Artificial Intelligence and Panendoscopy-Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy

Simple Summary Device-assisted enteroscopy is the only diagnostic and therapeutic exam capable of exploring the entire gastrointestinal tract. However, the diagnostic yield of this procedure is not sufficient enough to assure a cost-effective panendoscopy, and there is significant interobserver variability during the exam. Artificial intelligence tools have been proved to be beneficial in several areas of medicine, namely in Gastroenterology, with a strong image component. However, the development of deep learning models for application in device-assisted enteroscopy is still in an embryonic phase. The authors herein aimed to develop a multidevice convolutional neural network based on 338 exams performed in two renowned centers. The present model was able to accurately identify multiple clinically relevant lesions across the entire gastrointestinal tract, with an image processing time that favors its clinical applicability. The first worldwide panendoscopic model showed the potential of artificial intelligence in augmenting the accuracy and cost-effectiveness of device-assisted enteroscopy.Abstract Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE's diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm (R), Porto, Portugal), 172 double-balloon enteroscopies (Olympus (R), Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus (R), Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images, n = 36,599) and testing dataset (10% of the images, n = 4066) used to evaluate the model. The CNN's output was compared to an expert consensus classification. The model was evaluated by its sensitivity, specificity, positive (PPV) and negative predictive values (NPV), accuracy and area under the precision recall curve (AUC-PR). The CNN had an 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and an AUC-PR of 0.97. Our group developed the first multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. The development of accurate deep learning models is of utmost importance for increasing the diagnostic yield of DAE-based panendoscopy.