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. 2022 Aug 1;13(8):e00514.
doi: 10.14309/ctg.0000000000000514. Epub 2022 Jul 20.

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

Pedro Cardoso  1   2 , Miguel Mascarenhas Saraiva  1   2   3 , João Afonso  1   2 , Tiago Ribeiro  1   2 , Patrícia Andrade  1   2   3 , João Ferreira  4   5 , Hélder Cardoso  1   2   3 , Guilherme Macedo  1   2   3
Affiliations

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

Pedro Cardoso et al. Clin Transl Gastroenterol. .

Abstract

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.

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Conflict of interest statement

Guarantor of the article: Miguel Mascarenhas Saraiva, MD, MSc.

Specific author contributions: PC and MMS: equal contribution in study design, revision of DAE videos, image extraction, construction, and development of the CNN, drafting of the manuscript; and critical revision of the manuscript. JA: construction and development of the CNN, bibliographic review, and critical revision of the manuscript. TR: bibliographic review, drafting of the manuscript, and critical revision of the manuscript. JF: construction and development of the CNN, statistical analysis, and critical revision of the manuscript. PA, HC, and GM: study design and critical revision of the manuscript. All authors approved the final version of the manuscript.

Financial support: The authors acknowledge Fundação para a Ciência e Tecnologia (FCT) for supporting the computational costs related to this study through CPCA/A0/7363/2020 grant. This entity had no role in the study design, data collection, data analysis, preparation of the manuscript, and publishing decision.

Potential competing interests: None to report.

Figures

Figure 1.
Figure 1.
Output obtained from the application of the convolutional neural network. The bars represent the probability estimated by the network, and the blue bars represent a correct prediction. N, Normal mucosa/other findings; PR, protuberant lesions.
Figure 2.
Figure 2.
Receiver operating characteristic analyses of the network's performance in the detection of protuberant lesions vs normal colonic mucosa/other findings. PR, protuberant lesions.
See this image and copyright information in PMC

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