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IPCLs visualised on magnification endoscopy with Narrow Band Imaging (ME\u2010NBI) can be used to train convolutional neural networks (CNNs) to detect the presence and classify staging of ESCN lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 7046 sequential high\u2010definition ME\u2010NBI 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\u20133. Matched histology was obtained for all imaged areas.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2009ms and 37.48\u2009ms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our novel and proof\u2010of\u2010concept application of computer\u2010aided 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\u2010time clinical decision support tool for endoscopists assessing and directing local therapy of ESCN.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/2050640618821800","type":"journal-article","created":{"date-parts":[[2019,1,7]],"date-time":"2019-01-07T19:00:50Z","timestamp":1546887650000},"page":"297-306","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":82,"title":["Artificial intelligence for the real\u2010time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof\u2010of\u2010concept study"],"prefix":"10.1002","volume":"7","author":[{"given":"M","family":"Everson","sequence":"first","affiliation":[{"name":"Division of Surgery and Interventional Science University College London (UCL) UK"},{"name":"Department of Gastroenterology University College Hospital NHS Foundation 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