From data to artificial intelligence: evaluating the readiness of gastrointestinal endoscopy datasets

• Published in: Journal of the Canadian Association of Gastroenterology Vol. 8; no. Supplement_2; pp. S81 - S86
• Main Authors: Elamin, Sami, Johri, Shreya, Rajpurkar, Pranav, Geisler, Enrik, Berzin, Tyler M
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.03.2025
• Subjects: Supplement; Datasets; Gastroenterology; Machine learning; computer vision; AI; Data; Endoscopy; Algorithm; Artificial intelligence
• ISSN: 2515-2084; 2515-2092; 2515-2092
• DOI: 10.1093/jcag/gwae041

The incorporation of artificial intelligence (AI) into gastrointestinal (GI) endoscopy represents a promising advancement in gastroenterology. With over 40 published randomized controlled trials and numerous ongoing clinical trials, gastroenterology leads other medical disciplines in AI research. Computer-aided detection algorithms for identifying colorectal polyps have achieved regulatory approval and are in routine clinical use, while other AI applications for GI endoscopy are in advanced development stages. Near-term opportunities include the potential for computer-aided diagnosis to replace conventional histopathology for diagnosing small colon polyps and increased AI automation in capsule endoscopy. Despite significant development in research settings, the generalizability and robustness of AI models in real clinical practice remain inconsistent. The GI field lags behind other medical disciplines in the breadth of novel AI algorithms, with only 13 out of 882 Food and Drug Administration (FDA)-approved AI models focussed on GI endoscopy as of June 2024. Additionally, existing GI endoscopy image databases are disproportionately focussed on colon polyps, lacking representation of the diversity of other endoscopic findings. High-quality datasets, encompassing a wide range of patient demographics, endoscopic equipment types, and disease states, are crucial for developing effective AI models for GI endoscopy. This article reviews the current state of GI endoscopy datasets, barriers to progress, including dataset size, data diversity, annotation quality, and ethical issues in data collection and usage, and future needs for advancing AI in GI endoscopy.