Reducing reading time and assessing disease in capsule endoscopy videos: A deep learning approach.

Abstract

The wireless capsule endoscope (CE) is a valuable diagnostic tool in gastroenterology, offering a safe and minimally invasive visualization of the gastrointestinal tract. One of the few drawbacks identified by the gastroenterology community is the time-consuming task of analyzing CE videos. This article investigates the feasibility of a computer-aided diagnostic method to speed up CE video analysis. We aim to generate a significantly smaller CE video with all the anomalies (i.e., diseases) identified by the medical doctors in the original video. The summarized video consists of the original video frames classified as anomalous by a pre-trained convolutional neural network (CNN). We evaluate our approach on a testing dataset with eight CE videos captured with five CE types and displaying multiple anomalies. On average, the summarized videos contain 93.33% of the anomalies identified in the original videos. The average playback time of the summarized videos is just 10 min, compared to 58 min for the original videos. Our findings demonstrate the potential of deep learning-aided diagnostic methods to accelerate CE video analysis.