Artificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks.
Convolutional neural networks (CNNs) are widely used for artificial intelligence (AI)-based image classification. Residual network (ResNet) is a new technology that facilitates the accuracy of image classification by CNN-based AI. In this study, we developed a novel AI model combined with ResNet to...
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Public Library of Science (PLoS)
2021
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oai:doaj.org-article:687a9cbefc8e4ec7a8ba48c807a476e42021-12-02T20:10:17ZArtificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks.1932-620310.1371/journal.pone.0253585https://doaj.org/article/687a9cbefc8e4ec7a8ba48c807a476e42021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253585https://doaj.org/toc/1932-6203Convolutional neural networks (CNNs) are widely used for artificial intelligence (AI)-based image classification. Residual network (ResNet) is a new technology that facilitates the accuracy of image classification by CNN-based AI. In this study, we developed a novel AI model combined with ResNet to diagnose colorectal polyps. In total, 127,610 images consisting of 62,510 images with adenomatous polyps, 30,443 with non-adenomatous hyperplastic polyps, and 34,657 with healthy colorectal normal mucosa were subjected to deep learning after annotation. Each validation process was performed using 12,761 stored images of colorectal polyps by a 10-fold cross validation. The efficacy of the ResNet system was evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy for adenomatous polyps at WLIs were 98.8%, 94.3%, 90.5%, 87.4%, and 92.8%, respectively. Similar results were obtained for adenomatous polyps at narrow-band imagings (NBIs) and chromoendoscopy images (CEIs) (NBIs vs. CEIs: sensitivity, 94.9% vs. 98.2%; specificity, 93.9% vs. 85.8%; PPV, 92.5% vs. 81.7%; NPV, 93.5% vs. 99.9%; and overall accuracy, 91.5% vs. 90.1%). The ResNet model is a powerful tool that can be used for AI-based accurate diagnosis of colorectal polyps.Yoriaki KomedaHisashi HandaRyoma MatsuiShohei HatoriRiku YamamotoToshiharu SakuraiMamoru TakenakaSatoru HagiwaraNaoshi NishidaHiroshi KashidaTomohiro WatanabeMasatoshi KudoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253585 (2021) |
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DOAJ |
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EN |
| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q Yoriaki Komeda Hisashi Handa Ryoma Matsui Shohei Hatori Riku Yamamoto Toshiharu Sakurai Mamoru Takenaka Satoru Hagiwara Naoshi Nishida Hiroshi Kashida Tomohiro Watanabe Masatoshi Kudo Artificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks. |
| description |
Convolutional neural networks (CNNs) are widely used for artificial intelligence (AI)-based image classification. Residual network (ResNet) is a new technology that facilitates the accuracy of image classification by CNN-based AI. In this study, we developed a novel AI model combined with ResNet to diagnose colorectal polyps. In total, 127,610 images consisting of 62,510 images with adenomatous polyps, 30,443 with non-adenomatous hyperplastic polyps, and 34,657 with healthy colorectal normal mucosa were subjected to deep learning after annotation. Each validation process was performed using 12,761 stored images of colorectal polyps by a 10-fold cross validation. The efficacy of the ResNet system was evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy for adenomatous polyps at WLIs were 98.8%, 94.3%, 90.5%, 87.4%, and 92.8%, respectively. Similar results were obtained for adenomatous polyps at narrow-band imagings (NBIs) and chromoendoscopy images (CEIs) (NBIs vs. CEIs: sensitivity, 94.9% vs. 98.2%; specificity, 93.9% vs. 85.8%; PPV, 92.5% vs. 81.7%; NPV, 93.5% vs. 99.9%; and overall accuracy, 91.5% vs. 90.1%). The ResNet model is a powerful tool that can be used for AI-based accurate diagnosis of colorectal polyps. |
| format |
article |
| author |
Yoriaki Komeda Hisashi Handa Ryoma Matsui Shohei Hatori Riku Yamamoto Toshiharu Sakurai Mamoru Takenaka Satoru Hagiwara Naoshi Nishida Hiroshi Kashida Tomohiro Watanabe Masatoshi Kudo |
| author_facet |
Yoriaki Komeda Hisashi Handa Ryoma Matsui Shohei Hatori Riku Yamamoto Toshiharu Sakurai Mamoru Takenaka Satoru Hagiwara Naoshi Nishida Hiroshi Kashida Tomohiro Watanabe Masatoshi Kudo |
| author_sort |
Yoriaki Komeda |
| title |
Artificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks. |
| title_short |
Artificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks. |
| title_full |
Artificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks. |
| title_fullStr |
Artificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks. |
| title_full_unstemmed |
Artificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks. |
| title_sort |
artificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2021 |
| url |
https://doaj.org/article/687a9cbefc8e4ec7a8ba48c807a476e4 |
| work_keys_str_mv |
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