基于深度学习的人工智能技术在早期胃癌诊断中的应用

Artificial intelligence based on deep learning for automatic detection of early gastric cancer

目的构建和验证一个用于早期胃癌自动识别的深度学习模型，旨在提高早期胃癌的识别和诊断水平。方法从长海医院消化内镜中心数据库选取2014年5月至2016年12月期间5159张胃镜图像，其中包括早期胃癌1000张，良性病变及正常图像4159张。首先选取4449张图像（其中早期胃癌图像768张，其他良性病变及正常图像3681张）用于深度学习模型的训练。然后将剩余的710张图像用于模型的验证，同时再交给4名内镜医师进行诊断。最后统计相关结果。结果深度学习模型用于早期胃癌诊断的准确率89．4％（635／710）、敏感度88．8％（206／232）、特异度89．7％（429／478），每张图像的诊断时间为（0．30±0．02）s，均优于相比较的4名内镜医师。结论本研究构建的深度学习模型用于早期胃癌的诊断具有较高的准确率、特异度和敏感度，可在胃镜检查中辅助内镜医师进行实时诊断。

Objective To develop and validate a model based on deep learning for automatic diagnosis of early gastric cancer （EGC） to improve detection and diagnosis of EGC. Methods A total of 5 159 images （including 1 000 images of EGC and 4 159 images of other benign lesions or normal patients） obtained from May 2014 to December 2016 were collected from endoscopic database in changhai Hospital. Then 4 449 images were selected randomly for a deep convolutional neural network （CNN） training, of which 768 were diagnosed as EGC and 3 681 diagnosed as other benign lesions or normal. The remaining 710 images were used to test the model by comparing with diagnostic results of four endoscopists. Results The deep learning model showed accuracy of 89.4% （ 635/710 ）, sensitivity of 88. 8% （ 206/232 ） and specificity of 89. 7% （429/478） for EGC. The mean time required for diagnosis was 0. 30 ± 0. 02 s. The performance of the model was superior to that of four endoscopists. Conclusion The model based on deep learning has high accuracy, sensitivity and specificity for detecting EGC, which could assist endoscopists in real-time diagnosis.