A deep convolutional neural network ensemble for composite identification of pulmonary nodules and incidental findings on routine PET/CT.

Segment aorta and localize landmarks simultaneously on noncontrast CT using a multitask learning framework for patients without severe vascular disease

Nomogram based on clinical features at a single outpatient visit to predict masked hypertension and masked uncontrolled hypertension: A study of diagnostic accuracy

Application of an artificial intelligence ensemble for detection of important secondary findings on lung ventilation and perfusion SPECT-CT.

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 SUMMARYHTN 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.

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

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