Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands.
Department of Electrical Engineering, VCA group, Eindhoven University of Technology, Groene Loper 19, 5612 AP Eindhoven, the Netherlands.
Gastrointest Endosc Clin N Am. 2021 Jan;31(1):91-103. doi: 10.1016/j.giec.2020.08.006. Epub 2020 Oct 26.
Because the current Barrett's esophagus (BE) surveillance protocol suffers from sampling error of random biopsies and a high miss-rate of early neoplastic lesions, many new endoscopic imaging and sampling techniques have been developed. None of these techniques, however, have significantly increased the diagnostic yield of BE neoplasia. In fact, these techniques have led to an increase in the amount of visible information, yet endoscopists and pathologists inevitably suffer from variations in intra- and interobserver agreement. Artificial intelligence systems have the potential to overcome these endoscopist-dependent limitations.
由于当前 Barrett 食管 (BE) 的监测方案存在随机活检的采样误差和早期肿瘤病变的高漏诊率,因此开发了许多新的内镜成像和采样技术。然而,这些技术都没有显著提高 BE 肿瘤的诊断率。事实上,这些技术导致了可见信息量的增加,但内镜医生和病理学家不可避免地会受到观察者内和观察者间一致性的差异的影响。人工智能系统有可能克服这些依赖于内镜医生的局限性。
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