Automatic detection of colorectal neoplasia in wireless colon capsule endoscopic images using a deep convolutional neural network

Abstract

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.

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.

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.

Conclusions We developed and validated a new AI-based system that automatically detects colorectal neoplasms in CCE images.

• References

• 1 Niikura R, Yasunaga H, Yamada A. et al. Factors predicting adverse events associated with therapeutic colonoscopy for colorectal neoplasia: a retrospective nationwide study in Japan. Gastrointest Endosc 2016; 84: 971-982.e976
• 2 Saito Y, Saito S, Oka S. et al. Evaluation of the clinical efficacy of colon capsule endoscopy in the detection of lesions of the colon: prospective, multicenter, open study. Gastrointest Endosc 2015; 82: 861-869
• 3 Ota Y, Yamada A, Kobayashi Y. et al. Diagnostic capability of colon capsule endoscopy for advanced colorectal cancer: A pilot study. Dig Endosc 2017; 29: 695-701
• 4 Eliakim R, Yassin K, Niv Y. et al. Prospective multicenter performance evaluation of the second-generation colon capsule compared with colonoscopy. Endoscopy 2009; 41: 1026-1031
• 5 Ozawa T, Ishihara S, Fujishiro M. et al. Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks. Therap Adv Gastroenterol 2020; 13: 1756284820910659
• 6 Misawa M, Kudo SE, Mori Y. et al. Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience. Gastroenterology 2018; 154: 2027-2029.e2023
• 7 The Paris endoscopic classification of superficial neoplastic lesions: esophagus, stomach, and colon: November 30 to December 1, 2002. Gastrointest Endosc 2003; 58: S3-S43
• 8 Eliakim R, Fireman Z, Gralnek IM. et al. Evaluation of the PillCam Colon capsule in the detection of colonic pathology: results of the first multicenter, prospective, comparative study. Endoscopy 2006; 38: 963-970
• 9 Liu W, Anguelov D, Erhan D. et al. SSD: Single Shot MultiBox Detector. Computer Vision – ECCV 2016; 9905: 21-37
• 10 Saito H, Aoki T, Aoyama K. et al. Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc 2020; 92: 144-151.e1
• 11 Schisterman EF, Perkins NJ, Liu A. et al. Optimal cut-point and its corresponding Youden Index to discriminate individuals using pooled blood samples. Epidemiology 2005; 16: 73-81
• 12 DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44: 837-845
• 13 Blanes-Vidal V, Baatrup G, Nadimi ES. Addressing priority challenges in the detection and assessment of colorectal polyps from capsule endoscopy and colonoscopy in colorectal cancer screening using machine learning. Acta Oncol 2019; 58: S29-S36
• 14 Aoki T, Yamada A, Aoyama K. et al. Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc 2019; 89: 357-363.e352