Thoracic Aorta Diameter Calculation by Artificial Intelligence Can Predict the Degree of Arterial Stiffness

Yaoling Wang; Jinrong Yang; Yichen Lu; Wenliang Fan; Lijuan Bai; Zhuang Nie; Ruiyun Wang; Jie Yu; Lihua Liu; Yun Liu; Linfeng He; Kai Wen; Li Chen; Fan Yang; Benling Qi

doi:10.3389/fcvm.2021.737161

Thoracic Aorta Diameter Calculation by Artificial Intelligence Can Predict the Degree of Arterial Stiffness

Front Cardiovasc Med. 2021 Dec 15:8:737161. doi: 10.3389/fcvm.2021.737161. eCollection 2021.

Authors

Yaoling Wang¹, Jinrong Yang², Yichen Lu³, Wenliang Fan², Lijuan Bai¹, Zhuang Nie², Ruiyun Wang¹, Jie Yu², Lihua Liu¹, Yun Liu¹, Linfeng He¹, Kai Wen⁴, Li Chen⁵, Fan Yang², Benling Qi¹

Affiliations

¹ Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
³ Siemens Healthineers Digital Technology (Shanghai) Co., Ltd., Shanghai, China.
⁴ School of Software and Microelectronics, Peking University, Beijing, China.
⁵ Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States.

Abstract

Background: Arterial aging is characterized by decreased vascular function, caused by arterial stiffness (AS), and vascular morphological changes, caused by arterial dilatation. We analyzed the relationship of pre-AS and AS, as assessed by cardio ankle vascular index (CAVI), with arterial diameters (AD) at nine levels, from the aortic sinus to the abdominal aorta, as measured by artificial intelligence (AI) on non-enhanced chest computed tomography (CT) images. Methods: Overall, 801 patients who underwent both chest CT scan and arterial elasticity test were enrolled. Nine horizontal diameters of the thoracic aorta (from the aortic sinuses of Valsalva to the abdominal aorta at the celiac axis origin) were measured by AI using CT. Patients were divided into non-AS (mean value of the left and right CAVIs [M.CAVI] < 8), pre-AS (8 ≤ M.CAVI < 9), and AS (M.CAVI ≥ 9) groups. We compared AD differences among groups, analyzed the correlation of age, ADs, and M.CAVI or the mean pressure-independent CAVI (M.CAVI₀), Furthermore, we evaluated the risk predictors and the diagnostic value of the nine ADs for pre-AS and AS. Results: The AD at mid descending aorta (MD) correlated strongest with CAVI (r = 0.46, p < 0.001) or M.CAVI₀ (r = 0.42, p < 0.001). M.CAVI was most affected by the MD AD and by age. An increase in the MD AD independently predicted the occurrence of pre-AS or AS. For MD AD, every 4.37 mm increase caused a 14% increase in the pre-AS and AS risk and a 13% increase in the AS risk. With a cut-off value of 26.95 mm for the MD AD, the area under the curve (AUC) for identifying the risk of AS was 0.743. With a cut-off value of 25.15 mm, the AUC for identifying the risk of the stage after the prophase of AS is 0.739. Conclusions: Aging is associated with an increase in AD and a decrease in arterial elasticity. An increase in AD, particularly at the MD level is an independent predictor of AS development.

Keywords: arterial diameter; arterial stiffness; artificial intelligence; cardio-ankle vascular index; chest computed tomography.