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Updated: Dec 8, 2025
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
Automatic detection of colorectal neoplasia in wireless colon capsule endoscopic images using a deep convolutional
Atsuo Yamada1, Ryota Niikura1, Keita Otani2
1Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
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.
