Artificial intelligence in human parasitic gastrointestinal infections: a systematic review closing the protozoan gap

Abstract

Abstract Human parasitic gastrointestinal infections affect over 3.5 billion people worldwide, yet their diagnostic management remains heavily dependent on conventional microscopy. While artificial intelligence (AI) has emerged as a valuable tool in digital parasitology, its application has progressed unevenly across different parasite classes. This systematic review evaluates AI-based diagnostic methods for human gastrointestinal parasites, systematically comparing performance between helminth detection and protozoan identification. From 4323 identified records, 77 studies met our inclusion criteria after rigorous application of exclusion criteria. Of these, 42 studies (54.5%) focused on helminth detection, 28 studies (36.4%) focused on gastrointestinal protozoa and 7 studies (9.1%) evaluated algorithms applicable to both parasite classes. Convolutional neural networks dominated the field (66.2% of studies), with You Only Look Once variants representing the most common object detection architecture (27.3%). For helminth detection, reported sensitivities ranged from 85% to 98% in externally validated studies, with several systems demonstrating superior sensitivity compared to expert microscopists. For protozoan detection, although internal validation frequently reported accuracies exceeding 90%, only 17.9% of studies performed external validation, which revealed substantial performance degradation of 15–30 percentage points. AI demonstrates promising performance in controlled settings, yet it remains limited by a lack of external validation. Notably, 71.4% of the datasets were sourced from a single institution, restricting the scope of generalizability claims. Closing this diagnostic gap requires coordinated efforts to establish standardized imaging protocols, create diverse open-access repositories and adapt advanced machine learning techniques from adjacent fields such as digital pathology.