Recognition of moyamoya disease and its hemorrhagic risk using deep learning algorithms: sourced from retrospective studies.
Recognition of moyamoya disease and its hemorrhagic risk using deep learning algorithms: sourced from retrospective studies.
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使用深度学习算法识别烟雾病及其出血风险：源自回顾性研究
DOI：
10.4103/1673-5374.297085
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发表时间：
2021-05
影响因子：
6.1
通讯作者：
Mao Y
中科院分区：
文献类型：
--
作者：
Lei Y;Zhang X;Ni W;Yang H;Su JB;Xu B;Chen L;Yu JH;Gu YX;Mao Y
尽管烟雾病可反复发生颅内出血，但预测该疾病很困难。近年来发展起来的深度学习算法为识别隐藏的危险因素，评估不同因素的权重，定量评估烟雾病颅内出血的风险提供了一个新的角度。为了研究卷积神经网络算法是否可以用于识别烟雾病和预测出血发作，我们回顾性地选择了460例成人单侧烟雾病脑半球作为诊断建模的阳性样本，其中418例烟雾病脑半球和42例烟雾综合征脑半球。另取500例正常脑半球作为阴性标本。我们使用深度残差神经网络（ResNet-152）算法从颈内动脉数字减影血管造影获得的原始数据中提取特征，然后训练和验证模型。该模型识别单侧烟雾血管病变的准确性、敏感性和特异性分别为97.64 ± 0.87%、96.55 ± 3.44%和98.29 ± 0.98%。受试者工作特征曲线下面积为0.990。我们使用了一种组合的多视图传统神经网络算法，将年龄、性别和出血因素与数字减影血管造影的特征相结合。模型预测单侧出血风险的准确性为90.69 ± 1.58%，敏感性和特异性分别为94.12 ± 2.75%和89.86 ± 3.64%。我们提出的深度学习算法很有价值，可能有助于烟雾病的自动诊断和及时识别再出血的风险。本研究于2015年1月12日获得中国复旦大学附属华山医院伦理委员会批准（批准号：2014-278）。
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.
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影响因子：
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作者：
Lo BW;Macdonald RL;Baker A;Levine MA
通讯作者：
Levine MA
影响因子：
4.1
作者：
Kobayashi, E;Saeki, N;Yamaura, A
通讯作者：
Yamaura, A
影响因子：
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作者：
Ryan RW;Chowdhary A;Britz GW
通讯作者：
Britz GW
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影响因子：
8.3
作者：
Kang, Shuai;Liu, Xingju;Zhao, Ji-zong
通讯作者：
Zhao, Ji-zong
影响因子：
2.4
作者：
Kawaguchi, S;Sakaki, T;Kamada, K
通讯作者：
Kamada, K