Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions

Abstract

Background and Objectives: Device-assisted enteroscopy (DAE) has a significant role in approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and can be associated with many nosological entities, namely Crohn’s disease. 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 technical feasibility and clinical applicability of DAE. This study aimed to develop and test a multi-brand convolutional neural network (CNN)-based algorithm for automatically detecting ulcers and erosions in DAE. Materials and Methods: A unicentric retrospective study was conducted for the development of a CNN, based on a total of 250 DAE exams. A total of 6772 images were used, of which 678 were considered ulcers or erosions after double-validation. Data were divided into a training and a validation set, the latter being used for the performance assessment of the model. 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: Sensitivity, specificity, PPV, and NPV were respectively 88.5%, 99.7%, 96.4%, and 98.9%. The algorithm’s accuracy was 98.7%. The AUC-PR was 1.00. The CNN processed 293.6 frames per second, enabling AI live application in a real-life clinical setting in DAE. Conclusion: To the best of our knowledge, this is the first study regarding the automatic multi-brand panendoscopic detection of ulcers and erosions throughout the digestive tract during DAE, overcoming a relevant interoperability challenge. Our results highlight that using a CNN to detect this type of lesion is associated with high overall accuracy. The development of binary CNN for automatically detecting clinically relevant endoscopic findings and assessing endoscopic inflammatory activity are relevant steps toward AI application in digestive endoscopy, particularly for panendoscopic evaluation.

A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination

“…These studies were only based on images and our study also assessed ENDOANGEL-DBE’s performance with videos. Miguel Martins et al [ 51 ] developed a model recognizing erosions and ulcers from normal images, whose sensitivity was higher than ours. Since they did not use an independent test set, the images in their test set and training set might come from the same cases, and using such a test set might lead to higher results than those in the real world.…”

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

“…In spite of the exponential growth in the development of deep learning models for CE [ 44 , 45 ], the application of AI technologies to DAE is still in a premature state, with scarce works applying deep learning models to augment the diagnostic performance of the exam. Additionally, the existing works were focused on detecting a specific type of lesion [ 18 , 19 , 20 ], which guarantees a diminished clinical applicability and a lower technology readiness level (TRL) of the technology. This work constitutes a landmark with the development of a CNN capable of detecting clinically relevant lesions during DAE, namely, vascular and protuberant lesions, hematic residues, ulcers and erosions.…”

“…Nevertheless, the implementation of AI models for DAE is still in the early stages. In fact, AI application in DAE has been studied for the identification of vascular lesions, protuberant lesions, ulcers and erosions [ 18 , 19 , 20 ]. Nevertheless, the clinical application of such technology is dependent on the ability to identify different types of lesions throughout a complete exam, while functioning in different devices.…”

Multiobjective Optimization of Fuzzy System for Cardiovascular Risk Classification

“…The use of artificial intelligence and optimization techniques has shown promising results in various healthcare applications [17][18][19][20][21][22][23][24][25][26][27], including the management of CVDs and related risk factors, offering a valuable tool for improving public health. For example, artificial neural networks (ANNs) have been used to elucidate the connections between olfaction, eating habits, and metabolic disturbances in the health of overweight patients [17].…”

“…Similarly, the use of DL and device-assisted enteroscopy for automatic panendoscopy detection of erosions and ulcers is proposed. The study stakes the potential of deep learning to aid in the diagnosis of gastrointestinal disorders, including those that may contribute to the development of CVD [27].…”