The current state of artificial intelligence in endoscopic diagnosis of early esophageal squamous cell carcinoma

Frontiers in Oncology

Oncology

Yuwei Pan¹  Lanying He¹  Yongtao Yang²  Weiqing Chen² 

[1] Department of Gastroenterology, Chongqing University Cancer Hospital, Chongqing, China;Department of Gastroenterology, Chongqing University Cancer Hospital, Chongqing, China;Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China;

关键词: artificial intelligence;    convolutional neural network;    endoscopy;    esophageal squamous cell carcinoma;    diagnosis;   

DOI  :  10.3389/fonc.2023.1198941

 received in 2023-04-02, accepted in 2023-05-16,  发布年份 2023

来源: Frontiers

【 摘 要 】

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the digestive tract. The most effective method of reducing the disease burden in areas with a high incidence of esophageal cancer is to prevent the disease from developing into invasive cancer through screening. Endoscopic screening is key for the early diagnosis and treatment of ESCC. However, due to the uneven professional level of endoscopists, there are still many missed cases because of failure to recognize lesions. In recent years, along with remarkable progress in medical imaging and video evaluation technology based on deep machine learning, the development of artificial intelligence (AI) is expected to provide new auxiliary methods of endoscopic diagnosis and the treatment of early ESCC. The convolution neural network (CNN) in the deep learning model extracts the key features of the input image data using continuous convolution layers and then classifies images through full-layer connections. The CNN is widely used in medical image classification, and greatly improves the accuracy of endoscopic image classification. This review focuses on the AI-assisted diagnosis of early ESCC and prediction of early ESCC invasion depth under multiple imaging modalities. The excellent image recognition ability of AI is suitable for the detection and diagnosis of ESCC and can reduce missed diagnoses and help endoscopists better complete endoscopic examinations. However, the selective bias used in the training dataset of the AI system affects its general utility.

【 授权许可】

Unknown   
Copyright © 2023 Pan, He, Chen and Yang