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

Sponsor

Enaiblers AB (Industry)

Overall Status

Not yet recruiting

CT.gov ID

NCT06055530

Collaborator

Jimma University (Other), Ministry of Health, Uganda (Other), Ghent University, Belgium (Other)

1,100

Enrollment

Anticipated Duration (Months)

Study Details

Study Description

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).

Condition or Disease	Intervention/Treatment	Phase
Soil Transmitted Helminths Schistosomiasis Mansoni	Diagnostic Test: Artificial Intelligence Digital Pathology

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.

Study Design

Study Type:

Observational [Patient Registry]

Anticipated Enrollment :

1100 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

A Comprehensive Evaluation of an Artificial Intelligence Based Digital Pathology to Monitor Large-scale Deworming Programs Against Soil-transmitted Helminths

Anticipated Study Start Date :

Oct 1, 2023

Anticipated Primary Completion Date :

Dec 1, 2023

Anticipated Study Completion Date :

Jul 1, 2024

Arms and Interventions

Arm	Intervention/Treatment
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 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. Other Names: Kato-Katz 2.0
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 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. Other Names: Kato-Katz 2.0

Arm

Intervention/Treatment

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 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.

Other Names:

Kato-Katz 2.0

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

Other Names:

Kato-Katz 2.0

Outcome Measures

Primary Outcome Measures

Diagnostic performance, P1.1-2 [up to 10 months]
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
Diagnostic Performance P1.3-4 [up to 10 months]
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
Repeatability and Reproducibility Performance P2 [up to 10 months]
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)).
Time to Result P3 [up to 10 months]
Time to result for the artificial intelligence digital pathology diagnostic (Kato-Katz 2.0 (KK2.0)) result.
Cost Efficiency P4.1 [up to 10 months]
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).
Cost Efficiency P4.2 [up to 10 months]
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).
Usability observation P5 [up to 10 months]
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.

Secondary Outcome Measures

Diagnostic performance S1.1 [up to 10 months]
the clinical sensitivity and specificity of Kato-Katz 2.0 (KK2.0)KK2.0 and Kato-Katz 1.0 (KK1.0) to detect infections of S. Mansoni
Diagnostic performance S1.2 [up to 10 months]
The detection limit that yields a positive test result for both Kato-Katz 2.0 (KK2.0) and Kato-Katz 1.0 (KK1.0) and Ascaris, Trichuris, hookworm and S. Mansoni separately
Diagnostic performance S1.3 [up to 10 months]
The egg recovery rate of KK1.0 and Kato-Katz 2.0 (KK2.0) when compared to the ground truth for Ascaris, Trichuris, hookworms and S. mansoni
Diagnostic performance S1.4 [up to 10 months]
the clinical sensitivity and clinical specificity of the AI-DP when the AI verification process is simplified (only objects for which the AI is uncertain) or even omitted
Repeatability and Reproducibility Performance S2.1 [up to 10 months]
the agreement between repeated egg counts for Ascaris, Trichuris and S. mansoni
Repeatability and Reproducibility Performance S2.2 [up to 10 months]
the repeatability and reproducibility in test results when the AI verification process is simplified (only objects for which the AI is uncertain)
Time to Result S3.1 [up to 10 months]
time for participant registration using Electronic Data Capture (EDC) tools and quick response (QR) code printing
Time to Result S3.2 [up to 10 months]
the correlation between time-to-result and Ascaris, Trichuris and S. mansoni egg counts recorded by Kato-Katz 2.0 (KK2.0)
Time to Result S3.3 [up to 10 months]
time-to-result of the AI-DP when the AI verification process is simplified (only objects for which the AI is uncertain) or even omitted
Cost Efficiency S4.1 [up to 10 months]
the total survey cost to make reliable program decisions on the frequency of large-scale deworming programs for Kato-Katz 2.0 (KK2.0) and KK1.0
Cost Efficiency S4.2 [up to 10 months]
the total survey cost to reliably monitor the therapeutic drug efficacy of anthelmintic against STHs for Kato-Katz 2.0 (KK2.0)
Cost Efficiency S4.3 [up to 10 months]
the total survey cost to make reliable program decisions on the frequency of large-scale deworming programs for Kato-Katz 2.0 (KK2.0) when the AI verification process is simplified (only objects for which the AI is uncertain) or even omitted
Cost Efficiency S4.4 [up to 10 months]
the required performance of AI to make reliable program decisions on the frequency of large-scale deworming programs for Kato-Katz 2.0 (KK2.0)
Cost Efficiency S4.5 [up to 10 months]
the optimal set-up for Kato-Katz 2.0 (KK2.0) (sample throughput; number of AI-DP devices; number of operators) to inform large-scale deworming programs when deployed in a fully equipped laboratory and M&E setting
Usability observation S5.1 [up to 10 months]
identification of barriers for a successful outcome of the complete work process with the AI-DP device by the identified end-users
Usability observation S5.2 [up to 10 months]
The task completion time for novel users of the AI-DP device
Usability observation S5.3 [up to 10 months]
Outcome rates (Success/failure) for novel users of the AI-DP device.

Eligibility Criteria

Criteria

Ages Eligible for Study:

5 Years to 14 Years

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Yes

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