evaluation of an ai-dp for sth deworming programs: a study protocol

Conditions: Soil Transmitted Helminths; Schistosomiasis Mansoni

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).

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.

Eligibility criteria:
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

Keywords: artificial intelligence digital pathology diagnostic performance usability repeatability cost efficiency