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. 2023 Sep;38(9):1587-1591.
doi: 10.1111/jgh.16274. Epub 2023 Jul 5.

Comparing artificial intelligence to humans for endoscopic diagnosis of gastric neoplasia: An external validation study

Sabrina Xin Zi Quek  1 , Jonathan W J Lee  1   2   3 , Zhu Feng  2 , Min Min Soh  2 , Masayuki Tokano  4 , Yeoh Khay Guan  1   2 , Jimmy B Y So  2   5 , Tomohiro Tada  4   6 , Calvin J Koh  1   2   7
Affiliations

Comparing artificial intelligence to humans for endoscopic diagnosis of gastric neoplasia: An external validation study

Sabrina Xin Zi Quek et al. J Gastroenterol Hepatol. 2023 Sep.

Abstract

Objectives: Artificial intelligence (AI) uses deep learning functionalities that may enhance the detection of early gastric cancer during endoscopy. An AI-based endoscopic system for upper endoscopy was recently developed in Japan. We aim to validate this AI-based system in a Singaporean cohort.

Methods: There were 300 de-identified still images prepared from endoscopy video files obtained from subjects that underwent gastroscopy in National University Hospital (NUH). Five specialists and 6 non-specialists (trainees) from NUH were assigned to read and categorize the images into "neoplastic" or "non-neoplastic." Results were then compared with the readings performed by the endoscopic AI system.

Results: The mean accuracy, sensitivity, and specificity for the 11 endoscopists were 0.847, 0.525, and 0.872, respectively. These values for the AI-based system were 0.777, 0.591, and 0.791, respectively. While AI in general did not perform better than endoscopists on the whole, in the subgroup of high-grade dysplastic lesions, only 29.1% were picked up by the endoscopist rating, but 80% were classified as neoplastic by AI (P = 0.0011). The average diagnostic time was also faster in AI compared with endoscopists (677.1 s vs 42.02 s (P < 0.001).

Conclusion: We demonstrated that an AI system developed in another health system was comparable in diagnostic accuracy in the evaluation of static images. AI systems are faster and not fatigable and may have a role in augmenting human diagnosis during endoscopy. With more advances in AI and larger studies to support its efficacy it would likely play a larger role in screening endoscopy in future.

Keywords: Artificial intelligence; Deep learning; Endoscopy; Gastric cancer; Screening.

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