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  redictions 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 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.

Keywords Capsule endoscopy, artificial intelligence, convolutional neural network, vascular lesions, gastrointestinal bleeding

Ann Gastroenterol 2021; 34 (6): 820-828