Table 1.
The six 2030 targets and corresponding milestones put forward by the WHO [7].
Fig 1.
An overview of how Kato-Katz (KK) thick smears are processed with the AI-DP (KK2.0).
AI: artificial intelligence, KK: Kato-Katz. Figure created using BioRender.com.
Fig 2.
Tentative schedule for enrolment and assessments.
AI-DP: artificial intelligence based digital pathology system.
Table 2.
Inclusion and exclusion criteria that will be endorsed during the recruitment of participants (adapted from [29]).
Table 3.
An overview of the hypotheses, primary, and secondary outcomes to comprehensively evaluate KK2.0.
Fig 3.
Overview of the study design for the experiment on diagnostic performance.
FOV: field-of-view, KK: Kato-Katz. Figure created using BioRender.com.
Fig 4.
Overview of the study design for the experiment on the repeatability and reproducibility.
Figure created using BioRender.com.
Table 4.
Overview of the required number of KK thick smear to test the hypotheses for the experiments on diagnostic performance and repeatability/reproducibility.
Fig 5.
Overview of the different outcome scenarios based on a random sample and its corresponding CI.
This figure illustrates the different outcome scenarios around the difference in performance between KK2.0 and KK1.0 based on the CI. The green lines represent the scenarios where there is evidence of non-inferiority, while the lines in orange illustrate the scenarios where there is no evidence of non-inferiority. In this example we set the level of equivalence at -5 percent difference between (KK2.0 –KK1.0), a negative value indicating that KK1.0 is better.
Table 5.
The FEC thresholds defining low intensity and MHI STH infections.
This table summarizes the WHO FEC (in EPG) thresholds to classify the intensity of STH infections into low, moderate and heavy [44].
Fig 6.
Overview per cluster of how the current AI-DP already meets the attributes defined in the WHO TPP criteria, and for which attributes this study will provide full, partial or no evidence.