Device-assisted enteroscopy (DAE) plays a major role in the investigation and endoscopic treatment of small bowel diseases. Recently, the implementation of artificial intelligence (AI) algorithms to gastroenterology has been the focus of great interest. Our aim was to develop AI model for automatic detection of protruding lesions (PP) in DAE images.
A deep learning algorithm based on a convolutional neural network (CNN) was designed. Each frame was evaluated for the presence of enteric protruding lesions. The area under the curve (AUC), sensitivity, specificity, positive and negative predictive values were used to assess the performance of the CNN.
A total of 7925 images from 72 patients were included. Our model had a sensitivity and specificity 97.0% and 97.4%, respectively. The AUC was 1.00.
Our model was able to efficiently detect enteric protruding lesions. The development of AI tools may enhance the diagnostic capacity of deep enteroscopy techniques.