Miguel Mascarenhas, MD1, João PL. Afonso, MD2, Tiago Ribeiro, MD1, Pedro Cardoso, MD1, Francisco Mendes, MD1, Ana Andrade, MD1, Hélder Cardoso, MD2, João Ferreira, PhD3, Guilherme Macedo, MD, PhD2 1Centro Hospitalar São João, Porto, Porto, Portugal; 2Centro Hospitalar de São João, Porto, Porto, Portugal; 3University of Porto-FEUP, Porto, Porto, Portugal
Introduction: Device-assisted enteroscopy (DAE) allows deep exploration of the gastrointestinal (GI) tract, combining its diagnostic abilities with tissue sampling and the application of endoscopic therapy.
This study aimed to develop and test the first multi-brand and multi-device Convolutional Neural Network CNN-based algorithm for detecting and differentiating multiple lesions in DAE.
Methods: A unicentric model AI model development study was conducted to develop a CNN based on 258 DAE exams. DAE was performed by two experienced endoscopists using the double-balloon enteroscopy system Fujifilm EN-580T (n = 152), the single-balloon enteroscopy system Olympus EVIS EXERA II SIF-Q180 (n = 98), and the Olympus PowerSpiral Motorized Enteroscope PSF-1 (n = 8). A total of 11205 images from different segments of the GI tract (stomach, small bowel, and colon) were included, 2535 images containing protruding lesions (polyps, epithelial tumors, subepithelial lesions, and nodules), 1435 containing blood or hematic residues, 1395 images containing angioectasias and 450 images containing ulcers/erosions. The remaining images showed normal mucosa (n=5390). The output provided by the network was compared to a consensus classification provided by three expert endoscopists, and a patient-split analysis for model validation was performed.
Results: The CNN model for automatic detection of GI protruding lesions identified these lesions with a sensitivity of 97.0%, a specificity of 97.4%, and positive and negative predictive values of 94.6% and 98.6%, respectively. The model for the detection of blood/hematic residues had a sensitivity of 95.8%, a specificity of 97.6%, and positive and negative predictive values of 91.4% and 98.9%, respectively. The CNN's angioectasia model automatically detected angioectasia with a sensitivity of 88.5%, a specificity of 97.1%, and positive and negative predictive values of 88.1% and 97.0%, respectively. Furthermore, the CNN ulcers/erosions model automatically detected ulcers/erosions with a sensitivity of 100%, a specificity of 96.4%, and positive and negative predictive values of 96.8% and 100%, respectively. The algorithm's accuracy was 98.7%. The AUC-PR was 1.00.
Discussion: The authors have developed a pioneer combined deep learning system for the automatic detection and characterization of lesions in DAE. This is the first study regarding the automatic multi-brand and multi-device pan-endoscopic pleomorphic lesion detection in DAE.
Figure: Tiago Ribeiro Attachments Tue, Jun 21, 2022, 12:18 AM to me
Figure 1: 1A – Heatmaps showing features detected by the convolutional neural network. 1B Output obtained from the application of the CNN. A blue bar represents a correct prediction. ALL – lesions; N – normal.
Disclosures:
Miguel Mascarenhas indicated no relevant financial relationships.
João Afonso indicated no relevant financial relationships.
Tiago Ribeiro indicated no relevant financial relationships.
Pedro Cardoso indicated no relevant financial relationships.
Francisco Mendes indicated no relevant financial relationships.
Ana Andrade indicated no relevant financial relationships.
Hélder Cardoso indicated no relevant financial relationships.
João Ferreira indicated no relevant financial relationships.
Guilherme Macedo indicated no relevant financial relationships.
Miguel Mascarenhas, MD1, João PL. Afonso, MD2, Tiago Ribeiro, MD1, Pedro Cardoso, MD1, Francisco Mendes, MD1, Ana Andrade, MD1, Hélder Cardoso, MD2, João Ferreira, PhD3, Guilherme Macedo, MD, PhD2, 53, Deep Learning and Device-Assisted Enteroscopy: Multi-Device and Multi-Brand Detection of Pan-Endoscopic Pleomorphic Lesions, ACG 2023 Annual Scientific Meeting Abstracts. Vancouver, BC, Canada: American College of Gastroenterology.
