Automatic detection of colorectal neoplasia in wireless colon capsule endoscopic images using a deep convolutional neural network
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- Atsuo Yamada
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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- Ryota Niikura
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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- Keita Otani
- Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
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- Tomonori Aoki
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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- Kazuhiko Koike
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
説明
<jats:title>Abstract</jats:title><jats:p> Background Although colorectal neoplasms are the most common abnormalities found in colon capsule endoscopy (CCE), no computer-aided detection method is yet available. We developed an artificial intelligence (AI) system that uses deep learning to automatically detect such lesions in CCE images.</jats:p><jats:p> Methods We trained a deep convolutional neural network system based on a Single Shot MultiBox Detector using 15 933 CCE images of colorectal neoplasms, such as polyps and cancers. We assessed performance by calculating areas under the receiver operating characteristic curves, along with sensitivities, specificities, and accuracies, using an independent test set of 4784 images, including 1850 images of colorectal neoplasms and 2934 normal colon images.</jats:p><jats:p> Results The area under the curve for detection of colorectal neoplasia by the AI model was 0.902. The sensitivity, specificity, and accuracy were 79.0 %, 87.0 %, and 83.9 %, respectively, at a probability cutoff of 0.348.</jats:p><jats:p> Conclusions We developed and validated a new AI-based system that automatically detects colorectal neoplasms in CCE images.</jats:p>
収録刊行物
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- Endoscopy
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Endoscopy 53 (08), 832-836, 2020-09-18
Georg Thieme Verlag KG