目的 分析基于从头训练模式深度学习-卷积神经网络模型［the deep learning convolutional neural network model trained from scratch，DL-CNN（fs）］的人工智能算法评估急性肺动脉血栓栓塞（acute pulmonary thromboembolism，APE）的价值。 方法 回顾性纳入214例可疑APE行CT肺动脉造影（CTPA）的住院患者，包括急性肺动脉血栓栓塞137例，阴性77例。放射科医师根据CTPA图像判断有无APE，并计算Qanadli评分、Mastora评分和其他CTPA参数。采用DL-CNN（fs）训练网络模型自动检测栓子的分布及容积。评估DL-CNN（fs）模型测量血栓分布的价值，计算血栓负荷与Qanadli评分、Mastora评分和其他CTPA参数的相关性。 结果 DL-CNN（fs）测算的中心肺动脉栓子敏感度、特异度、感兴趣区曲线下面积（AUC）分别为100%、16.8%、0.584（95%CI， 0.508 ~ 0.661）；DL-CNN（fs）测算的外周肺动脉栓子敏感度、特异度、AUC均较高（R1-R9，60.8% ~ 95.2%，67.9% ~ 87.1%，0.740 ~ 0.844；L1-L10，64.6% ~ 93.4%，62.7% ~ 83.1%，0.732 ~ 0.791）。DL-CNN（fs）测算的栓子体积与Qanadli score肺栓塞指数显著正相关（r = 0.867，P < 0.001），与Mastora score肺栓塞指数显著正相关（r = 0.854，P < 0.001），与右心室及左心室最大横径比、右心室及左心室最大面积比呈正相关（r = 0.549，0.559，P<0.01）。 结论 DL-CNN（fs）模型检测外周肺动脉栓子具有较高的价值，对中心肺动脉栓子诊断特异度有待进一步提高。DL-CNN（fs）模型自动提供APE患者的栓子体积，可以一定程度反映栓塞程度及右心功能，能够辅助医生对于APE患者血栓负荷及危险分层的快速评估。

Objective This research aimed to study the values of the deep learning convolutional neural network model trained from scratch（DL?CNN（fs）） in assessment of acute pulmonary thromboembolism （APE）. Methods A total of 214 patients with suspected APE who underwent computed tomography pulmonary angiography（CTPA） were retrospectively studied， including 137 patients with APE and 77 patients without APE. The presence or absence of APE was determined by the radiologists based on CTPA. The Qanadli score， Mastora score and other parameters on CTPA were measured by the radiologists. The clot volumes and distribution were measured by U?net model which was based on DL?CNN. The performance of DL?CNN（fs） in measuring clot distribution and clot burden was evaluated. The correlation between clot burden and Qanadli score， Mastora score and other CTPA parameters was calculated. Results Sensitivity， specificity and AUC of the central pulmonary artery clot distribution measured by DL?CNN（fs） were 100%， 16.8%， AUC = 0.584 （95%CI： 0.508 ~ 0.661）. Sensitivity， specificity and AUC of the peripheral pulmonary artery clot distribution were high （R1?R9， 60.8% ~ 95.2%，67.9% ~ 87.1%，0.740 ~ 0.844； L1?L10， 64.6% ~ 93.4%， 62.7% ~ 83.1%， 0.732 ~ 0.791）. Strong positive correlation was noted between clot volumes measured by DL?CNN （fs） model and Qanadli score （r = 0.867，P < 0.001）， as well as Mastora score （r = 0.854， P < 0.001）. Clot volumes measured by DL?CNN （fs） model were correlated with the right ventricular functional parameters（right ventricular diameter/left ventricular diameter， right ventricular area/left ventricular area，r = 0.549， 0.559， P < 0.01）. Conclusion The DL?CNN （fs） model has high value in detecting peripheral pulmonary embolism， and its diagnostic specificity for central pulmonary embolism needs to be further improved. The clot volumes from DL?CNN（fs） were correlated with metrics of pulmonary embolism and right ventricular function， which may help doctors to quickly evaluate the clot burden and risk stratification of acute pulmonary thromboembolism.