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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023-09-21

Autores

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

Revista

Título: Revista Espanola de Enfermedades Digestivas

Vol. 115

Páginas: 75-79

ISSN: 1130-0108

Editora: Aran Ediciones SA

Indexação

ISI Web of Knowledge - 5 Citações

Scopus - 6 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5