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-based specimens 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. First, we embedded a prototype whole slide imaging scanner into field studies in Cambodia, Ethiopia, Kenya and Tanzania. With the scanner, over 300 KK stool thick smears were scanned, resulting in total of 7,780 field-of-view (FOV) images containing 16,990 annotated helminth eggs (Ascaris: 8,600; Trichuris: 4,083; hookworms: 3,623; SCH: 684). Around 90% of the annotated eggs were used to train a deep learning-based object detection model. From an unseen test set of 752 FOV images containing 1,671 manually verified STH and SCH eggs (the remaining 10% of annotated eggs), our trained object detection model extracted and classified helminth eggs from co-infected FOV images in KK smears, achieving a weighted average precision (± standard deviation) of 94.9% ± 0.8% and a weighted average recall of 96.1% ± 2.1% across all four helminth egg species. Conclusions/Significance We present a proof-of-concept for an AI-DP device for automated scanning and detection of helminth eggs in KK stool thick smears. We identified obstacles that need to be addressed before the diagnostic performance can be evaluated against the target product profiles for both STH and SCH. Given that these obstacles are primarily associated with the required hardware and scanning methodology, opposed to the feasibility of artificial intelligence-based results, we are hopeful that this research can support the 2030 NTDs road map and eventually other poverty-related diseases for which microscopy is the diagnostic standard.

The World Health Organization (WHO) recently published target product profiles (TPPs) for neglected tropical diseases (NTDs) to inform and accelerate the development of diagnostics tools necessary to achieve targets in the decade ahead. These TPPs describe the minimal and ideal requirements for various diagnostic needs related to NTD specific use-cases. An early step towards the manufacture and implementation of new diagnostics is to critically review the TPPs and translate these into an initial design and ultimately into user requirement specifications (URS). Artificial intelligence-based digital pathology (AI-DP) may overcome critical shortcomings of current standards for most NTDs reliant on microscopy, such as poor reproducibility and error-prone manual read-out. Furthermore, a digitalised workflow can create opportunities to reduce operational costs via increased throughput and automated data capture, analysis, and reporting. Despite these promising benefits, a critical review of the NTD TPPs with consideration to an AI-DP diagnostic solution is lacking. We present a systematic analysis of one of the WHO TPPs with the aim to inform the development of a URS for an AI-DP solution for NTDs. As a case study we focused on monitoring and evaluation (M&E) of programs designed to control soil-transmitted helminths (STHs). To this end, we start by outlining a brief overview of diagnostic needs for STHs, after which we systematically analyse the recently published WHO TPPs, highlighting the technical considerations for an AI-DP diagnostic solution to meet the minimal requirements for this TPP. Finally, we further reflect on the feasibility of an AI-DP informing STH programs towards the WHO 2030 targets in due time.