基于深度学习的人工智能辅助诊断在食管早癌中的应用

Application of artificial intelligence assisted diagnosis based on deep learning for early esophageal cancer

摘要:

目的 构建人工智能食管早癌辅助诊断系统，旨在提高临床食管早癌的检出率。 方法 收集复旦大学附属中山医院2016年1月至2017年12月共2 400张食管图像，食管早癌、正常食管黏膜各1 200张，对图片中病变位置进行矩形框标记。将其中2 000张图片作为训练集、400张图片作为测试集，通过计算机深度学习中的反向传播算法建立食管早癌的诊断辅助模型。利用测试集来测试训练得到的模型，计算不同截断点系统的灵敏度及特异度，绘制受试者工作特征曲线（ROC曲线），评价诊断模型的性能指标。 结果 辅助诊断模型的ROC曲线下面积（AUC）值达0996 1，灵敏度及特异度均令人满意。 结论 本研究构建的深度学习模型用于食管早癌的诊断具有较好的特异度、敏感度和AUC值，可在临床检查中辅助内镜医师进行实时诊断。

Abstract:

Objective To improve the detection rate of early esophageal cancer during endoscopy by construction of artificial intelligence assistant diagnosis system. Methods A total of 2 400 esophageal images were collected from Zhongshan Hospital of Fudan University from January 2016 to December 2017, including 1 200 images of early esophageal cancer and 1 200 images of normal esophageal mucosa. The lesions in pictures were marked with rectangular box by using computer program. Among them, 2 000 pictures were divided into the training set and 400 pictures into the test set. An assistant diagnostic model of early esophageal cancer was established by back propagation algorithm in computer deep learning. The training model was tested and the sensitivity and specificity of the system at different cut-off points in the test set was calculated. Receiver operating characteristic (ROC) curve was used to evaluate the performance of the diagnostic model. Results The area under ROC curve (AUC) of the auxiliary diagnostic model was 0996 1. The sensitivity and specificity were satisfactory. Conclusion The deep learning model constructed in this study has good specificity, sensitivity and AUC value in the diagnosis of early esophageal cancer, and can assist endoscopists in real-time diagnosis in clinical examination.