Diagnostic performance of digital and clinical approaches for detection of podoconiosis by community health workers in Rwanda: a comparison with dermatologist diagnosis

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Introduction Detection of podoconiosis is essential to prevent disability, but diagnostic challenges mean it often goes unnoticed in Rwanda, leading to irreversible harm and social stigma. Methods This study evaluates the diagnostic performance of the WHO Skin-NTD mobile application and a clinical algorithm implemented by trained community health workers (CHWs) in detecting podoconiosis in endemic communities comparing them with dermatological reports as the clinical reference standard method. Results Both the WHO Skin-NTD mobile application and the clinical algorithm demonstrated high diagnostic performance for podoconiosis, with different proportion classified positive estimates (66.3% and 71.4% respectively). The mobile application showed a sensitivity of 89.8% (83.4%–94.3%) and specificity of 88.1% (77.1%–95.1%), while the clinical algorithm demonstrated a sensitivity of 96.4% (91.7%–98.8%) and specificity of 86.4% (75.0%–94.0%). Overall diagnostic accuracy was high for both approaches, with Sensitivity + Specificity/2of 0.89 (0.84–0.94) for the mobile application and 0.91 (0.87–0.96) for the clinical algorithm. Conclusion Both the WHO Skin-NTD mobile application and the clinical algorithm demonstrated high diagnostic performance for detection of podoconiosis by CHWs in Rwanda. However, their use should be guided by context-specific resource availability rather than simultaneous deployment. The clinical algorithm is particularly suitable for low-resource and offline settings, while the mobile application offers added value in settings with digital infrastructure through standardized decision support and enhanced data management. A flexible, context-dependent approach to implementation may optimize case detection and support podoconiosis control efforts in endemic settings.

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