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

ABSTRACT:

Skin-related Neglected Tropical Diseases (Skin NTDs) pose a significant public health challenge, affecting 1.8 billion people globally. Skin NTDs significantly affect marginalized communities due to several factors, such as lack of trained healthcare staff and diagnostic tools. Currently, due to a scarcity of dermatologists, the majority of the rural population with skin diseases is served by frontline health workers (FHWs) with limited dermatological knowledge. The low prevalence of skin NTDs further confounds their diagnosis and recognition by FHWs. Novel innovative approaches are therefore needed to build capacity and improve diagnosis for skin NTDs. Mobile health (mHealth) interventions, particularly those incorporating artificial intelligence (AI), offer a promising solution to enhance the diagnostic capabilities of FHWs. The SkincAIr project aims to evaluate whether introducing a mobile app with AI functionality can improve the diagnostic accuracy (sensitivity and specificity) of FHWs in detecting skin NTDs, and to determine their retention of improved diagnostic skills after the AI assistance is removed, indicating potential capacity building and sustained improvement in healthcare delivery. In the 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 (Image data collection phase) that will be used for training and development of the SkincAIr app, before it is evaluated among FHWs during the 24-month validation study. The validation study arm for the app will involve a within-subjects longitudinal design which will enroll 50 FHWs and will recruit about 750 patients with skin complaints from areas with high burden of skin NTDs in each of 3 countries (Kenya, Ethiopia, Senegal) over a 24-month validation study period. Data will be analyzed using R and Python, with descriptive statistics (frequency, central tendency, and dispersion) summarized in tables and charts. Diagnostic accuracy of FHWs before and after app introduction will be evaluated using sensitivity, specificity, predictive values, and percent agreement with a dermatologist. Qualitative data from interviews and FGDs will be audio-recorded, transcribed, coded, and thematically analyzed using Atlas.ti Version 7. Study findings will be shared with National Ministries of Health, presented at local and international conferences, and reported to IRBs and regulatory authorities. It is envisaged that the app will improve the diagnostic accuracy of FHWs in early detection of skin NTDs and will facilitate real-time epidemiological surveillance, contributing to improved disease mapping and hotspot identification.

INTRODUCTION/BACKGROUND:

Skin-related Neglected Tropical Diseases (MDPI, 2019) (skin NTDs) such as leprosy, Buruli ulcer, yaws (endemic treponematosis), cutaneous leishmaniasis, chromoblastomycosis, mycetoma, scabies, tungiasis, Post Kala-azar Dermal Leishmaniasis (PKDL), lymphatic filariasis, onchocerciasis, podoconiosis and sporotrichosis pose a significant public health challenge, affecting 1.8 billion people globally at any given moment (WHO, 2023). Particularly in sub-Saharan Africa (SSA), these diseases are highly prevalent and are linked to substantial health inequities, predominantly impacting marginalized communities (Kariuki et al. 2023). Kenya for instance has a significant burden of Skin NTDs, including Lymphatic filariasis at the Coastal region (Njenga et al. 2017; Ofire et al. 2025), Mycetoma in Turkana (Colom et al. 2023), Leishmaniasis in Rift Valley and Eastern parts (Baringo, Naivasha/Gilgil, Laikipia, Samburu, Nakuru, Meru, West Pokot, Elgeyo Marakwet, Isiolo, Nyandarua, and Marsabit) (Ngere et al. 2020; van Dijk et al. 2024), Tungiasis (Elson et al. 2019; Nyangacha et al. 2019) and Scabies (Schmeller and Dzikus, 2001; Mbogori 2014; Macharia et al. 2024) that have a wide distribution across the country and some pockets of cases of Leprosy in Kwale, Kilifi, Kisumu, Siaya, Homabay and Busia counties (Kenya NTLLD Program Annual Report, 2014; Wangara et al. 2019).

The prevalence of these diseases is exacerbated by factors such as poverty and a lack of adequate healthcare resources, notably insufficiently trained staff for effective management of skin NTDs (Ochola et al. 2021). The stigma surrounding skin NTDs, entrenched in societal and economic contexts, leads to isolation and discrimination, discouraging diagnosis or treatment, which in turn exacerbates disease spread and complicates control and elimination efforts, creating a self-perpetuating cycle of challenges. A high proportion of NTDs have major skin manifestations. Therefore, examination of the skin serves as an opportunity to identify multiple NTDs in a single intervention. The integrative approach, recommended by the World Health Organization (WHO) (https://www.who.int/activities/promoting-the-integrated-approach-to-skin-related-neglected-tropical-diseases), results in enhanced case detection and increased efficiency through sharing of resources and expanded programme coverage. However, there is a major barrier to the integration of skin NTD interventions: the lack of dermatologists (Schmid-Grendelmeier et al. 2019) (one dermatologist for 1-2 million inhabitants) and adequately trained healthcare staff. Currently, the majority of the rural population with skin diseases is served by frontline health workers (FHWs) with limited dermatological knowledge (Mieras et al. 2018). This challenge is further confounded by the low prevalence of skin-related NTDs, which makes them difficult to be recognized by FHWs (Mieras et al. 2018; Hotez et al. 2009). As skin NTDs rely mostly on clinical diagnosis, lack of adequate training of FHWs jeopardizes disease control programs and the attainment of the overall 2021-2030 WHO NTD roadmap target to reduce morbidity, disability and the psychosocial impact of skin NTDs by 2030. Novel innovative approaches are needed to build capacity and improve diagnosis for skin NTDs to realize the 2030 goals. In this regard, Artificial Intelligence (AI) now provides an unprecedented opportunity to use advances in medical imaging applied to the skin to tackle current barriers in the diagnosis and management of skin NTDs in SSA.

Existing AI models in dermatology often focus on diseases prevalent in developed countries and rely on homogeneous datasets, leading to models that do not generalize well to diverse populations. They typically use internal validation methods, which are insufficient for real-world deployment where models encounter varied data sources (Daneshjou et al. 2021). Whereas AI-powered algorithms have demonstrated diagnostic accuracy similar to expert clinicians in high-resource settings (Salinas et al. 2024), very few studies have explored the use of AI-powered apps for diagnosing skin NTDs in low-resource settings in SSA. This is due to the novelty of the technology which contributes to the scarcity of such research. The WHO NTD-led Global Initiative discusses progress, challenges, gaps and solutions in developing and implementing artificial augmented intelligence-based apps as a capacity building and monitoring tool for skin NTDs and selected common skin conditions in resource-limited settings. Recently, the WHO incorporated two online AI algorithms that intend to classify 12 skin NTDS and 24 common skin conditions (Quilter et al. 2024) into the WHO Skin NTDs app (mainly built as a repository of educational resources and training materials, which adhere to WHO guidelines). While the app aims to improve capacity building through AI, its "real-world" impact on disease management is still not available and upcoming studies will determine its utility. The performance of AI-algorithms is also limited by the availability of images datasets. In the AI4Leprosy study conducted in 2022 at the Brazil leprosy national referral center, although the convolutional neural networks (CNN)-based AI algorithm could contribute to the diagnosis of leprosy with high classification accuracy (90%) (AI4Leprosy), to the best of our knowledge, this has not been validated in a low-resource setting with lack of highly specialized staff. Further afield, while the technology's performance is increasingly being validated in dermatological conditions such as melanoma and other skin cancers (Patel et al. 2023), the direct evidence for its efficacy in diagnosing skin-NTDs remains limited. Overall, while these studies and initiatives demonstrate the potential of AI-powered diagnostic tools, evidence specific to their use for skin NTDs in low-resource settings is still emerging.

Our proposed validation study aims to address the aforementioned gaps by evaluating the diagnostic accuracy of an AI-powered app in diagnosing skin NTDs in Kenya, Ethiopia and Senegal, providing critical insights into its practical utility and impact on clinical practice in these settings. The intervention is the SkincAIr Research App, a unified mobile platform containing three role-specific modules: a Dermatologist Dataset eCRF for structured image collection by dermatologists across 5 countries; an FHW eCRF for clinical data collection and case documentation across all 3 study phases; and the SkincAIr Detection App - an AI-powered diagnostic decision-support feature embedded within the FHW eCRF, activated exclusively during Phase B (6 months), and withdrawn during Phase C to assess retention of improved diagnostic skills. The study measures: (SO1) diagnostic performance of FHWs with and without the app, including early detection rate, diagnostic accuracy, sensitivity and specificity against a dermatologist reference standard; (SO2) dataset quality including number, geographic diversity and image quality; (SO3) reduction in diagnostic delay; (SO4) epidemiological surveillance indicators including DHIS2 integration, case confirmation ratio and hotspot identification; (SO5) FHW knowledge gain and user satisfaction. Cost-effectiveness is assessed through primary vs secondary care cost comparison and ICER calculation.

This clinical study will provide vital data to assess the practical utility of AI in improving diagnostic accuracy and speed among non-specialist health workers. By leveraging AI and mobile technology, we can equip FHWs in low-resource settings with tools to quickly detect and manage skin NTDs.

Justification for the Study:

Neglected tropical diseases (NTDs) affect over one billion people globally, with skin NTDs such as leprosy, cutaneous leishmaniasis, and onchocerciasis contributing significantly to morbidity, disability, and stigma in affected populations. Early detection and treatment are crucial to prevent complications, reduce transmission, and improve patient outcomes. In resource-limited settings like Kenya, Ethiopia, Senegal, Nigeria and the Democratic Republic of the Congo, FHWs are often the first point of contact for patients with skin conditions. However, FHWs typically lack specialized training in dermatology, leading to misdiagnosis or delayed diagnosis of skin NTDs. Mobile health (mHealth) interventions, particularly those incorporating AI, offer a promising solution to enhance the diagnostic capabilities of FHWs. The SkincAIr project aims to evaluate whether introducing a mobile app with AI functionality can improve the diagnostic accuracy of FHWs in detecting skin NTDs. Additionally, the study seeks to determine if FHWs retain improved diagnostic skills after the AI assistance is removed, indicating potential capacity building and sustained improvement in healthcare delivery. Our project transcends the twin limitations of lack of specialized training in dermatology and availability of images datasets by collecting real clinical data from multiple geographic locations within Low-and Middle-Income Countries (LMICs), ensuring that our models are trained and externally vali

• Early Detection Rate of Skin NTDs by FHWs (KPI 1.1) [Time frame: 22 through Month 39]
• Time Reduction from FHW Suspicion to Diagnostic Confirmation (KPI 1.2) [Time frame: Month 22 through Month 45]
• Sensitivity of FHW Diagnosis for Skin NTDs (KPI 1.4) [Time frame: Month 22 through Month 45]
• Specificity of FHW Diagnosis for Skin NTDs (KPI 1.5) [Time frame: Month 22 through Month 45]
• Diagnostic Delay Reduction from First Healthcare Contact to Confirmation (KPI 3.1) [Time frame: Month 22 through Month 45]
• FHW Diagnostic Knowledge Gain and Retention (KPI 5.1) [Time frame: Month 33 through Month 45]
• User Education Satisfaction Index (UESI) (KPI 5.2) [Time frame: Month 34 through Month 39]
• Epidemiological Surveillance - Subjects Integrated into DHIS2 (KPI 4.1) [Time frame: Month 22 through Month 60]
• Case Confirmation Ratio (CCR) (KPI 4.2) [Time frame: Month 22 through Month 45]
• Response Time to Hotspot Identification (KPI 4.3) [Time frame: Month 22 through Month 60]
• New Skin NTD Hotspots Identified (KPI 4.4) [Time frame: Month 12 through Month 60]
• Cost-Effectiveness of AI-Assisted Primary Diagnosis [Time frame: Month 22 through Month 45]