A deep neural network improves endoscopic detection of early gastric cancer without blind spots - Wu, Lianlian; Zhou, Wei; Wan, Xinyue; Zhang, Jun; Shen, Lei; Hu, Shan; Ding, Qianshan; Mu, Ganggang; Yin, Anning; Huang, Xu; Liu, Jun; Jiang, Xiaoda; Wang, Zhengqiang; Deng, Yunchao; Liu, Mei; Lin, Rong; Ling, Tingsheng; Li, Peng; Wu, Qi; Jin, Peng; Chen, Jie; Yu, Honggang (Endoscopy, 2019)
Background Gastric cancer is the third most lethal malignancy worldwide. A novel deep convolution neural network (DCNN) to perform visual tasks has been recently developed. The aim of this study was to build a system using the DCNN to detect early gastric cancer (EGC) without blind spots during esophagogastroduodenoscopy (EGD). Methods 3170 gastric cancer and 5981 benign images were collected to train the DCNN to detect EGC. A total of 24549 images from different parts of stomach were collected to train the DCNN to monitor blind spots. Class activation maps were developed to automatically cover suspicious cancerous regions. A grid model for the stomach was used to indicate the existence of blind spots in unprocessed EGD videos. Results The DCNN identified EGC from non-malignancy with an accuracy of 92.5 %, a sensitivity of 94.0 %, a specificity of 91.0 %, a positive predictive value of 91.3 %, and a negative predictive value of 93.8 %, outperforming all levels of endoscopists. In the task of classifying gastric locations into 10 or 26 parts, the DCNN achieved an accuracy of 90 % or 65.9 %, on a par with the performance of experts. In real-time unprocessed EGD videos, the DCNN achieved automated performance for detecting EGC and monitoring blind spots. Conclusions We developed a system based on a DCNN to accurately detect EGC and recognize gastric locations better than endoscopists, and proactively track suspicious cancerous lesions and monitor blind spots during EGD.