Wang Jinglu, Sun Jie, Xu Jingxu, Lu Shiyu, Wang Hao, Huang Chencui, Zhang Fandong, Yu Yizhou, Gao Xiang, Wang Ming, Wang Yu, Ruan Xinzhong, Pan Yuning
Department of Radiology, Ningbo First Hospital, Ningbo, Zhejiang Province, People's Republic of China.
Department of Neurosurgery, Ningbo First Hospital, Ningbo, Zhejiang Province, People's Republic of China.
Acad Radiol. 2023 Nov;30(11):2477-2486. doi: 10.1016/j.acra.2022.12.043. Epub 2023 Feb 1.
Determine the effect of a multiphase fusion deep-learning model with automatic phase selection in detection of intracranial aneurysm (IA) from computed tomography angiography (CTA) images.
CTA images of intracranial arteries from patients at Ningbo First Hospital were retrospectively analyzed. Images were randomly classified as training data, internal validation data, or test data. CTA images from cases examined by digital subtraction angiography (DSA) were examined for independent validation. A deep-learning model was constructed by automatic phase selection of multiphase fusion, and compared to the single-phase algorithm to evaluate algorithm sensitivity.
We analyzed 1110 patients (1493 aneurysms) as training data, 139 patients (174 aneurysms) as internal validation data, and 134 patients (175 aneurysms) as test data. The sensitivity of the multiphase analysis of the internal validation data, test data, and independent validation data were greater than from the single-phase analysis. The recall of the multiphase selection was greater or equal to that of single-phase selection in the aneurysm position, shape, size, and rupture status. Use of the test data to determine the presence and absence of aneurysm rupture led to a recall from multiphase selection of 94.8% and 87.6% respectively; both of these values were greater than those from single-phase selection (89.6% and 79.4%).
A multiphase fusion deep learning model with automatic phase selection provided automated detection of IAs with high sensitivity.
确定具有自动相位选择功能的多相融合深度学习模型在从计算机断层扫描血管造影（CTA）图像中检测颅内动脉瘤（IA）方面的效果。
回顾性分析宁波第一医院患者的颅内动脉CTA图像。图像被随机分类为训练数据、内部验证数据或测试数据。对经数字减影血管造影（DSA）检查的病例的CTA图像进行独立验证。通过多相融合的自动相位选择构建深度学习模型，并与单相算法进行比较以评估算法敏感性。
我们分析了1110例患者（1493个动脉瘤）作为训练数据，139例患者（174个动脉瘤）作为内部验证数据，134例患者（175个动脉瘤）作为测试数据。内部验证数据、测试数据和独立验证数据的多相分析敏感性均高于单相分析。在动脉瘤位置、形状、大小和破裂状态方面，多相选择的召回率大于或等于单相选择。使用测试数据确定动脉瘤破裂的有无，多相选择的召回率分别为94.8%和8
