Artificial intelligence and capsule endoscopy: automatic detection of vascular lesions using a convolutional neural network

Abstract

Background Capsule endoscopy (CE) is the first line for evaluation of patients with obscure gastrointestinal bleeding. A wide range of small intestinal vascular lesions with different hemorrhagic potential are frequently found in these patients. Nevertheless, reading CE exams is time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence tools with high performance levels in image analysis. This study aimed to develop a CNN-based model for identification and differentiation of vascular lesions with distinct hemorrhagic potential in CE images. Methods The development of the CNN was based on a database of CE images. This database included images of normal small intestinal mucosa, red spots, and angiectasia/varices. The hemorrhagic risk was assessed by Saurin’s classification. For CNN development, 11,588 images (9525 normal mucosa, 1026 red spots, and 1037 angiectasia/varices) were ultimately extracted. Two image datasets were created for CNN training and testing. Results The network was 91.8% sensitive and 95.9% specific for detection of vascular lesions, providing accurate predictions in 94.4% of cases. In particular, the CNN had a sensitivity and specificity of 97.1% and 95.3%, respectively, for detection of red spots. Detection of angiectasia/varices occurred with a sensitivity of 94.1% and a specificity of 95.1%. The CNN had a frame reading rate of 145 frames/sec. Conclusions The developed algorithm is the first CNN-based model to accurately detect and distinguish enteric vascular lesions with different hemorrhagic risk. CNN-assisted CE reading may improve the diagnosis of these lesions and overall CE efficiency.

Deep learning and capsule endoscopy: Automatic multi-brand and multi-device panendoscopic detection of vascular lesions

“…However, most studies focus on the development of deep learning models in automatic identification of a specific type of lesion in either the small bowel or the colon. In the small bowel, there are very accurate deep learning models capable of detecting different types of vascular lesions, as well as predicting their bleeding risk accuracy 14 . In the colon, although the vast majority of retrospective studies focus on the detection of protruding lesions, there are already published AI algorithms not only for automatic detection of blood or hematic residues 20 .…”

“…Many articles have been published using this type of artificial intelligence (AI) system in different image-base procedures, including CE. Currently, there is published research in the field of automatic detection of vascular lesions during CE, in the small bowel, which has high overall accuracy 11 12 13 14 15 . These algorithms can not only identify different types of vascular lesions (red spots, angiectasia and/or varices), but also predict their likelihood of bleeding, according to Saurin classification 14 16 .…”

“…Currently, there is published research in the field of automatic detection of vascular lesions during CE, in the small bowel, which has high overall accuracy 11 12 13 14 15 . These algorithms can not only identify different types of vascular lesions (red spots, angiectasia and/or varices), but also predict their likelihood of bleeding, according to Saurin classification 14 16 . Nonetheless, no studies on panendoscopic assessment of vascular lesions have been published.…”

The optimal use of colon capsule endoscopes in clinical practice

“…28 Ribeiro et al developed an AI algorithm for detecting blood in CCE images. 29 They retrospectively extracted greater than 5,000 CCE pictures used to train and validate the CNN, achieving a sensitivity of 99.8% and specificity of 93.2% for the detection of blood. In another study, the same authors found a deep learning algorithm capable of detecting protruding lesions in CCE images with a sensitivity of 90.7% and specificity of 92.6%.…”

Deep Learning and Capsule Endoscopy: Automatic Panendoscopic Detection of Vascular Lesions

“…This study aimed to develop and test a Convolutional Neural Network (CNN)-based model for panendoscopic automatic detection of vascular lesions during CE. 1 2 3 4 5 6 7 8 9…”