Recognition of moyamoya disease and its hemorrhagic risk using deep learning algorithms: sourced from retrospective studies
Yu Lei1
Xin Zhang1
Wei Ni1
Heng Yang1
Jia-Bin Su1
Bin Xu1
Liang Chen1
Jin-Hua Yu2
Yu-Xiang Gu1
Ying Mao1
1.Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China2.Department of Electronic Engineering, Fudan University, Shanghai, China
摘要：Although intracranial hemorrhage in moyamoya disease can occur repeatedly, predicting the disease is difficult. Deep learning algorithms developed in recent years provide a new angle for identifying hidden risk factors, evaluating the weight of different factors, and quantitatively evaluating the risk of intracranial hemorrhage in moyamoya disease. To investigate whether convolutional neural network algorithms can be used to recognize moyamoya disease and predict hemorrhagic episodes, we retrospectively selected 460 adult unilateral hemispheres with moyamoya vasculopathy as positive samples for diagnosis modeling, including 418 hemispheres with moyamoya disease and 42 hemispheres with moyamoya syndromes. Another 500 hemispheres with normal vessel appearance were selected as negative samples. We used deep residual neural network (ResNet-152) algorithms to extract features from raw data obtained from digital subtraction angiography of the internal carotid artery, then trained and validated the model. The accuracy, sensitivity, and specificity of the model in identifying unilateral moyamoya vasculopathy were 97.64 ± 0.87％, 96.55 ± 3.44％, and 98.29 ± 0.98％, respectively. The area under the receiver operating characteristic curve was 0.990. We used a combined multi-view conventional neural network algorithm to integrate age, sex, and hemorrhagic factors with features of the digital subtraction angiography. The accuracy of the model in predicting unilateral hemorrhagic risk was 90.69 ± 1.58％ and the sensitivity and specificity were 94.12 ± 2.75％ and 89.86 ± 3.64％, respectively. The deep learning algorithms we proposed were valuable and might assist in the automatic diagnosis of moyamoya disease and timely recognition of the risk for re-hemorrhage. This study was approved by the Institutional Review Board of Huashan Hospital, Fudan University, China (approved No. 2014-278) on January 12, 2015.
机标关键词：
分类号：R445(诊断学)R743.34(神经病学与精神病学)Q-334(生物学实验与生物学技术)
论文发表日期：2021-05-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：6( 830-835 )
英文信息