完整後設資料紀錄
DC 欄位語言
dc.contributor.authorChen, Peng-Jenen_US
dc.contributor.authorLin, Meng-Chiungen_US
dc.contributor.authorLai, Mei-Juen_US
dc.contributor.authorLin, Jung-Chunen_US
dc.contributor.authorLu, Henry Horng-Shingen_US
dc.contributor.authorTseng, Vincent S.en_US
dc.date.accessioned2018-08-21T05:53:17Z-
dc.date.available2018-08-21T05:53:17Z-
dc.date.issued2018-02-01en_US
dc.identifier.issn0016-5085en_US
dc.identifier.urihttp://dx.doi.org/10.1053/j.gastro.2017.10.010en_US
dc.identifier.urihttp://hdl.handle.net/11536/144505-
dc.description.abstractBACKGROUND & AIMS: Narrow-band imaging is an image-enhanced form of endoscopy used to observed microstructures and capillaries of the mucosal epithelium which allows for real-time prediction of histologic features of colorectal polyps. However, narrow-band imaging expertise is required to differentiate hyperplastic from neoplastic polyps with high levels of accuracy. We developed and tested a system of computer-aided diagnosis with a deep neural network (DNN-CAD) to analyze narrow-band images of diminutive colorectal polyps. METHODS: We collected 1476 images of neoplastic polyps and 681 images of hyperplastic polyps, obtained from the picture archiving and communications system database in a tertiary hospital in Taiwan. Histologic findings from the polyps were also collected and used as the reference standard. The images and data were used to train the DNN. A test set of images (96 hyperplastic and 188 neoplastic polyps, smaller than 5 mm), obtained from patients who underwent colonoscopies from March 2017 through August 2017, was then used to test the diagnostic ability of the DNN-CAD vs endoscopists (2 expert and 4 novice), who were asked to classify the images of the test set as neoplastic or hyperplastic. Their classifications were compared with findings from histologic analysis. The primary outcome measures were diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic time. The accuracy, sensitivity, specificity, PPV, NPV, and diagnostic time were compared among DNN-CAD, the novice endoscopists, and the expert endoscopists. The study was designed to detect a difference of 10% in accuracy by a 2-sided McNemar test. RESULTS: In the test set, the DNN-CAD identified neoplastic or hyperplastic polyps with 96.3% sensitivity, 78.1% specificity, a PPV of 89.6%, and a NPV of 91.5%. Fewer than half of the novice endoscopists classified polyps with a NPV of 90% (their NPVs ranged from 73.9% to 84.0%). DNN-CAD classified polyps as neoplastic or hyperplastic in 0.45 +/- 0.07 seconds-shorter than the time required by experts (1.54 +/- 1.30 seconds) and nonexperts (1.77 +/- 1.37 seconds) (both P < .001). DNN-CAD classified polyps with perfect intra-observer agreement (kappa score of 1). There was a low level of intra-observer and inter-observer agreement in classification among endoscopists. CONCLUSIONS: We developed a system called DNN-CAD to identify neoplastic or hyperplastic colorectal polyps less than 5 mm. The system classified polyps with a PPV of 89.6%, and a NPV of 91.5%, and in a shorter time than endoscopists. This deep-learning model has potential for not only endoscopic image recognition but for other forms of medical image analysis, including sonography, computed tomography, and magnetic resonance images.en_US
dc.language.isoen_USen_US
dc.subjectColon Cancer Detectionen_US
dc.subjectMachine Learningen_US
dc.subjectCost-effectivenessen_US
dc.subjectMagnifyingen_US
dc.titleAccurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysisen_US
dc.typeArticleen_US
dc.identifier.doi10.1053/j.gastro.2017.10.010en_US
dc.identifier.journalGASTROENTEROLOGYen_US
dc.citation.volume154en_US
dc.citation.spage568en_US
dc.citation.epage575en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department統計學研究所zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitute of Statisticsen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000424741500029en_US
顯示於類別:期刊論文