Pan Yuwei, He Lanying, Chen Weiqing, Yang Yongtao
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.
Front Oncol. 2023 May 24;13:1198941. doi: 10.3389/fonc.2023.1198941. eCollection 2023.
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.
食管鳞状细胞癌（ESCC）是一种常见的消化道恶性肿瘤。在食管癌高发地区，减轻疾病负担的最有效方法是通过筛查预防疾病发展为浸润性癌。内镜筛查是ESCC早期诊断和治疗的关键。然而，由于内镜医师专业水平参差不齐，仍有许多病例因未能识别病变而漏诊。近年来，随着基于深度机器学习的医学成像和视频评估技术取得显著进展，人工智能（AI）的发展有望为早期ESCC的内镜诊断和治疗提供新的辅助方法。深度学习模型中的卷积神经网络（CNN）使用连续卷积层提取输入图像数据的关键特征，然后通过全连接层对图像进行分类。CNN广泛应用于医学图像分类，大大提高了内镜图像分类的准确性。本文综述聚焦于多种成像模式下AI辅助早期ESCC诊断及早期ESCC浸润深度预测。AI出色的图像识别能力适用于ESCC的检测和诊断，可减少漏诊并帮助内镜医师更好地完成内镜检查。然而，AI系统训练数据集中使用的选择偏倚会影响其通用性。
