P469 Deep learning and minimally invasive endoscopy – panendoscopic detection of pleomorphic lesions

Abstract Background Capsule endoscopy is a minimally invasive exam capable of performing a panendoscopic evaluation of the gastrointestinal tract. Nevertheless, capsule endoscopy is a time-consuming exam, revealing suboptimal diagnostic yield when considering the upper gastrointestinal tract. Convolutional neural network are models based on the human visual cortex architecture, suitable for image analysis. However, there is still an absence of studies about their role in capsule panendoscopy. Methods Our group developed a deep Learning model for panendoscopic automatic detection of pleomorphic lesions (namely vascular lesions, protuberant lesions, hematic residues, ulcers and erosions). In order to develop the convolutional neural network, 355 110 images (6 977 oesophageal, 12 918 gastric, 258 443 enteric, 76 772 colonic) from eight different capsule endoscopy and colon capsule endoscopy devices were divided in a training and validation dataset in a patient split design. The model’s output was compared to three CE experts’ classification. The model performance was evaluated by its sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the precision-recall curve. Results The panendoscopic convolutional neural network was composed by organ-specific neural networks. The binary oesophagus convolutional neural network had a diagnostic accuracy for pleomorphic lesions of 83.6%. The binary gastric network identified pleomorphic lesions with a 96.6% accuracy. The undenary small bowel convolutional neural network distinguished pleomorphic lesions with different potential with 97.6% accuracy. The trinary colonic model (detection and differentiation of normal mucosa, pleomorphic lesions and hematic residues) had 94.9% global accuracy. Conclusion Our group developed the first deep learning model for panendoscopic automatic detection of pleomorphic lesions in both small bowel and colon capsule endoscopy devices from multiple brands, solving a critical interoperability technological challenge. Deep learning-based tools may change the landscape of minimally invasive capsule panendoscopy.