Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia

Abstract

Background and Objectives: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly angioectasia, are the most common etiology. Nevertheless, the diagnostic yield of DAE for the detection of these lesions is suboptimal. Deep learning algorithms have shown great potential for automatic detection of lesions in endoscopy. We aimed to develop an artificial intelligence (AI) model for the automatic detection of angioectasia DAE images. Materials and Methods: A convolutional neural network (CNN) was developed using DAE images. Each frame was labeled as normal/mucosa or angioectasia. The image dataset was split for the constitution of training and validation datasets. The latter was used for assessing the performance of the CNN. Results: A total of 72 DAE exams were included, and 6740 images were extracted (5345 of normal mucosa and 1395 of angioectasia). The model had a sensitivity of 88.5%, a specificity of 97.1% and an AUC of 0.988. The image processing speed was 6.4 ms/frame. Conclusions: The application of AI to DAE may have a significant impact on the management of patients with suspected mid-gastrointestinal bleeding.

A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination

“…Automatic detection assists in reducing the miss rate of four common lesions instead of a single type, and classification will be finished after detecting them, which is one of the advantages of our study compared with previous studies [ 50 – 52 ]. Our system has reached a high accuracy in detecting and classifying small bowel lesions.…”

Artificial Intelligence and Panendoscopy—Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy

“…In spite of the exponential growth in the development of deep learning models for CE [ 44 , 45 ], the application of AI technologies to DAE is still in a premature state, with scarce works applying deep learning models to augment the diagnostic performance of the exam. Additionally, the existing works were focused on detecting a specific type of lesion [ 18 , 19 , 20 ], which guarantees a diminished clinical applicability and a lower technology readiness level (TRL) of the technology. This work constitutes a landmark with the development of a CNN capable of detecting clinically relevant lesions during DAE, namely, vascular and protuberant lesions, hematic residues, ulcers and erosions.…”

“…Nevertheless, the implementation of AI models for DAE is still in the early stages. In fact, AI application in DAE has been studied for the identification of vascular lesions, protuberant lesions, ulcers and erosions [ 18 , 19 , 20 ]. Nevertheless, the clinical application of such technology is dependent on the ability to identify different types of lesions throughout a complete exam, while functioning in different devices.…”

Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions

“…Despite significant advances in efficiency and identification of lesions by applying deep-learning technologies in CE, there is still a lack of evidence of AI pertinence during DAE. Although AI application during DAE has been studied to detect vascular and protruding enteric lesions automatically, no proof-of-concept studies in ulcerative lesions have been conducted [ 12 , 13 ].…”