• Y P Zhou, S Li, X X Zhang, Z D Zhang, Y X Gao, L Ding, and Y Lu.
• Department of Gastrointestinal Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China.
• Zhonghua Wai Ke Za Zhi. 2019 Feb 1; 57 (2): 108-113.

AbstractObjective: To investigate the clinical significance of high definition (HD) MRI rectal lymph node aided diagnostic system based on deep neural network. Methods: The research selected 301 patients with rectal cancer who underwent pelvic HD MRI and reported pelvic lymph node metastasis from July 2016 to December 2017 in Affiliated Hospital of Qingdao University. According to the chronological order, the first 201 cases were used as learning group. The remaining 100 cases were used as verification group. There were 149 males (74.1%) and 52 females in the study group, with an average age of 58.8 years. There were 76 males (76.0%) and 24 females in the validation group, with an average age of 60.2 years. Firstly, Using deep learning technique, researchers trained the 12 060 HD MRI lymph nodes images data of learning group with convolution neural network to simulate the judgment process of radiologists, and established an artificial intelligence automatic recognition system for metastatic lymph nodes of rectal cancer. Then, 6 030 images of the validation group were clinically validated. Artificial intelligence and radiologists simultaneously diagnosed all cases of HD MRI images and made the diagnosis results of metastatic lymph node. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to compare the diagnostic level of them. Results: After continuous iteration training of the learning group data, the loss function value of artificial intelligence decreased continuously, and the diagnostic error decreased continuously. Among the 6 030 images of verification group, 912 images were considered to exist metastatic lymph nodes in radiologists' diagnosis and 987 in artificial intelligence diagnosis. There were 772 images having identical diagnostic results of lymph node location and number of metastases with the two methods. Compared with manual diagnosis, the AUC of the intelligent platform was 0.886 2, the diagnostic time of a single case was 10 s, but the average diagnostic time of doctors was 600 s. Conclusion: The HD MRI lymph node automatic recognition system based on deep neural network has high accuracy and high efficiency, and has the clinical significance of auxiliary diagnosis.

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