https://scholars.lib.ntu.edu.tw/handle/123456789/540916
標題: | 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 | 作者: | Everson M. Herrera L.C.G.P. Li W. Luengo I.M. Ahmad O. Banks M. Magee C. Alzoubaidi D. Hsu H.M. Graham D. Vercauteren T. Lovat L. Ourselin S. Kashin S. HSIU-PO WANG Wang W.-L. Haidry R.J. |
關鍵字: | Artificial intelligence; computer-aided diagnosis; endoscopy; neural networks; oesophageal cancer; squamous cell cancer | 公開日期: | 2019 | 出版社: | SAGE Publications Ltd | 卷: | 7 | 期: | 2 | 起(迄)頁: | 297-306 | 來源出版物: | United European Gastroenterology Journal | 摘要: | Background: Intrapapillary capillary loops (IPCLs) represent an endoscopically visible feature of early squamous cell neoplasia (ESCN) which correlate with invasion depth – an important factor in the success of curative endoscopic therapy. IPCLs visualised on magnification endoscopy with Narrow Band Imaging (ME-NBI) can be used to train convolutional neural networks (CNNs) to detect the presence and classify staging of ESCN lesions. Methods: A total of 7046 sequential high-definition ME-NBI images from 17 patients (10 ESCN, 7 normal) were used to train a CNN. IPCL patterns were classified by three expert endoscopists according to the Japanese Endoscopic Society classification. Normal IPCLs were defined as type A, abnormal as B1–3. Matched histology was obtained for all imaged areas. Results: This CNN differentiates abnormal from normal IPCL patterns with 93.7% accuracy (86.2% to 98.3%) and sensitivity and specificity for classifying abnormal IPCL patterns of 89.3% (78.1% to 100%) and 98% (92% to 99.7%), respectively. Our CNN operates in real time with diagnostic prediction times between 26.17 ms and 37.48 ms. Conclusion: Our novel and proof-of-concept application of computer-aided endoscopic diagnosis shows that a CNN can accurately classify IPCL patterns as normal or abnormal. This system could be used as an in vivo, real-time clinical decision support tool for endoscopists assessing and directing local therapy of ESCN. ? Author(s) 2019. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059896338&doi=10.1177%2f2050640618821800&partnerID=40&md5=74ce5a72627a9ee838ec3d7ae8912408 https://scholars.lib.ntu.edu.tw/handle/123456789/540916 |
ISSN: | 2050-6406 | DOI: | 10.1177/2050640618821800 | SDG/關鍵字: | accuracy; Article; artificial intelligence; clinical article; controlled study; endoscopy; eradication therapy; esophageal squamous cell carcinoma; female; gastroscopy; histopathology; human; human tissue; image quality; lamina propria; magnifying endoscopy; male; narrow band imaging; priority journal; proof of concept; sensitivity and specificity; diagnostic imaging; early cancer diagnosis; esophageal squamous cell carcinoma; esophagoscopy; image processing; neovascularization (pathology); pathology; procedures; reproducibility; Taiwan; Artificial Intelligence; Early Detection of Cancer; Esophageal Squamous Cell Carcinoma; Esophagoscopy; Female; Humans; Image Processing, Computer-Assisted; Male; Neovascularization, Pathologic; Reproducibility of Results; Sensitivity and Specificity; Taiwan |
顯示於: | 醫學系 |
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