A comprehensive evaluation of an artificial intelligence based digital pathology to monitor large-scale deworming programs against soil-transmitted helminths: A study protocol.

Background: 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, we 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 this technology as a cost-efficient end-to-end diagnostic to inform STH control programs against the target product profiles (TPP) of the World Health Organisation (WHO) is the next step for validation.

Developing biomarker assays to accelerate tuberculosis drug development: defining target product profiles.

Drug development for tuberculosis is hindered by the methodological limitations in the definitions of patient outcomes, particularly the slow organism growth and difficulty in obtaining suitable and representative samples throughout the treatment. We developed target product profiles for biomarker assays suitable for early-phase and late-phase clinical drug trials by consulting subject-matter experts on the desirable performance and operational characteristics of such assays for monitoring of tuberculosis treatment in drug trials. Minimal and optimal criteria were defined for scope, intended use, pricing, performance, and operational characteristics of the biomarkers.

Whole blood transcriptome analysis in onchocerciasis.

Identifying the molecular mechanisms controlling the host's response to infection with is important to understand how the human host controls such parasitic infection. Little is known of the cellular immune response upon infection with . We performed a transcriptomic study using PAXgene-preserved whole blood from 30 nodule-positive individuals and 21 non-endemic controls.

Affordable artificial intelligence-based digital pathology for neglected tropical diseases: A proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears.

Background: With the World Health Organization's (WHO) publication of the 2021-2030 neglected tropical diseases (NTDs) roadmap, the current gap in global diagnostics became painfully apparent. Improving existing diagnostic standards with state-of-the-art technology and artificial intelligence has the potential to close this gap.

Methodology/principal Findings: We prototyped an artificial intelligence-based digital pathology (AI-DP) device to explore automated scanning and detection of helminth eggs in stool prepared with the Kato-Katz (KK) technique, the current diagnostic standard for diagnosing soil-transmitted helminths (STHs; Ascaris lumbricoides, Trichuris trichiura and hookworms) and Schistosoma mansoni (SCH) infections.