[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"podoconiosis\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:podoconiosis":39},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":40,"overallStatus":57,"whyStopped":4,"lastUpdateSubmitDate":58,"lastUpdatePostDateStruct":59,"startDateStruct":62,"completionDateStruct":64,"leadSponsor":66,"locationsCount":69},"100631921","early-detection-and-ai-based-management-of-skin-related-neglected-tropical-diseases-in-sub-saharan-africa-by-frontline-health-workers-100631921",false,"NCT07506967","Early Detection and AI-Based Management of Skin-Related Neglected Tropical Diseases in Sub-Saharan Africa by Frontline Health Workers","Early Detection and Management of SKIN-related negleCted Tropical Diseases Using Artificial Intelligence in Sub-saharan afRica (SkincAIr)","SkincAIr","1. Frontline Health Workers (FHWs) Age Group\n\n   * Age Range: 18 years and above o Justification: FHWs must be adults, legally eligible to provide healthcare services and consent to participate in the study Sex Distribution\n   * Male and Female FHWs o Justification: Both male and female FHWs will be included to reflect the actual workforce distribution and to ensure generalizability of the results across genders.\n\n   Inclusion criteria for FHWs:\n   1. Professional Role:\n\n      o Must be working as a FHW at one of the selected health centers at the time of the validation study.\n\n      ▪ Justification: The study aims to assess the diagnostic performance of those directly involved in primary patient care in the targeted settings.\n   2. Willingness to Participate:\n\n      o Willing to provide written informed consent to participate in the study.\n\n      ▪ Justification: Ethical standards require voluntary participation with informed consent.\n   3. Smartphone Usage:\n\n      o Willing and able to use a smartphone during the study.\n\n      ▪ Justification: The SkincAIr app is smartphone-based; therefore, FHWs must be willing to use and have access to such devices.\n   4. No Specialized Dermatology Training:\n\n      * FHWs without specialised training in dermatology or extensive experience in skin disease diagnosis.\n\n        * Justification: The study aims to evaluate the app's effectiveness among generalist healthcare workers who would benefit most from diagnostic support tools.\n\n   Exclusion criteria for FHWs:\n\n   1\\. Prior Specialised Training in dermatology:\n\n   o FHWs with formal education or extensive experience in dermatology.\n   * Justification: Including specialists could skew results, as their baseline diagnostic accuracy may already be high, reducing the observable impact of the app.\n\n     2\\. Refusal or Inability to Consent:\n     * FHWs unwilling or unable to provide written informed consent.\n   * 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.\n   * Justification: Effective use of the app is essential for the intervention; inability to use it would prevent meaningful participation.\n2. Patients with Skin complaints Size\n\n   ● Total Patients: \\~750 patients Age Group\n\n   ● All Age Groups:\n\n   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.\n\n   Sex Distribution\n   * Male and Female Patients\n   * 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.\n\n   Inclusion Criteria for Patients with Skin complaints:\n\n   1\\. Presenting with Skin Complaints:\n\n   o Patients presenting to participating health centres with symptoms suggestive of skin-NTDs (e.g., visible skin lesions, nodules, ulcers) but have not been diagnosed by a specialist for that specific skin condition.\n   * Justification: The study aims to evaluate the app's effectiveness in real-world conditions, including all patients with potential skin-NTDs 2. Willingness to Participate: o Patients (or guardians, in the case of minors) willing to provide written informed consent for participation.\n   * Justification: Ethical standards require informed consent from patients or their legal guardians.\n\n     3\\. Ability to Comply with Study Procedures:\n\n     o Patients are able to follow study instructions and attend necessary follow-up appointments.\n   * Justification: Ensures complete data collection and accurate assessment of outcomes.\n\n     4\\. Patients with Co-morbid conditions:\n   * Justification: Immunosuppression that occurs in some comorbid conditions e.g. HIV\u002FAIDS or severe malnutrition can reveal the Skin disease and can affect both the clinical progression and even severity of the Skin NTD. This also includes patients with multiple skin-NTDs.\n\n   Exclusion Criteria for Patients with Skin complaints:\n   1. Refusal or Inability to Consent:\n\n      o Patients (or guardians) unwilling or unable to provide written informed consent.\n\n      ▪ Justification: Ethical compliance requires informed consent for participation.\n   2. Non-Skin-Related Complaints:\n\n      o Patients presenting with complaints unrelated to skin conditions.\n\n      ▪ Justification: The study focuses on skin-NTDs; including unrelated cases would not contribute to the study objectives.\n   3. Previous Participation in the Study:\n\n      o Patients who have already participated in the study.\n\n      ▪ Justification: To avoid duplicate data and potential bias in outcomes.\n\n      Additional Considerations:\n\n      Diversity and Representation ● Geographical Diversity:\n\n      o Including health centres from different regions within each country ensures that the findings are representative of various settings (urban, peri-urban, rural).\n\n      ● Cultural and Socioeconomic Factors:\n\n      o The study acknowledges that cultural beliefs and socioeconomic status may influence healthcare-seeking behaviour and disease presentation. By including a diverse patient population, the study aims to capture these variations.\n\n      Ethical Justification ● Inclusivity:\n      * Including all age groups and both sexes aligns with ethical principles of justice and fairness, ensuring that the benefits of the research are accessible to all segments of the population.\n\n        * Vulnerable Populations:\n      * While including minors and potentially vulnerable adults, the study will implement additional safeguards to protect their rights and well-being, following ethical guidelines and obtaining consent from guardians when necessary.","ALL","0 Years",{"count":20,"type":21},2420,"ESTIMATED","INTERVENTIONAL",[24],"NA","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.",[27,28,29,30,31,32,33,34,35,36,37,38,39],"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",[41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56],"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","NOT_YET_RECRUITING","2026-03-27",{"date":60,"type":61},"2026-04-02","ACTUAL",{"date":63,"type":21},"2026-05-01",{"date":65,"type":21},"2030-05-31",{"name":67,"class":68},"Kenya Medical Research Institute","OTHER",5]