NCT06055530

Brief 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).

Trial Health

35
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
1,100

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Oct 2023

Shorter than P25 for all trials

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

September 15, 2023

Completed
11 days until next milestone

First Posted

Study publicly available on registry

September 26, 2023

Completed
5 days until next milestone

Study Start

First participant enrolled

October 1, 2023

Completed
2 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 1, 2023

Completed
7 months until next milestone

Study Completion

Last participant's last visit for all outcomes

July 1, 2024

Completed
Last Updated

October 2, 2023

Status Verified

September 1, 2023

Enrollment Period

2 months

First QC Date

September 15, 2023

Last Update Submit

September 29, 2023

Conditions

Soil Transmitted HelminthsSchistosomiasis Mansoni

Keywords

Artificial Intelligence Digital PathologyDiagnostic PerformanceUsabilityRepeatabilityCost Efficiency

Outcome Measures

Primary Outcomes (7)

  • Diagnostic performance, P1.1-2

    the clinical sensitivity of Kato-Katz 2.0 (KK2.0) and Kato-Katz 1.0 (KK1.0) to detect low, moderate and heavy intensity infections of Ascaris, Trichuris and hookworms

    up to 10 months

  • Diagnostic Performance P1.3-4

    The clinical specificity of Kato-Katz 2.0 (KK2.0) and Kato-Katz 1.0 (KK1.0) to detect low, moderate and heavy intensity infections of Ascaris, Trichuris and hookworms

    up to 10 months

  • Repeatability and Reproducibility Performance P2

    The repeatability and the reproducibility of the scanning process, the AI verification process, the Kato-Katz 2.0 (KK2.0) system as a whole and the manual counting by a microscopist (Kato-Katz 1.0 (KK1.0)).

    up to 10 months

  • Time to Result P3

    Time to result for the artificial intelligence digital pathology diagnostic (Kato-Katz 2.0 (KK2.0)) result.

    up to 10 months

  • Cost Efficiency P4.1

    The total survey cost to reliably inform a stop decision to the program for Kato-Katz 2.0 (KK2.0) and Kato-Katz 1.0 (KK1.0).

    up to 10 months

  • Cost Efficiency P4.2

    The total survey cost to reliably inform a declaration that STH are eliminated as a public health problem for Kato-Katz 2.0 (KK2.0) and Kato-Katz 1.0 (KK1.0).

    up to 10 months

  • Usability observation P5

    The ease-of-use of the complete AI-DP work process for the identified end-users assessed by observations of user groups and user interviews.

    up to 10 months

Secondary Outcomes (17)

  • Diagnostic performance S1.1

    up to 10 months

  • Diagnostic performance S1.2

    up to 10 months

  • Diagnostic performance S1.3

    up to 10 months

  • Diagnostic performance S1.4

    up to 10 months

  • Repeatability and Reproducibility Performance S2.1

    up to 10 months

  • +12 more secondary outcomes

Study Arms (2)

School aged children in Ethiopia

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

Diagnostic Test: Artificial Intelligence Digital Pathology

School aged children in Uganda

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

Diagnostic Test: Artificial Intelligence Digital Pathology

Interventions

Artificial Intelligence Digital PathologyDIAGNOSTIC_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.

Also known as: Kato-Katz 2.0
School aged children in EthiopiaSchool aged children in Uganda

Eligibility Criteria

Age5 Years - 14 Years
Sexall
Healthy VolunteersYes
Age GroupsChild (0-17)
Sampling MethodProbability Sample
Study Population

The study will focus on SAC (age 5 - 14) only, since they are the major target of large-scale deworming programs against STHs. The investigators will apply the inclusion and exclusion criteria summarized above

You may qualify if:

  • 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

You may not qualify if:

  • 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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Related Publications (1)

  • 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.

    PMID: 39466830DERIVED

Related Links

MeSH Terms

Conditions

Schistosomiasis mansoni

Condition Hierarchy (Ancestors)

SchistosomiasisTrematode InfectionsHelminthiasisParasitic DiseasesInfectionsVector Borne Diseases

Study Officials

  • Bruno Levecke, PhD

    University Ghent

    STUDY DIRECTOR

  • Zeleke Mekonnen, PhD

    Jimma University

    PRINCIPAL INVESTIGATOR

  • Narcis Kabatereine, PhD

    Ministry of Health, Uganda

    PRINCIPAL INVESTIGATOR

Central Study Contacts

Peter Dahlberg, MSc

CONTACT

+46735195218peter.dahlberg@enaiblers.com

Peter Ward, PhD Student

CONTACT

+46702595977peter.ward@enaiblers.com

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Target Duration
3 Days
Sponsor Type
INDUSTRY
Responsible Party
SPONSOR

Study Record Dates

First Submitted

September 15, 2023

First Posted

September 26, 2023

Study Start

October 1, 2023

Primary Completion

December 1, 2023

Study Completion

July 1, 2024

Last Updated

October 2, 2023

Record last verified: 2023-09

Data Sharing

IPD Sharing
Will not share

The investigators do not intend to share individual participant data (IPD).