Automated Detection of Crohn’s Disease Intestinal Strictures on Capsule Endoscopy Images Using Deep Neural Networks

Abstract

Abstract Background and Aims Passable intestinal strictures are frequently detected on capsule endoscopy [CE]. Such strictures are a major component of inflammatory scores. Deep neural network technology for CE is emerging. However, the ability of deep neural networks to identify intestinal strictures on CE images of Crohn’s disease [CD] patients has not yet been evaluated. Methods We tested a state-of-the-art deep learning network for detecting CE images of strictures. Images of normal mucosa, mucosal ulcers, and strictures of Crohn’s disease patients were retrieved from our previously described CE image bank. Ulcers were classified as per degree of severity. We performed 10 cross-validation experiments. A clear patient-level separation was maintained between training and testing sets. Results Overall, the entire dataset included 27 892 CE images: 1942 stricture images, 14 266 normal mucosa images, and 11 684 ulcer images [mild: 7075, moderate: 2386, severe: 2223]. For classifying strictures versus non-strictures, the network exhibited an average accuracy of 93.5% [±6.7%]. The network achieved excellent differentiation between strictures and normal mucosa (area under the curve [AUC] 0.989), strictures and all ulcers [AUC 0.942], and between strictures and different grades of ulcers [for mild, moderate, and severe ulcers—AUCs 0.992, 0.975, and 0.889, respectively]. Conclusions Deep neural networks are highly accurate in the detection of strictures on CE images in Crohn’s disease. The network can accurately separate strictures from ulcers across the severity range. The current accuracy for the detection of ulcers and strictures by deep neural networks may allow for automated detection and grading of Crohn’s disease-related findings on CE.