Hypertension

Fully automatic estimation of global left ventricular systolic function using deep learning in transoesophageal echocardiography.

AIMS: To improve monitoring of cardiac function during major surgery and intensive care, we have developed a method for fully automatic estimation of mitral annular plane systolic excursion (auto-MAPSE) using deep learning in transoesophageal echocardiography (TOE). The aim of this study was a clinical validation of auto-MAPSE in patients with heart disease.

APPRAISE-HRI: AN ARTIFICIAL INTELLIGENCE ALGORITHM FOR TRIAGE OF HEMORRHAGE CASUALTIES.

Background: Hemorrhage remains the leading cause of death on the battlefield. This study aims to assess the ability of an artificial intelligence triage algorithm to automatically analyze vital-sign data and stratify hemorrhage risk in trauma patients. Methods: Here, we developed the APPRAISE-Hemorrhage Risk Index (HRI) algorithm, which uses three routinely measured vital signs (heart rate and dia...

Deep Learning-Based Non-Contact IPPG Signal Blood Pressure Measurement Research.

In this paper, a multi-stage deep learning blood pressure prediction model based on imaging photoplethysmography (IPPG) signals is proposed to achieve...

Predicting blood pressure from face videos using face diagnosis theory and deep neural networks technique.

Hypertension is a major cause of cardiovascular diseases. Accurate and convenient measurement of blood pressure are necessary for the detection, treat...

Cardiac phase detection in echocardiography using convolutional neural networks.

Echocardiography is a commonly used and cost-effective test to assess heart conditions. During the test, cardiologists and technicians observe two car...

A deep learning method for continuous noninvasive blood pressure monitoring using photoplethysmography.

. The aim of this study is to investigate continuous blood pressure waveform estimation from a plethysmography (PPG) signal, thus providing more human...

Combating hypertension beyond genome-wide association studies: Microbiome and artificial intelligence as opportunities for precision medicine.

The single largest contributor to human mortality is cardiovascular disease, the top risk factor for which is hypertension (HTN). The last two decades...

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching.

Cardiac monitoring can be performed by means of an accelerometer attached to a subject's chest, which produces the Seismocardiography (SCG) signal. De...

Machine-learning predictive model of pregnancy-induced hypertension in the first trimester.

In the first trimester of pregnancy, accurately predicting the occurrence of pregnancy-induced hypertension (PIH) is important for both identifying hi...

Survey and Evaluation of Hypertension Machine Learning Research.

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in di...

Body composition predicts hypertension using machine learning methods: a cohort study.

We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records ...

Differential diagnosis of secondary hypertension based on deep learning.

Secondary hypertension is associated with higher risks of target organ damage and cardiovascular and cerebrovascular disease events. Early aetiology i...

A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images.

Accurate segmentation of the left ventricle (LV) is crucial for evaluating myocardial perfusion SPECT (MPS) and assessing LV functions. In this study,...

Novel Port Placement in Robot-Assisted DIEP Flap Harvest Improves Visibility and Bilateral DIEP Access: Early Controlled Cohort Study.

SUMMARY: To minimize donor-site damage, robot-assisted (RA) deep inferior epigastric perforator (DIEP) flap harvest has been suggested. Current roboti...

Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes.

BACKGROUND: Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex ...

A genome-wide association study of childhood adiposity and blood lipids.

The rising prevalence of childhood obesity and dyslipidaemia is a major public health concern due to its association with morbidity and mortality in ...

Effect of pneumatic leg compression on post-induction hypotension in elderly patients undergoing robot-assisted laparoscopic prostatectomy: a double-blind randomised controlled trial.

Post-induction hypotension is common and associated with postoperative complications. We hypothesised that pneumatic leg compression reduces post-indu...

EchoEFNet: Multi-task deep learning network for automatic calculation of left ventricular ejection fraction in 2D echocardiography.

Left ventricular ejection fraction (LVEF) is essential for evaluating left ventricular systolic function. However, its clinical calculation requires t...

Soft robotic patient-specific hydrodynamic model of aortic stenosis and ventricular remodeling.

Aortic stenosis (AS) affects about 1.5 million people in the United States and is associated with a 5-year survival rate of 20% if untreated. In these...

Phonocardiogram transfer learning-based CatBoost model for diastolic dysfunction identification using multiple domain-specific deep feature fusion.

Left ventricular diastolic dyfunction detection is particularly important in cardiac function screening. This paper proposed a phonocardiogram (PCG) t...