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. Although the application of artificial intelligence (AI) is growing exponentially in various imaged-based gastroenterology procedures, there is still a lack of evidence of the AI's technical feasibility and clinical applicability of DAE. Convolutional Neural Networks (CNN) are a multi-layer AI architecture with high-performance levels for image analysis.
Aims & Methods
This study aimed to develop and test a multi-brand CNN-based algorithm for automatically detecting multiple lesions in DAE.
A unicentric retrospective 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). A training dataset comprising 80% of the total pool of images (n=8964) was used to develop the network. The output provided by the network was compared to a consensus classification provided. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the curve precision–recall curve (AUC-PR).
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 component for automatic 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. The CNN analyzed images at approximately 239 frames per second, enabling AI live application in a real-life clinical setting in DAE.
Conclusion
The authors have developed a pioneer combined deep learning system for automatically detecting a wide range of lesions in different gastrointestinal tract segments. To our knowledge, this is the first study regarding the automatic multi-brand panendoscopic detection using Olympus PowerSpiral Motorized Enteroscope, overcoming a relevant interoperability challenge. Our results highlight that using a CNN to detect pleomorphic lesions is associated with high overall accuracy. The development of these tools may enhance the diagnostic yield of device-assisted enteroscopy in the setting of AI applications in digestive endoscopy, particularly for panendoscopic evaluation.
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