Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy

Abstract

Background and objectives: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel cleansing to obtain conclusive results. Artificial intelligence (AI) algorithms have been seen to offer important benefits in the field of medical imaging over recent years, particularly through the adaptation of convolutional neural networks (CNNs) to achieve more efficient image analysis. Here, we aimed to develop a deep learning model that uses a CNN to automatically classify the quality of intestinal preparation in CE. Methods: A CNN was designed based on 12,950 CE images obtained at two clinical centers in Porto (Portugal). The quality of the intestinal preparation was classified for each image as: excellent, ≥90% of the image surface with visible mucosa; satisfactory, 50–90% of the mucosa visible; and unsatisfactory, <50% of the mucosa visible. The total set of images was divided in an 80:20 ratio to establish training and validation datasets, respectively. The CNN prediction was compared with the classification established by consensus of a group of three experts in CE, currently considered the gold standard to evaluate cleanliness. Subsequently, how the CNN performed in diagnostic terms was evaluated using an independent validation dataset. Results: Among the images obtained, 3633 were designated as unsatisfactory preparation, 6005 satisfactory preparation, and 3312 with excellent preparation. When differentiating the classes of small-bowel preparation, the algorithm developed here achieved an overall accuracy of 92.1%, with a sensitivity of 88.4%, a specificity of 93.6%, a positive predictive value of 88.5%, and a negative predictive value of 93.4%. The area under the curve for the detection of excellent, satisfactory, and unsatisfactory classes was 0.98, 0.95, and 0.99, respectively. Conclusions: A CNN-based tool was developed to automatically classify small-bowel preparation for CE, and it was seen to accurately classify intestinal preparation for CE. The development of such a system could enhance the reproducibility of the scales used for such purposes.

From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?

“…The methodology was particularly robust, using images from two different centers, two different SB-CE systems, and a large dataset (CE from 4319 patients, 12,950 images of SB mucosa). This study suggests a high potential for replicating this algorithm in real-time practice [70].…”

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“…In fact, the TRL is very different between Gastroenterology fields. For instance, capsule endoscopy is one of the main areas for the development of deep learning models in Gastroenterology [ 38 , 39 ]. However, the majority of studies are still in an early development phase and not validated in the clinical practice.…”

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“…They can detect missed lesions or abnormalities, providing feedback to endoscopists for continuous improvement [11,58]. In addition, AI applications are being designed to evaluate the preparation and cleanliness of the gastrointestinal tract when performing these examinations [68][69][70]. This is one of the most important issues in defining the success of the examination, and if not adequately assessed, it is not clear whether the results of an examination can be trusted.…”