Artificial intelligence technique in detection of early esophageal cancer

• Published in: World journal of gastroenterology : WJG Vol. 26; no. 39; pp. 5959 - 5969
• Main Authors: Huang, Lu-Ming, Yang, Wen-Juan, Huang, Zhi-Yin, Tang, Cheng-Wei, Li, Jing
• Format: Journal Article
• Language: English
• Published: United States Baishideng Publishing Group Inc 21.10.2020
• Subjects: Artificial Intelligence; Barrett Esophagus - diagnosis; Esophageal Neoplasms - diagnosis; Esophagoscopy; Humans; Minireviews; Neural Networks, Computer; Prospective Studies; Esophageal squamous cell carcinoma; Pathological diagnosis; Endoscopic diagnosis; Artificial intelligence; Early esophageal cancer; Barrett's esophagus
• ISSN: 1007-9327; 2219-2840; 2219-2840
• DOI: 10.3748/wjg.v26.i39.5959

Due to the rapid progression and poor prognosis of esophageal cancer (EC), the early detection and diagnosis of early EC are of great value for the prognosis improvement of patients. However, the endoscopic detection of early EC, especially Barrett's dysplasia or squamous epithelial dysplasia, is difficult. Therefore, the requirement for more efficient methods of detection and characterization of early EC has led to intensive research in the field of artificial intelligence (AI). Deep learning (DL) has brought about breakthroughs in processing images, videos, and other aspects, whereas convolutional neural networks (CNNs) have shone lights on detection of endoscopic images and videos. Many studies on CNNs in endoscopic analysis of early EC demonstrate excellent performance including sensitivity and specificity and progress gradually from image analysis for classification to real-time detection of early esophageal neoplasia. When AI technique comes to the pathological diagnosis, borderline lesions that are difficult to determine may become easier than before. In gene diagnosis, due to the lack of tissue specificity of gene diagnostic markers, they can only be used as supplementary measures at present. In predicting the risk of cancer, there is still a lack of prospective clinical research to confirm the accuracy of the risk stratification model.