Advanced Warning of Aortic Dissection on Non-Contrast CT: The Combination of Deep Learning and Morphological Characteristics

Frontiers in Cardiovascular Medicine

Donggang Jia¹  Li Mao¹  Cheng Wang¹  Xiu-Li Li¹  Judong Pan²  Zhengyu Jin³  Yan Yi³  Yubo Guo³  Yining Wang³  Xiao Luo⁴  Yi Lei⁴  Jiayue Li⁵  Shufang Li⁶ 

[1] AI Lab, Deepwise Healthcare, Beijing, China;Department of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), San Francisco, CA, United States;Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China;Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China;School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States;School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China;

关键词: aortic dissection;    computed tomography angiography;    diagnostic imaging;    multidetector computed tomography;    deep learning;   

DOI  :  10.3389/fcvm.2021.762958

来源: DOAJ

【 摘 要 】

Background: The identification of aortic dissection (AD) at baseline plays a crucial role in clinical practice. Non-contrast CT scans are widely available, convenient, and easy to perform. However, the detection of AD on non-contrast CT scans by radiologists currently lacks sensitivity and is suboptimal.Methods: A total of 452 patients who underwent aortic CT angiography (CTA) were enrolled retrospectively from two medical centers in China to form the internal cohort (341 patients, 139 patients with AD, 202 patients with non-AD) and the external testing cohort (111 patients, 46 patients with AD, 65 patients with non-AD). The internal cohort was divided into the training cohort (n = 238), validation cohort (n = 35), and internal testing cohort (n = 68). Morphological characteristics were extracted from the aortic segmentation. A deep-integrated model based on the Gaussian Naive Bayes algorithm was built to differentiate AD from non-AD, using the combination of the three-dimensional (3D) deep-learning model score and morphological characteristics. The areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to evaluate the model performance. The proposed model was also compared with the subjective assessment of radiologists.Results: After the combination of all the morphological characteristics, our proposed deep-integrated model significantly outperformed the 3D deep-learning model (AUC: 0.948 vs. 0.803 in the internal testing cohort and 0.969 vs. 0.814 in the external testing cohort, both p < 0.05). The accuracy, sensitivity, and specificity of our model reached 0.897, 0.862, and 0.923 in the internal testing cohort and 0.730, 0.978, and 0.554 in the external testing cohort, respectively. The accuracy for AD detection showed no significant difference between our model and the radiologists (p > 0.05).Conclusion: The proposed model presented good performance for AD detection on non-contrast CT scans; thus, early diagnosis and prompt treatment would be available.

【 授权许可】

Unknown