Trials / Unknown
UnknownNCT06055530
Evaluation of an AI-DP for STH Deworming Programs: a Study Protocol
A Comprehensive Evaluation of an Artificial Intelligence Based Digital Pathology to Monitor Large-scale Deworming Programs Against Soil-transmitted Helminths
- Status
- Unknown
- Phase
- —
- Study type
- Observational
- Enrollment
- 1,100 (estimated)
- Sponsor
- Enaiblers AB · Industry
- Sex
- All
- Age
- 5 Years – 14 Years
- Healthy volunteers
- Accepted
Summary
The goal of this observational study is to test a new AI diagnostic tool for detection, specification and quantification of parasitic infections (Ascaris, Trichuris, hookworm and S. Mansoni) in School aged children in Ethiopia and Uganda. The main questions it aims to answer are: * Diagnostic Performance of the AI tool and compare to traditional manual microscopy * Repeatability and reproducibility of the AI tool and compare to traditional manual microscopy * Time-to-result for the AI tool * Cost efficiency for the AI tool and traditional manual microscopy to inform programmatic decisions. * Usability of the AI tool Participants will be asked to provide a stool sample for examination by the AI tool and traditional manual microscopy. Participants with a positive test result will receive the proper treatment (Deworming drug).
Detailed description
Manual screening of a Kato-Katz (KK) thick stool smear remains the current standard to monitor the impact of large-scale deworming programs against soil-transmitted helminths (STHs). To improve this diagnostic standard, the investigators recently designed an artificial intelligence based digital pathology system (AI-DP) for digital image capture and analysis of KK thick smears. Preliminary results of its diagnostic performance are encouraging, and a comprehensive evaluation of the AI-DP as a cost-efficient end-to-end diagnostic to inform STHs control programs against the target product profiles (TPP) of the World Health Organisation (WHO) is the next step for validation. The study protocol describes a comprehensive evaluation of the AI-DP based on its (i) diagnostic performance, (ii) repeatability/reproducibility, (iii) time-to-result, (iv) cost-efficiency to inform large-scale deworming programs and (v) usability in both laboratory and field settings. For each of these five attributes, the investigators designed separate experiments with sufficient power to verify the non-inferiority of the AI-DP (KK2.0) over the manual screening of the KK smears (KK1.0). These experiments will be conducted in two STH endemic countries with national deworming programs (Ethiopia and Uganda), focusing on school-age children (SAC) only. Participants will be asked to provide a stool sample for examination by the AI tool and traditional manual microscopy. Participants with a positive test result will receive the proper treatment (Deworming drug). This comprehensive and well-designed study and accompanying protocols will provide the necessary data to make an evidence-based decision on whether the AI-DP is indeed performant and a cost-efficient end-to-end diagnostic to inform large-scale deworming programs against STHs. Following the protocolized collection of high-quality data the investigators will seek approval by WHO. Through the dissemination of the methodology and statistics, the investigators hope to support additional developments in AI-DP technologies for other neglected tropical diseases in resource-limited settings.
Conditions
Interventions
| Type | Name | Description |
|---|---|---|
| DIAGNOSTIC_TEST | Artificial Intelligence Digital Pathology | School aged children will be asked to leave a stool sample. The samples will be prepared with the Kato-Katz method and scanned and processed by an artificial intelligence digital pathology system to determine the infection level of soil transmitted helminths and schistosomiasis. The samples will also be analyzed by a human microscopist for comparison. |
Timeline
- Start date
- 2023-10-01
- Primary completion
- 2023-12-01
- Completion
- 2024-07-01
- First posted
- 2023-09-26
- Last updated
- 2023-10-02
Source: ClinicalTrials.gov record NCT06055530. Inclusion in this directory is not an endorsement.