Abstract：Objective To explore the apparent diffusion coefficient (ADC) image characteristic and the degree of liver fibrosis, by
the analysis of convolutional neural network based on deep learning automatic detection method for MRI of the ADC image.
Methods A total of 123 patients with chronic hepatitis B were selected as research subjects. 5 groups of hepatic fibrosis stages (F0~F4)
were determined according to liver biopsy. The ADC images were analyzed using 5-layer depth convolutional neural network
structure, and the ADC values were measured by MRI workstation, and the two methods were compared for cross-validation. Results
The accuracy, sensitivity, specificity, precision, F1 value, Matthews correlation coefficient (MCC) and Fowlkes–Mallows index (FMI)
of ADC images acquired by depth learning were 88.13%±1.47%, 81.45%±3.69%, 91.12%±1.72%, 80.49%±2.94%, 80.90%±
2.39%, 72.36%±3.39% and 80.94%±2.37%. The accuracy, sensitivity, specificity, precision, F1, MCC and FMI of ADC values
measured by MRI workstation were 75.07%±13.35%, 90.03%±9.24%, 42.67%±35.42%, 78.44%±11.42%, 83.30%±8.18%,
16.00%±60.46% and 83.77%±7.98%. The accuracy of 5-layer deep convolutional neural network was significantly higher than
that of ADC measured by MRI workstation (P<0.01). Conclusion The accuracy of ADC value obtained by the deep learning
convolutional neural network automatic detection method is better than that obtained by MRI measurement method. The deep
learning convolutional neural network automatic detection method has a high diagnostic value for chronic hepatitis B liver fibrosis.
朱桂娟，张鑫，叶晓航，李锋. 基于磁共振ADC图像的深度学习和ADC值评估慢性乙型肝炎肝纤维化程度价值比较[J]. 中国医疗设备, 2023, 38(2): 12-16.
ZHU Guijuan, ZHANG Xin, YE Xiaohang, LI Feng. Comparison of Deep Learning Based on Magnetic Resonance ADC Image and ADC Value in
Evaluating the Degree of Liver Fibrosis in Chronic Hepatitis B. China Medical Devices, 2023, 38(2): 12-16.
