Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
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DOI：
10.1007/s10120-018-0793-2
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发表时间：
2018-07-01
期刊：
影响因子：
7.4
通讯作者：
Tada, Tomohiro
中科院分区：
文献类型：
--
作者：
Hirasawa, Toshiaki;Aoyama, Kazuharu;Tada, Tomohiro
通过卷积神经网络（cnn）进行深度学习的人工智能图像识别已经得到了极大的改进，并越来越多地应用于医学领域的诊断成像。我们开发了一个可以在内镜图像中自动检测胃癌的CNN。方法基于单镜头多盒检测器（Single Shot MultiBox Detector）架构构建基于cnn的胃癌诊断系统，并对13584张胃癌内镜图像进行训练。为了评估诊断的准确性，将连续69例77个胃癌病变患者的2296张胃图像独立测试集应用于构建的CNN。结果CNN对2296张测试图像的分析时间为47 s。在77个胃癌病变中，CNN正确诊断71个，总敏感性为92.2%，其中161个非癌病变被检出为胃癌，阳性预测值为30.6%。71个直径大于或等于6mm的病变中有70个（98.6%）以及所有浸润性肿瘤均被正确检测。所有漏诊病变均为表面凹陷和分化型粘膜内癌，即使经验丰富的内窥镜医师也难以与胃炎区分。近一半的假阳性病变为胃炎，伴有色调改变或粘膜表面不规则。结论所构建的CNN胃癌检测系统可以在很短的时间内处理大量存储的内镜图像，具有临床相关的诊断能力。它可以很好地应用于日常临床实践，以减轻内镜医师的负担。
Background Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images.Methods A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer. To evaluate the diagnostic accuracy, an independent test set of 2296 stomach images collected from 69 consecutive patients with 77 gastric cancer lesions was applied to the constructed CNN.Results The CNN required 47 s to analyze 2296 test images. The CNN correctly diagnosed 71 of 77 gastric cancer lesions with an overall sensitivity of 92.2%, and 161 non-cancerous lesions were detected as gastric cancer, resulting in a positive predictive value of 30.6%. Seventy of the 71 lesions 98.6%) with a diameter of 6 mm or more as well as all invasive cancers were correctly detected. All missed lesions were superficially depressed and differentiated-type intramucosal cancers that were difficult to distinguish from gastritis even for experienced endoscopists. Nearly half of the false-positive lesions were gastritis with changes in color tone or an irregular mucosal surface.Conclusion The constructed CNN system for detecting gastric cancer could process numerous stored endoscopic images in a very short time with a clinically relevant diagnostic ability. It may be well applicable to daily clinical practice to reduce the burden of endoscopists.