICs, especially in microscopy-supported workflows. However, translation into routine practice is constrained by limited dataset representativeness, inadequate external validation, and insufficient attention to operational feasibility and explainability. Future research must prioritise implementation-oriented evaluation to realise public health impact. CLINICAL TRIAL NUMBER: Not applicable. PROSPERO: CRD420261339435.