人工智能在上消化道内镜检查中的应用与发展

ISSN:2705-098X(P)

EISSN:2705-0505(O)

语言:中文

作者
王毓龙
文章摘要
近年来,人工智能(AI)在医学中的应用,特别是在可视化方法应用的专业领域,如放射学、胃肠病学(消化内镜)、外科和皮肤科,得到了越来越多的关注。AI定义为计算机实现人类认知表现的能力,主要包括学习和决策。上消化道内镜检查能够检查食道、胃和十二指肠。除了内镜设备的质量和患者准备外,上消化道内镜检查的表现还取决于内镜医生的经验和知识。AI在内窥镜中的应用包括计算机辅助检测和更复杂的计算机辅助诊断,旨在提高癌前和恶性病变的检测率,特别是早期发现巴雷特食管中的异常增生、食管癌和胃癌以及幽门螺杆菌感染。AI减轻了内镜医生的工作量,不受人为因素影响,提高了内窥镜方法的诊断准确性和质量。
文章关键词
消化内镜;人工智能;上消化道;胃癌;食管癌
参考文献
[1] Kaul, V., S. Enslin, and S.A. Gross, History of artificial intelligence in medicine. Gastrointest Endosc, 2020. 92(4): p. 807-812. [2] Tokat, M., et al., Artificial Intelligence in Upper Gastrointestinal Endoscopy. Digestive Diseases, 2021. 40(4): p. 395-408. [3] Okagawa, Y., et al., Artificial Intelligence in Endoscopy. Digestive Diseases and Sciences, 2022. 67(5): p. 1553-1572. [4] Ebigbo, A., et al., A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology. Endosc Int Open, 2019. 07(12): p. E1616-E1623. [5] Renna, F., et al., Artificial Intelligence for Upper Gastrointestinal Endoscopy: A Roadmap from Technology Development to Clinical Practice. Diagnostics (Basel), 2022. 12(5). [6] Sung, H., et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 2021. 71(3): p. 209-249. [7] Pennathur, A., et al., Oesophageal carcinoma. The Lancet, 2013. 381(9864): p. 400-412. [8] Peters, Y., et al., Barrett oesophagus. Nature Reviews Disease Primers, 2019. 5(1): p. 35. [9] Weusten, B., et al., Endoscopic management of Barrett’s esophagus: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy, 2017. 49(02): p. 191-198. [10] van der Sommen, F., et al., Computer-aided detection of early neoplastic lesions in Barrett’s esophagus. Endoscopy, 2016. 48(07): p. 617-624. [11] de Groof, A.J., et al., Deep-Learning System Detects Neoplasia in Patients With Barrett’s Esophagus With Higher Accuracy Than Endoscopists in a Multistep Training and Validation Study With Benchmarking. Gastroenterology, 2020. 158(4): p. 915-929.e4. [12] Cai, S.L., et al., Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video). Gastrointest Endosc, 2019. 90(5): p. 745-753.e2. [13] Everson, M., et al., Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study. United European Gastroenterol J, 2019. 7(2): p. 297-306. [14] Nakagawa, K., et al., Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists. Gastrointest Endosc, 2019. 90(3): p. 407-414. [15] Katai, H., et al., Five-year survival analysis of surgically resected gastric cancer cases in Japan: a retrospective analysis of more than 100,000 patients from the nationwide registry of the Japanese Gastric Cancer Association (2001-2007). Gastric Cancer, 2018. 21(1): p. 144-154. [16] Miyaki, R., et al., Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement. Journal of Gastroenterology and Hepatology, 2013. 28(5): p. 841-847. [17] Luo, H., et al., Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol, 2019. 20(12): p. 1645-1654. [18] Kubota, K., et al., Medical image analysis: computer-aided diagnosis of gastric cancer invasion on endoscopic images. Surgical Endoscopy, 2012. 26(5): p. 1485-1489. [19] Niikura, R., et al., Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy. Endoscopy, 2022. 54(08): p. 780-784. [20] Lui, T.K.L., V.W.M. Tsui, and W.K. Leung, Accuracy of artificial intelligence–assisted detection of upper GI lesions: a systematic review and meta-analysis. Gastrointestinal Endoscopy, 2020. 92(4): p. 821-830.e9.
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