Construction of Diagnosis Model of Moyamoya Disease Based on Convolution Neural Network Algorithm

OBJECTIVE: The convolutional neural network (CNN) was used to improve the accuracy of digital subtraction angiography (DSA) in diagnosing moyamoya disease (MMD), providing a new method for clinical diagnosis of MMD. METHODS: A total of 40 diagnosed with MMD by DSA in the neurosurgery department of our hospital were included. At the same time, 40 age-matched and sex-matched patients were selected as the control group. The 80 included patients were divided into training set (n = 56) and validation set (n = 24). The DSA image was preprocessed, and the CNN was used to extract features from the preprocessed image. The precision and accuracy of the preprocessed image results were evaluated. RESULTS: There was no significant difference in baseline data between the training set and validation set (P > 0.05). The precision and accuracy of the images before processing were 79.68% and 81.45%, respectively. After image processing, the precision and accuracy of the model are 96.38% and 97.59%, respectively. The area under the curve of the CNN algorithm model was 0.813 (95% CI: 0.718-0.826). CONCLUSION: This diagnostic method based on CNN performs well in MMD detection.