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

NCT07506967 | Not Yet Recruiting

• 2 other identifiers: EC grant agreement 101190743MGAAres(2025)3881363
• Study Type: interventional
• Target: 2,420
• Locations: 5 countries
• Sites: 5

Brief 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 In the final 6 months (Phase C), the app is withdrawn to assess whether FHWs retain their improved diagnostic skills.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

Keywords

Skin-related neglected tropical diseases; Artificial Intelligence; Mobile Health; mHealth; Frontline Health Workers; Diagnostic Accuracy; Sub-Saharan Africa; Skin NTDs; Digital Health; AI Diagnostic Tool; Capacity Building; Kenya; Ethiopia; Senegal; Nigeria; Democratic Republic of the Congo

Outcome Measures

Primary Outcomes (1)

• FHW Diagnostic Accuracy Improvement (FHW-DAI)

  Percentage improvement in diagnostic accuracy of frontline health workers (FHWs) when using the SkincAIr Detection App compared to baseline performance without the app. Diagnostic accuracy is measured by comparing FHW diagnoses against the reference standard diagnosis established independently by a co-located dermatologist for each patient case. A minimum improvement of 15% (KPI 1.3) is required to demonstrate clinical utility of the app. Measured across all 3 study phases: Phase A (baseline, no app, M22-M33); Phase B (app active, M34-M39); Phase C (app withdrawn, M40-M45).

  Month 22 through Month 45

Secondary Outcomes (12)

• Early Detection Rate of Skin NTDs by FHWs (KPI 1.1)

  22 through Month 39

• Time Reduction from FHW Suspicion to Diagnostic Confirmation (KPI 1.2)

  Month 22 through Month 45

• Sensitivity of FHW Diagnosis for Skin NTDs (KPI 1.4)

  Month 22 through Month 45

• Specificity of FHW Diagnosis for Skin NTDs (KPI 1.5)

  Month 22 through Month 45

• Diagnostic Delay Reduction from First Healthcare Contact to Confirmation (KPI 3.1)

  Month 22 through Month 45

Other Outcomes (1)

• Skin NTD Image Dataset Size and Quality (KPI 2.1-2.4)

  Month 12 through Month 48

Study Arms (2)

Clinical Image Data Collection

OTHER

Dermatologists in 5 countries (Kenya, Ethiopia, Senegal, Nigeria and DRC) use the Dermatologist Dataset eCRF module of the SkincAIr Research App to capture and annotate high-quality clinical images of skin NTDs and other skin conditions during routine clinical activity (M12-M48, 36 months). Structured metadata including disease type, body site, age group, severity and geographic location are recorded alongside each image. Collected images are used exclusively to train and develop the SkincAIr AI model prior to its validation among frontline health workers. Target: \>3,500 high-quality annotated images of skin NTDs across 11 disease categories in 5 countries (KPI 2.1).

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

SkincAIr Validation Study - Frontline Health Workers

EXPERIMENTAL

50 frontline health workers (FHWs) per country (Kenya, Ethiopia, Senegal) and \~750 patients with skin complaints per country participate in a 24-month within-subjects validation study using the SkincAIr Research App. FHWs complete 3 consecutive phases: Phase A (M22-M33, 12 months): baseline data collection using standard diagnostic methods without AI support, using the FHW eCRF only; Phase B (M34-M39, 6 months): AI-assisted diagnosis using the SkincAIr Detection App embedded in the FHW eCRF, activated exclusively during this phase; Phase C (M40-M45, 6 months): AI support withdrawn to assess retention of improved diagnostic skills. Each FHW serves as their own control. A reference dermatologist independently evaluates each patient to provide the gold standard diagnosis.

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

Interventions

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

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.

Also known as: SkincAIr Detection App

Clinical Image Data Collection; SkincAIr Validation Study - Frontline Health Workers

Eligibility Criteria

• Age: 0 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Professional Role:
• o Must be working as a FHW at one of the selected health centers at the time of the validation study.
• ▪ Justification: The study aims to assess the diagnostic performance of those directly involved in primary patient care in the targeted settings.
• Willingness to Participate:
• o Willing to provide written informed consent to participate in the study.
• ▪ Justification: Ethical standards require voluntary participation with informed consent.
• Smartphone Usage:
• o Willing and able to use a smartphone during the study.
• ▪ Justification: The SkincAIr app is smartphone-based; therefore, FHWs must be willing to use and have access to such devices.
• No Specialized Dermatology Training:
• FHWs without specialised training in dermatology or extensive experience in skin disease diagnosis.
• Justification: The study aims to evaluate the app's effectiveness among generalist healthcare workers who would benefit most from diagnostic support tools.

You may not qualify if:

• \. Prior Specialised Training in dermatology:
• o FHWs with formal education or extensive experience in dermatology.
• Justification: Including specialists could skew results, as their baseline diagnostic accuracy may already be high, reducing the observable impact of the app.
• \. Refusal or Inability to Consent:
• FHWs unwilling or unable to provide written informed consent.
• Justification: Ethical compliance requires informed consent for participation. 3. Inability to Use the App: o FHWs unable to use a smartphone due to technical limitations, physical impairments, or lack of familiarity with the technology.
• Justification: Effective use of the app is essential for the intervention; inability to use it would prevent meaningful participation.
• Patients with Skin complaints Size
• ● Total Patients: \~750 patients Age Group
• ● All Age Groups:
• o Justification: Skin-NTDs affect individuals of all ages; including all age groups enhances the generalizability of the findings and assesses the app's effectiveness across the lifespan.
• Sex Distribution
• Male and Female Patients
• Justification: Both sexes are included to capture the full spectrum of the disease burden and ensure the app's diagnostic accuracy is effective regardless of sex.
• \. Presenting with Skin Complaints:

Sponsors & Collaborators

• Kenya Medical Research Institute: lead
• Universidad Politecnica de Madrid: collaborator
• FACHHOCHSCHULE ZENTRALSCHWEIZ - HOCHSCHULE LUZERN: collaborator
• SHERWOOD HEALTHCARE SENEGAL SARL: collaborator
• King's College London: collaborator
• TEACUP CONSULTING SL: collaborator
• MTU AUSTRALO ALPHA LAB: collaborator
• OMODI, AGASNA, ODIEMBO ADVOCATES LLP: collaborator
• OEUVRES HOSPITALIERES FRANCAISES DE L'ORDRE DE MALTE: collaborator
• ARMAUER HANSEN RESEARCH INSTITUTE: collaborator
• Leprosy and Tuberculosis Relief Initiative Nigeria: collaborator
• UNIVERSITE CATHOLIQUE DE BUKAVU: collaborator

Study Sites (5)

Université Catholique de Bukavu (UCB)

Bukavu, South Kivu, Democratic Republic of the Congo

Location

Armauer Hansen Research Institute (AHRI)

Addis Ababa, Addis Ababa, Ethiopia

Location

Kenya Medical Research Institute (KEMRI)

Kisumu, Nyanza, Kenya

Location

Leprosy and Tuberculosis Relief Initiative Nigeria (LTR)

Jos, Plateau State, Nigeria

Location

Centre Hospitalier de l'Ordre de Malte (CHOM)

Dakar, Dakar, Senegal

Location

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Related Links

• The WHO Skin NTD mobile application - a paradigm shift in leprosy diagnosis through Artificial Intelligence?
• The Burden of Neglected Tropical Diseases in Sub-Saharan Africa
• Kenya National Tuberculosis Leprosy and Lung Disease Program Annual Report, 2014
• Awareness, Attitudes, Perceptions and Practices of Scabies Infestation among Caregivers of Children under 5 Years of Age in Villages of Kwale County, Kenya
• Mbogori M. 2014 (Thesis)
• Wayne W. Daniel, Chad L. Cross, Biostatistics: A Foundation for Analysis in the Health Sciences, 2013, page 191
• WHO. Promoting the integrated approach to skin-related neglected tropical diseases
• WHO 2023. Report of the first WHO global meeting on skin-related neglected tropical diseases
• WHO 2016. Monitoring and Evaluating Digital Health Interventions: a practical guide to conducting research and assessment

MeSH Terms

Conditions

Skin and Connective Tissue Diseases; Neglected Diseases; Leprosy; Buruli Ulcer; Leishmaniasis, Cutaneous; Scabies; Mycetoma; Elephantiasis, Filarial; Onchocerciasis; Tungiasis; Yaws; Elephantiasis

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Mycobacterium Infections, Nontuberculous; Mycobacterium Infections; Actinomycetales Infections; Gram-Positive Bacterial Infections; Bacterial Infections; Bacterial Infections and Mycoses; Infections; Skin Ulcer; Skin Diseases; Leishmaniasis; Euglenozoa Infections; Protozoan Infections; Parasitic Diseases; Skin Diseases, Parasitic; Vector Borne Diseases; Skin Diseases, Infectious; Mite Infestations; Ectoparasitic Infestations; Nocardia Infections; Skin Diseases, Bacterial; Dermatomycoses; Mycoses; Filariasis; Spirurida Infections; Secernentea Infections; Nematode Infections; Helminthiasis; Mosquito-Borne Diseases; Lymphedema; Lymphatic Diseases; Hemic and Lymphatic Diseases; Flea Infestations; Treponemal Infections; Spirochaetales Infections; Gram-Negative Bacterial Infections

Study Officials

• Gustavo H Penaloza, PhD

  Polytechnic University of Madrid (UPM)

  STUDY CHAIR

• Carla Rodríguez Cuesta, MEng

  SHERWOOD HEALTHCARE SENEGAL SARL

  STUDY DIRECTOR

Central Study Contacts

Maurice R Odiere, PhD

CONTACT

254721845777; Modiere@kemri.go.ke

Ruth M Nyangacha, PhD

CONTACT

254728710650; RNyangacha@kemri.go.ke

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Research Scientist

Model Details: The SkincAIr Research App is evaluated across two non-randomized arms: (1) Clinical Image Data Collection - dermatologists in 5 countries use the Dermatologist Dataset eCRF to collect and annotate \>3,500 skin NTD images over 36 months; (2) Validation Study - 150 FHWs (50 per country, Kenya, Ethiopia, Senegal) serve as their own control across 3 consecutive phases: Phase A (12 months baseline, no AI); Phase B (6 months, AI app activated); Phase C (6 months, AI withdrawn to assess skill retention). \~750 patients enrolled per country.

Study Record Dates

• First Submitted: March 27, 2026
• First Posted: April 2, 2026
• Study Start: May 1, 2026
• Primary Completion (Estimated): February 28, 2029
• Study Completion (Estimated): May 31, 2030
• Last Updated: April 2, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: Beginning 12 months after study completion (M60), available for 10 years
• Access Criteria: Researchers must submit a data access request. Access granted subject to signing a data sharing agreement compliant with GDPR and national data protection laws of participating countries.

Locations

SE (1); NG (1); DE (1); ET (1); KE (1)