Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video)

Abstract

Background and Aims: The visual detection of early esophageal neoplasia (high-grade dysplasia and T1 cancer) in Barrett's esophagus (BE) with white-light and virtual chromoendoscopy still remains challenging. The aim of this study was to assess whether a convolutional neural artificial intelligence network can aid in the recognition of early esophageal neoplasia in BE. Methods: Nine hundred sixteen images from 65 patients of histology-proven early esophageal neoplasia in BE containing high-grade dysplasia or T1 cancer were collected. The area of neoplasia was masked using image annotation software. Nine hundred nineteen control images were collected of BE without high-grade dysplasia. A convolutional neural network (CNN) algorithm was pretrained on ImageNet and then fine-tuned with the goal of providing the correct binary classification of “dysplastic” or “nondysplastic.” We developed an object detection algorithm that drew localization boxes around regions classified as dysplasia. Results: The CNN analyzed 458 test images (225 dysplasia and 233 nondysplasia) and correctly detected early neoplasia with sensitivity of 96.4%, specificity of 94.2%, and accuracy of 95.4%. With regard to the object detection algorithm for all images in the validation set, the system was able to achieve a mean average precision of. 7533 at an intersection over union of. 3 Conclusions: In this pilot study, our artificial intelligence model was able to detect early esophageal neoplasia in BE images with high accuracy. In addition, the object detection algorithm was able to draw a localization box around the areas of dysplasia with high precision and at a speed that allows for real-time implementation.