Evaluation of an AI-DP for STH Deworming Programs: a Study Protocol

NCT ID: NCT06055530

Last Updated: 2023-10-02

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

1100 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-10-31

Study Completion Date

2024-07-31

Brief Summary

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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

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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

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Soil Transmitted Helminths Schistosomiasis Mansoni

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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School aged children in Ethiopia

A number of school aged children in Ethiopia from 5-7 different schools in the Jimma region.

Artificial Intelligence Digital Pathology

Intervention Type DIAGNOSTIC_TEST

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.

School aged children in Uganda

A number of school aged children from Uganda. Children from 5-7 different schools will be in the group.

Artificial Intelligence Digital Pathology

Intervention Type DIAGNOSTIC_TEST

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.

Interventions

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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.

Intervention Type DIAGNOSTIC_TEST

Other Intervention Names

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Kato-Katz 2.0

Eligibility Criteria

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Inclusion Criteria

* Subject, male or female, is 5-14 years of age
* Parent(s)/guardian(s) of subject signed an informed consent document indicating that they understand the purpose and procedures required for the study and that they are willing to have their child participate in the study
* Subject of ≥6 (Ethiopia) /8 (Uganda) years old has assented to participate in the study\*
* Subject of ≥12 years old has signed an informed consent document indicating that they understand the purpose of the study and procedures required for the study, and are willing to participate in the study (Ethiopia only)\*
* Subject has provided a stool sample of minimum 5 grams

Exclusion Criteria

* Subject has active diarrhoea (defined as the passage of 3 or more loose or liquid stools per day) at baseline or follow-up.
* Subject is experiencing a severe concurrent medical condition or has an acute medical condition
* Subject has received anthelmintic treatment within 90 days prior to the start of the study
Minimum Eligible Age

5 Years

Maximum Eligible Age

14 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Jimma University

OTHER

Sponsor Role collaborator

Ministry of Health, Uganda

OTHER_GOV

Sponsor Role collaborator

University Ghent

OTHER

Sponsor Role collaborator

Enaiblers AB

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Bruno Levecke, PhD

Role: STUDY_DIRECTOR

University Ghent

Zeleke Mekonnen, PhD

Role: PRINCIPAL_INVESTIGATOR

Jimma University

Narcis Kabatereine, PhD

Role: PRINCIPAL_INVESTIGATOR

Ministry of Health, Uganda

Central Contacts

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Peter Dahlberg, MSc

Role: CONTACT

+46735195218

Peter Ward, PhD Student

Role: CONTACT

+46702595977

References

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Ward PK, Roose S, Ayana M, Broadfield LA, Dahlberg P, Kabatereine N, Kazienga A, Mekonnen Z, Nabatte B, Stuyver L, Velde FV, Hoecke SV, Levecke B. A comprehensive evaluation of an artificial intelligence based digital pathology to monitor large-scale deworming programs against soil-transmitted helminths: A study protocol. PLoS One. 2024 Oct 28;19(10):e0309816. doi: 10.1371/journal.pone.0309816. eCollection 2024.

Reference Type DERIVED
PMID: 39466830 (View on PubMed)

Related Links

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Other Identifiers

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76906491

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

EN-2023-CT001

Identifier Type: -

Identifier Source: org_study_id

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