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

Authors Tiago Filipe Ribeiro, Miguel Mascarenhas Saraiva, João P.S. Ferreira, Hélder Cardoso, João Afonso, Patrícia Andrade, Marco Parente, Renato Natal Jorge, Guilherme Macedo.


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

Original Articles