Early Detection and AI-Based Management of Skin-Related Neglected Tropical Diseases in Sub-Saharan Africa by Frontline Health Workers

Summary

Skin-related Neglected Tropical Diseases (Skin NTDs) affect about 1.8 billion people worldwide, particularly in poor and rural communities where healthcare access is limited. Many people rely on frontline health workers (FHWs) for treatment, but these workers often lack specialized training in skin diseases, making diagnosis difficult. To address this challenge, the SkincAIr project is testing whether a mobile app powered by artificial intelligence (AI) can help FHWs improve their ability to detect Skin NTDs. The study will be conducted in two arms. In the first clinical image data collection arm (36 months), dermatologists in 5 countries (Kenya, Ethiopia, Senegal, Democratic Republic of Congo and Nigeria) will collect images of skin NTD and other skin conditions that will be used for development and training of the AI model within the SkincAIr app before it is tested among FHWs. The second validation study arm will take place in 3 countries (Kenya, Ethiopia and Senegal), and will involve 50 FHWs and around 750 patients in each country over 24 months. During the first 12 months (Phase A), FHWs will diagnose patients using standard methods without the app, establishing baseline performance on key indicators including diagnostic accuracy, time to diagnosis, referral patterns, and cost implications of improved primary-level diagnosis. For the following 6 months (Phase B), FHWs will use the SkincAIr app with AI functionality activated to support diagnosis and enable real-time geolocated disease mapping and hotspot identification. In the final 6 months (Phase C), the app is withdrawn to assess whether FHWs retain their improved diagnostic skills. We will summarize the results using simple numbers and charts to show how often things happen and what the average results look like. Researchers will evaluate how well the app improves diagnosis by FHWs and whether FHWs retain their improved skills even after AI support is removed, by comparing their results with those of a skin specialist (dermatologist). Interviews and group discussions will be recorded, written down, organized into key ideas, and carefully reviewed using a computer program to understand the main themes. Study findings will be shared with National Ministries of Health, presented at local and international conferences, and reported to relevant institutional and regulatory authorities. If successful, this AI tool could boost early detection of skin diseases, enhance disease tracking, and improve healthcare in underserved areas.

Conditions

• Skin and Connective Tissue Diseases
• Neglected Tropical Diseases
• Leprosy
• Buruli Ulcer
• Cutaneous Leishmaniasis
• Scabies
• Mycetoma
• Lymphatic Filariasis
• Onchocerciasis
• Tungiasis
• Post Kala-Azar Dermal Leishmaniasis
• Yaws
• Podoconiosis

Interventions

DEVICE

A mobile app with AI functionality for diagnosing skin-related NTDs

The SkincAIr Research App is a unified mobile platform (Android, offline-capable) containing three role-specific modules: (1) Dermatologist Dataset eCRF - used by dermatologists in 5 countries (M12-M48) to capture and annotate high-quality clinical images of skin NTDs for AI model development; (2) FHW eCRF - used by frontline health workers (FHWs) in 3 countries (M22-M45) to document clinical assessments with and without AI support; (3) SkincAIr Detection App - an AI-powered diagnostic decision-support feature embedded within the FHW eCRF, activated exclusively during Phase B (6 months), providing image-based diagnostic suggestions to assist FHWs in identifying skin NTDs. The SkincAIr Detection App is the primary intervention under validation. If proven effective, it is intended for adoption by National Ministries of Health, integration into national Health Information Systems (DHIS2), and scale-up across sub-Saharan Africa.

Sponsors & Collaborators

• Universidad Politecnica de Madrid

  collaborator OTHER

• FACHHOCHSCHULE ZENTRALSCHWEIZ - HOCHSCHULE LUZERN

  collaborator UNKNOWN

• SHERWOOD HEALTHCARE SENEGAL SARL

  collaborator UNKNOWN

• King's College London

  collaborator OTHER

• TEACUP CONSULTING SL

  collaborator UNKNOWN

• MTU AUSTRALO ALPHA LAB

  collaborator UNKNOWN

• OMODI, AGASNA, ODIEMBO ADVOCATES LLP

  collaborator UNKNOWN

• OEUVRES HOSPITALIERES FRANCAISES DE L'ORDRE DE MALTE

  collaborator UNKNOWN

• ARMAUER HANSEN RESEARCH INSTITUTE

  collaborator UNKNOWN

• Leprosy and Tuberculosis Relief Initiative Nigeria

  collaborator UNKNOWN

• UNIVERSITE CATHOLIQUE DE BUKAVU

  collaborator UNKNOWN

• Kenya Medical Research Institute

  lead OTHER

Principal Investigators

• Gustavo H Penaloza, PhD · Polytechnic University of Madrid (UPM)

• Carla Rodríguez Cuesta, MEng · SHERWOOD HEALTHCARE SENEGAL SARL

Study Design

Allocation

NON_RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

NONE

Model

PARALLEL

Eligibility

Min Age

0 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-05-01

Primary Completion

2029-02-28

Completion

2030-05-31

Countries

• Democratic Republic of the Congo
• Ethiopia
• Kenya
• Nigeria
• Senegal

Study Locations