Authors
Hannah Lee, Yeong Heon Han, Jun-Won Chung, Kyoung Oh Kim, Kwang An Kwon, Jung Ho Kim
Published in
The Korean journal of helicobacter and upper gastrointestinal research. Volume 26. Issue 3. Pages 294-299. Epub Sep 08, 2026.
Abstract
Esophageal cancer is associated with a relatively poor prognosis. Early detection and diagnosis are important for the survival and prognosis of patients with esophageal cancer. Recently, the development of artificial intelligence (AI) and its application in clinical medicine has led to remarkable progress in various endoscopic fields, including the detection of Barrett's esophagus and esophageal cancer. Compared to human errors induced by fatigue and impairment in diagnostic precision, progression in the field of AI, including deep learning and convolutional neural networks, has resulted in improved diagnostic accuracy. Subtle microvascular changes in lesions, visual disturbances, and fatigue, as well as varying endoscopic expertise depending on the operator, are the major causes of missing rates in detecting cancerous lesions. Specifically, the rate of missed diagnoses of esophageal squamous cell carcinoma reaches 4%-17%. In contrast, deep learning-based AI assists in the diagnosis of esophageal squamous cell carcinoma, evaluation of invasion depth, microvascular classification, and delineation of the margin of the lesion. In this review article, we investigated the development of the current AI model introduced for esophageal precancerous lesions, including Barrett's and esophageal cancer.
PMID:
42755000
Bibliographic data and abstract were imported from PubMed on 18 Sep 2026.
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