Año 2023 / Volumen 115 / Número 2
Original
Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network

75-79

DOI: 10.17235/reed.2021.7979/2021

Miguel Mascarenhas Saraiva, João Afonso, Tiago Ribeiro, João Ferreira, Hélder Cardoso, Patricia Andrade, Raquel Gonçalves, Pedro Cardoso, Marco Parente, Renato Jorge, Guilherme Macedo,

Resumen
Background and aims: capsule endoscopy (CE) revolutionized 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 images 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 predictive values. Results: a total of 18,625 CE images (2,830 showing protruding lesions and 15,795 normal mucosa) were included. Training 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 automatic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE.
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Bibliografía
1. Pennazio M, Spada C, Eliakim R, et al. Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Clinical Guideline. Endoscopy 2015;47(4):352-76. 10.1055/s-0034-1391855
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Mascarenhas Saraiva M, Afonso J, Ribeiro T, Ferreira J, Cardoso H, Andrade P, et all. Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network. 7979/2021


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Ficha Técnica

Recibido: 17/03/2021

Aceptado: 08/09/2021

Prepublicado: 14/09/2021

Publicado: 07/02/2023

Tiempo de revisión del artículo: 169 días

Tiempo de prepublicación: 181 días

Tiempo de edición del artículo: 692 días


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