Artificial Intelligence and Device-Assisted Enteroscopy: Automatic Detection of Enteric Protruding Lesions Using a Convolutional Neural Network

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

Cardoso, P (Autor) Outra; Saraiva, MM (Autor) Outra; Afonso, J (Autor) Outra; Ribeiro, T (Autor) Outra; Andrade, P (Autor) Outra; Ferreira, J (Autor) FEUP; Cardoso, H (Autor) Outra; Macedo G (Autor) FMUP

Revista

Título: Clinical and Translational Gastroenterology

Vol. 15

ISSN: 2155-384X

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 8 Citações

Scopus - 8 Citações

Outras Informações

ID Authenticus: P-00X-54X

DOI: 10.14309/ctg.0000000000000514

Abstract (EN): INTRODUCTION:Device-assisted enteroscopy (DAE) plays a major role in the investigation and endoscopic treatment of small bowel diseases. Recently, the implementation of artificial intelligence (AI) algorithms to gastroenterology has been the focus of great interest. Our aim was to develop an AI model for the automatic detection of protruding lesions in DAE images.METHODS:A deep learning algorithm based on a convolutional neural network was designed. Each frame was evaluated for the presence of enteric protruding lesions. The area under the curve, sensitivity, specificity, and positive and negative predictive values were used to assess the performance of the convolutional neural network.RESULTS:A total of 7,925 images from 72 patients were included. Our model had a sensitivity and specificity of 97.0% and 97.4%, respectively. The area under the curve was 1.00.DISCUSSION:Our model was able to efficiently detect enteric protruding lesions. The development of AI tools may enhance the diagnostic capacity of deep enteroscopy techniques.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 3