Artificial intelligence measuring the aortic diameter assist in identifying adverse blood pressure status including masked hypertension

ABSTRACT

Introduction and objectives

Artificial intelligence (AI) made it achievable that aortic dilation could be measured in CT images indirectly, while aortic diameter (AD) has the certain relationship with blood pressure. It was potential that the blood pressure condition be determined by AD measurement using the data obtained from a CT scanning especially in identifying masked hypertension and predicting the risk of poor control of blood pressure (BP) which was easy to elude diagnosis in clinic. We aimed to evaluate the possibility of utilizing AD by AI for predicting the risk of adverse BP status (including masked hypertension or poor BP control) and the optimal thoracic aortic position in measurement as well as the cutoff value for predicting the risk.

Methods

Eight hundred and one patients were enrolled in our study. AI-Rad Companion Cardiovascular (K183268 FDA approved) was used to perform automatic aorta measurement in thoracic CT images at nine key positions based on AHA guidelines. Data was post processed by software from AI-Rad Companion undergone rigorous clinical validation by both FDA and CE as verification of its efficacy and usability. The AD’s risk and diagnostic value was assessed in identifying hypertension in the general population, in identifying the poor BP controlled in the hypertension population, and in screening masked hypertension in the general population respectively by multiple regression analysis and receiver operating curve analysis.

Results

AD measured by AI was a risk factor for adverse BP status after clinical covariates adjustment (OR = 1.02 ~ 1.26). The AD at mid descending aorta was mostly affected by BP particularly, which is optimal indicator in identifying hypertension in the general population (AUC = 0.73) and for screening masked hypertension (AUC = 0.78).

Conclusion

Using AI to measure the AD of the aorta, particularly at the position of mid descending aorta, is greatly valuable for identifying people with poor BP status. It will be possible to reveal more clinical information reflected by ordinary CT images and enrich the screening methods for hypertension, especially masked hypertension.

PLAIN LANGUAGE SUMMARY

HTN has a significant adverse effect on arterial deformation. BP and arterial dilation promote each other in a vicious circle. Arterial dilation may not be restricted by apparent fluctuations in BP and is objective evidence of an undesirable BP state. The accuracy of AD measurements by AI on chest CT images has been verified. There has not been the application of AD measurement by AI in the scene of poor BP status in clinical practice.

In this study, we applied AI to measure the diameter of the aorta in nine consecutive positions. We explored the association between AD at various positions and BP levels and the possibility that AD in identifying poor BP status in different populations. We found that the AD at the MD is of great value in screening MH and evaluating the control state of BP in HTN. It will be possible to significantly expand the clinical information reflected by ordinary CT images and enrich the screening methods for HTN, especially MH.

KEYWORDS:

• Aortic diameter
• artificial intelligence
• blood pressure
• CT plain scan
• hypertension
• masked hypertension

Author contributions

Yaoling Wang and Lijuan Bai contribute equally to the article. Fan Yang and Benling Qi are both the corresponding authors, and Benling Qi takes primary responsibility for the entire paper.

Disclosure of financial/other conflicts of interest

Author Yichen Lu is employed by Siemens Healthineers Digital Technology (Shanghai). Author Li Chen is employed by Novartis pharmaceuticals corporation. The remaining authors declare that the research was conducted in the absence of any commercial, financial or other relationships that could be construed as a potential conflict of interest. Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Data availability statement

Data is openly available in a public repository that issues datasets with DOIs

Supplementary material

Supplemental data for this article can be accessed here

Additional information

Funding

The study was supported by the National Natural Science Foundation of China (Grant No.81571373, No.82001491), Health Commission of Hubei Province scientific research project WJ2021M247, Natural Science Foundation of Hubei Province of China (Grant No. 2017CFB627), and Scientific Research Fund of Wuhan Union Hospital (Grant No.2019). Author’s statement that the views expressed in the submitted article are his or her own and not an official position of the institution or funder.