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

Abstract

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.

Recent Advances for Global Perspectives on Etiology, Pathophysiology, Clinical Presentations, and Management of Moyamoya Disease

“…MMD encompasses both ischemic and hemorrhagic blood types, with ischemic MMD being more common in children and cerebral hemorrhage being predominantly observed in adults. The mortality rate of hemorrhagic MMD is reported to be higher compared to ischemic MMD [11]. Henceforth, objective of this review is to explore the lesser-known causes of moyamoya disease, focusing on recent advancements in understanding its pathophysiology, available treatment options, and potential future directions in this era of significant scientific breakthroughs.…”