Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network

BACKGROUND: Detecting blood content in the gastrointestinal tract is one of the crucial applications of capsule endoscopy (CE). The suspected blood indicator (SBI) is a conventional tool used to automatically tag images depicting possible bleeding in the reading system. We aim to develop a deep learning-based system to detect blood content in images and compare its performance with that of the SBI. METHODS: We trained a deep convolutional neural network (CNN) system, using 27,847 CE images (6,503 images depicting blood content from 29 patients and 21,344 images of normal mucosa from 12 patients). We assessed its performance by calculating the area under the receiver operating characteristic curve (ROC-AUC), and its sensitivity, specificity, and accuracy, using an independent test set of 10,208 small-bowel images (208 images depicting blood content and 10,000 images of normal mucosa). The performance of the CNN was compared with that of the SBI, in individual image analysis, using the same test set. RESULTS: The AUC for the detection of blood content was 0.9998. The sensitivity, specificity, and accuracy of the CNN were 96.63%, 99.96%, and 99.89%, respectively, at a cut-off value of 0.5 for the probability score, which were significantly higher than those of the SBI (76.92%, 99.82%, and 99.35%, respectively). The trained CNN required 250 seconds to evaluate 10,208 test images. CONCLUSIONS: We developed and tested the CNN-based detection system for blood content in capsule endoscopy images. This system has the potential to outperform the SBI system, and the patient-level analyses on larger studies are required.