Abstract
Esophageal cancer has a dismal prognosis and necessitates a multimodal and multidisciplinary approach from diagnosis to treatment. High-definition white-light endoscopy and histopathological confirmation remain the gold standard for the definitive diagnosis of premalignant and malignant lesions. Artificial intelligence using deep learning (DL) methods for image analysis constitutes a promising adjunct for the clinical endoscopist that could effectively decrease BE overdiagnosis and unnecessary surveillance, while also assisting in the timely detection of dysplastic BE and esophageal cancer. A plethora of studies published during the last five years have consistently reported highly accurate DL algorithms with comparable or superior performance compared to endoscopists. Recent efforts aim to expand DL utilization into further aspects of esophageal neoplasia management including histologic diagnosis, segmentation of gross tumor volume, pretreatment prediction and post-treatment evaluation of patient response to systemic therapy and operative guidance during minimally invasive esophagectomy. Our manuscript serves as an introduction to the growing literature of DL applications for image analysis in the management of esophageal neoplasia, concisely presenting all currently published studies. We also aim to guide the clinician across basic functional principles, evaluation metrics and limitations of DL for image recognition to facilitate the comprehension and critical evaluation of the presented studies.