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Background <p>Neglected 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.</p> Aim <p>To systematically evaluate the diagnostic performance and clinical utility of image-based machine learning models for NTD diagnosis in LMICs.</p> Methods <p>This 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.</p> Results <p>Eight 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.</p> Conclusion <p>Image-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.</p> Clinical trial number <p>Not applicable.</p> PROSPERO <p>CRD420261339435.</p>

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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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月15日

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  • David Chinaecherem Innocent,
  • Precious Ebube Anyakorah,
  • Rejoicing Chijindum Innocent,
  • Ikechukwu Nosike Simplicius Dozie,
  • Uchechukwu Madukaku Chukwuocha,
  • Chiagoziem Ogazirilem Emerole

摘要

Background

Neglected 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.

Aim

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

Methods

This 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.

Results

Eight 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.

Conclusion

Image-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.