Artificial Intelligence and Its Role in Identifying Esophageal Neoplasia.

Abstract

Randomized trials have demonstrated that ablation of dysplastic Barrett's esophagus can reduce the risk of progression to cancer. Endoscopic resection for early stage esophageal adenocarcinoma and squamous cell carcinoma can significantly reduce postoperative morbidity compared to esophagectomy. Unfortunately, current endoscopic surveillance technologies (e.g., high-definition white light, electronic, and dye-based chromoendoscopy) lack sensitivity at identifying subtle areas of dysplasia and cancer. Random biopsies sample only approximately 5% of the esophageal mucosa at risk, and there is poor agreement among pathologists in identifying low-grade dysplasia. Machine-based deep learning medical image and video assessment technologies have progressed significantly in recent years, enabled in large part by advances in computer processing capabilities. In deep learning, sequential layers allow models to transform input data (e.g., pixels for imaging data) into a composite representation that allows for classification and feature identification. Several publications have attempted to use this technology to help identify dysplasia and early esophageal cancer. The aims of this reviews are as follows: (a) discussing limitations in our current strategies to identify esophageal dysplasia and cancer, (b) explaining the concepts behind deep learning and convolutional neural networks using language appropriate for clinicians without an engineering background, (c) systematically reviewing the literature for studies that have used deep learning to identify esophageal neoplasia, and (d) based on the systemic review, outlining strategies on further work necessary before these technologies are ready for "prime-time," i.e., use in routine clinical care.