Potential of the World Health Organization’s Skin NTDs App to Support and Improve the Detection of Skin-Related Neglected Tropical Diseases: Protocol for a Performance Evaluation and Feasibility Study in Senegal

Background

The World Health Organization (WHO) roadmap aims to control, eliminate, or eradicate neglected tropical diseases (NTDs) by promoting innovation in prevention, diagnosis, and treatment. In this context, mobile health (mHealth) tools could play an important role in improving health care across the globe, including for skin-related NTDs. One such tool is the WHO Skin NTDs App (currently available in its beta version), which utilizes artificial intelligence (AI) algorithms to classify skin lesion images and offers diagnostic suggestions and management information to bolster early detection at primary care levels. However, to harness the full potential of this and similar mHealth tools, additional insights into their diagnostic performance and potential implementation avenues in settings with limited access to trained dermatologists are essential.

Objective

The objective of our mixed methods study is to test the functionality, operability, and potential of the AI-supported diagnostic component of the WHO Skin NTDs App (beta version) to support the detection of skin NTDs and common skin conditions in Senegal.

Methods

We are conducting a diagnostic accuracy study combined with a qualitative preimplementation usability exploration. For the quantitative component, we will collect and analyze approximately 800 skin lesion images from patients presenting to the dermatology unit at the Thiès regional hospital in Senegal. Each lesion will be independently assessed by the AI-based WHO Skin NTDs App and by a dermatologist who will provide a diagnosis serving as the reference standard. Performance metrics, including accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic curve, will be calculated for each diagnostic category to evaluate the app’s ability to detect skin-related NTDs. In parallel, we will conduct semistructured in-depth interviews with a purposive sample of 70-80 stakeholders, including policymakers, health care workers, community leaders, dermatologists, and members of leprosy-affected communities. Interviews will explore perceptions of the app’s usability, acceptability, and potential barriers and facilitators to its adoption within Senegal’s health system. Thematic analysis will be used to interpret qualitative data. Findings will help inform the design of an app-based intervention to be piloted in future community-level studies.

Results

We expect the results to provide detailed insights into the feasibility and potential of the WHO Skin NTDs App to support and improve the detection of skin NTDs and common skin conditions at the community level in Senegal. We started data collection in August 2024, with the first results expected to be available in 2025.

Conclusions

Our study will assess the performance and potential use of the WHO Skin NTDs App to detect skin NTDs and common skin conditions in Senegal, outlining its potential role in supporting early diagnoses and enhancing public health responses.