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

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.

SUBMITTER: Wang Y

PROVIDER: S-EPMC8714774 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesProgressive root dilatation is an important complication in patients with transposition of the great arteries (TGA) after arterial switch operation (ASO) that may be caused by altered flow dynamics. Aortic wall shear stress (WSS) distribution at rest and under dobutamine stress (DS) conditions using 4D flow magnetic resonance imaging were investigated in relation to thoracic aorta geometry.Methods4D flow magnetic resonance imaging was performed in 16 adolescent TGA patients after ASO (rest and DS condition) and in 10 healthy controls (rest). The primary outcome measure was the WSS distribution along the aortic segments and the WSS change with DS in TGA patients. Based on the results, we secondary zoomed in on factors [aortic geometry and left ventricular (LV) function parameters] that might relate to these WSS distribution differences. Aortic diameters, arch angle, LV function parameters (stroke volume, LV ejection fraction, cardiac output) and peak systolic aortic WSS were obtained.ResultsTGA patients had significantly larger neoaortic root and smaller mid-ascending aorta (AAo) dimensions and aortic arch angle. At rest, patients had significantly higher WSS in the entire thoracic aorta, except for the dilated root. High WSS levels beyond the proximal AAo were associated with the diameter decrease from the root to the mid-AAo (correlation coefficient r = 0.54-0.59, P = 0.022-0.031), not associated with the aortic arch angle. During DS, WSS increased in all aortic segments (P < 0.001), most pronounced in the AAo segments. The increase in LV ejection fraction, stroke volume and cardiac output as a result of DS showed a moderate linear relationship with the WSS increase in the distal AAo (correlation coefficient r = 0.54-0.57, P = 0.002-0.038).ConclusionsIncreased aortic WSS was observed in TGA patients after ASO, related to the ASO-specific geometry, which increased with DS. Stress-enhanced elevated WSS may play a role in neoaortic root dilatation and anterior aortic wall thinning of the distal AAo.

Project description:BackgroundArtificial intelligence-enabled electrocardiogram has become a substitute tool for echocardiography in left ventricular ejection fraction estimation. However, the direct use of artificial intelligence-enabled electrocardiogram may be not trustable due to the uncertainty of the prediction.ObjectiveThe study aimed to establish an artificial intelligence-enabled electrocardiogram with a degree of confidence to identify left ventricular dysfunction.MethodsThe study collected 76,081 and 11,771 electrocardiograms from an academic medical center and a community hospital to establish and validate the deep learning model, respectively. The proposed deep learning model provided the point estimation of the actual ejection fraction and its standard deviation derived from the maximum probability density function of a normal distribution. The primary analysis focused on the accuracy of identifying patients with left ventricular dysfunction (ejection fraction ≤ 40%). Since the standard deviation was an uncertainty indicator in a normal distribution, we used it as a degree of confidence in the artificial intelligence-enabled electrocardiogram. We further explored the clinical application of estimated standard deviation and followed up on the new-onset left ventricular dysfunction in patients with initially normal ejection fraction.ResultsThe area under receiver operating characteristic curves (AUC) of detecting left ventricular dysfunction were 0.9549 and 0.9365 in internal and external validation sets. After excluding the cases with a lower degree of confidence, the artificial intelligence-enabled electrocardiogram performed better in the remaining cases in internal (AUC = 0.9759) and external (AUC = 0.9653) validation sets. For the application of future left ventricular dysfunction risk stratification in patients with initially normal ejection fraction, a 4.57-fold risk of future left ventricular dysfunction when the artificial intelligence-enabled electrocardiogram is positive in the internal validation set. The hazard ratio was increased to 8.67 after excluding the cases with a lower degree of confidence. This trend was also validated in the external validation set.ConclusionThe deep learning model with a degree of confidence can provide advanced improvements in identifying left ventricular dysfunction and serve as a decision support and management-guided screening tool for prognosis.

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

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