Journal
DIAGNOSTICS
Volume 12, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/diagnostics12061445
Keywords
colon capsule endoscopy; artificial intelligence; convolutional neural network; colorectal neoplasia
Categories
Funding
- Fundacao para a Ciencia e Tecnologia (FCT) [CPCA/A0/7363/2020]
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This study aimed to develop an artificial intelligence algorithm using a convolutional neural network for automatic detection of colonic protruding lesions in CCE images. The results showed that the developed algorithm accurately detected protruding lesions, which may increase the diagnostic accuracy and acceptance of CCE for screening of colorectal neoplasia.
Background: Colon capsule endoscopy (CCE) is an alternative for patients unwilling or with contraindications for conventional colonoscopy. Colorectal cancer screening may benefit greatly from widespread acceptance of a non-invasive tool such as CCE. However, reviewing CCE exams is a time-consuming process, with risk of overlooking important lesions. We aimed to develop an artificial intelligence (AI) algorithm using a convolutional neural network (CNN) architecture for automatic detection of colonic protruding lesions in CCE images. An anonymized database of CCE images collected from a total of 124 patients was used. This database included images of patients with colonic protruding lesions or patients with normal colonic mucosa or with other pathologic findings. A total of 5715 images were extracted for CNN development. Two image datasets were created and used for training and validation of the CNN. The AUROC for detection of protruding lesions was 0.99. The sensitivity, specificity, PPV and NPV were 90.0%, 99.1%, 98.6% and 93.2%, respectively. The overall accuracy of the network was 95.3%. The developed deep learning algorithm accurately detected protruding lesions in CCE images. The introduction of AI technology to CCE may increase its diagnostic accuracy and acceptance for screening of colorectal neoplasia.
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