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

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.

Automated summary

An AI system using deep learning was developed to automatically detect colorectal neoplasms in colon capsule endoscopy (CCE) images. The system showed good performance in terms of sensitivity, specificity, and accuracy after being trained and validated with around 16,000 tumor images.