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

• Type: Article in International Scientific Journal
• Date: 2023-09-21

Authors

Helder Cardoso (Author) FMUP; Parente, MPL (Author) FEUP

Journal

Title: Revista Espanola de Enfermedades Digestivas

Vol. 115

Pages: 75-79

ISSN: 1130-0108

Publisher: Aran Ediciones SA

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-00W-GPS

DOI: 10.17235/reed.2021.7979/2021

Abstract (EN): Background and aims: capsule endoscopy (CE) revolution-ized the study of the small intestine. Nevertheless, reviewing CE images is time-consuming and prone to error. Artificial intelligence algorithms, particularly convolutional neural networks (CNN), are expected to overcome these drawbacks. Protruding lesions of the small intestine exhibit enormous morphological diversity in CE images. This study aimed to develop a CNN-based algorithm for the automatic detection small bowel protruding lesions.Methods: a CNN was developed using a pool of CE im-ages containing protruding lesions or normal mucosa from 1,229 patients. A training dataset was used for the development of the model. The performance of the network was evaluated using an independent dataset, by calculating its sensitivity, specificity, accuracy, positive and negative pre-dictive values.Results: a total of 18,625 CE images (2,830 showing protrud-ing lesions and 15,795 normal mucosa) were included. Train-ing and validation datasets were built with an 80 %/20 % distribution, respectively. After optimizing the architecture of the network, our model automatically detected small-bowel protruding lesions with an accuracy of 92.5 %. CNN had a sensitivity and specificity of 96.8 % and 96.5 %, respectively. The CNN analyzed the validation dataset in 53 seconds, at a rate of approximately 70 frames per second.Conclusions: we developed an accurate CNN for the auto-matic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5