A5060531120- Cardiac Imaging and Diagnostics
- Cardiovascular Disease and Adiposity
- Coronary Interventions and Diagnostics
- Advanced X-ray and CT Imaging
- Cerebrovascular and Carotid Artery Diseases
- Medical Imaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- Cardiac Valve Diseases and Treatments
- Advanced MRI Techniques and Applications
- COVID-19 diagnosis using AI
- Acute Myocardial Infarction Research
- Cardiovascular Function and Risk Factors
- Cardiovascular, Neuropeptides, and Oxidative Stress Research
- Infective Endocarditis Diagnosis and Management
- Sarcoidosis and Beryllium Toxicity Research
- Aortic aneurysm repair treatments
- Cardiovascular Effects of Exercise
- Amyloidosis: Diagnosis, Treatment, Outcomes
- COVID-19 Clinical Research Studies
- Prostate Cancer Diagnosis and Treatment
- Pericarditis and Cardiac Tamponade
- Artificial Intelligence in Healthcare and Education
- Cardiovascular Health and Disease Prevention
- Aortic Disease and Treatment Approaches
- COVID-19 and healthcare impacts
Cedars-Sinai Medical Center
2017-2025
Cedars-Sinai Smidt Heart Institute
2019-2025
Ijinkai Takeda General Hospital
2023-2024
Showa University
2023-2024
Medical University of Warsaw
2024
Showa University Hospital
2023
VA Greater Los Angeles Healthcare System
2023
TU Dresden
2023
Erasmus MC
2023
University of Calgary
2021
The future risk of myocardial infarction is commonly assessed using cardiovascular scores, coronary artery calcium score, or stenosis severity. We whether noncalcified low-attenuation plaque burden on CT angiography (CCTA) might be a better predictor the infarction.
Pericoronary adipose tissue (PCAT) computed tomography (CT) attenuation measured from coronary CT angiography (CTA) may be a promising metric in identifying high-risk plaques.To determine whether plaque characteristics CTA are associated with PCAT patients first acute syndrome (ACS) and matched controls stable artery disease (CAD).This retrospective, single-center case-control study (data were acquired at the University of Erlangen 2009-2010) analyzed data sets 19 who presented ACS 16 CAD...
Abstract Aims Increased attenuation of pericoronary adipose tissue (PCAT) around the proximal right coronary artery (RCA) from computed tomography angiography (CTA) has been shown to be associated with inflammation and improved prediction cardiac death over plaque features. Our aim was investigate whether PCAT CT is related progression burden. Methods results We analysed CTA studies 111 stable patients (age 59.2 ± 9.8 years, 77% male) who underwent sequential (3.4 1.6 years between scans)...
Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of artery disease burden and prognosis. We sought to develop validate a deep learning system for CCTA-derived measures volume stenosis severity.
Epicardial adipose tissue (EAT) is a visceral fat deposit related to coronary artery disease. Fully automated quantification of EAT volume in clinical routine could be timesaving and reliable tool for cardiovascular risk assessment. We propose new fully deep learning framework thoracic (TAT) from non-contrast calcium computed tomography (CT) scans. The first multi-task convolutional neural network (ConvNet) used determine heart limits perform segmentation tissues. second ConvNet, combined...
Abstract Aims Our aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and automated epicardial adipose tissue (EAT) quantification, for prediction long-term risk myocardial infarction (MI) cardiac death in asymptomatic subjects. Methods results study included 1912 subjects [1117 (58.4%) male, age: 55.8 ± 9.1 years] from prospective EISNER trial follow-up after CAC scoring. EAT volume density were quantified using a...
Epicardial adipose tissue (EAT) volume (cm3) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT measurements quantified from noncontrast cardiac computed tomography.Our study included 2068 asymptomatic subjects (56±9 years, 59% male) EISNER trial (Early Identification Subclinical Atherosclerosis by Noninvasive Imaging Research) with long-term follow-up after coronary...
To evaluate the performance of deep learning for robust and fully automated quantification epicardial adipose tissue (EAT) from multicenter cardiac CT data.In this study, a convolutional neural network approach was trained to quantify EAT on non-contrast material-enhanced calcium-scoring scans multiple cohorts, scanners, protocols (n = 850). Deep compared with three expert readers interobserver variability in subset 141 scans. The algorithm incorporated into research software. Automated...
Adverse plaque characteristics determined by coronary computed tomography angiography (CTA) have been associated with future cardiac events. Our aim was to investigate whether quantitative global per-patient from CTA can predict subsequent death during long-term follow-up.Out of 2748 patients without prior history artery disease undergoing dual-source CT, 32 suffered (mean follow-up 5 ± 2 years). These were matched controls age, gender, risk factors, and symptoms (total 64 patients, 59%...
In patients with stable coronary artery disease (CAD) and high-risk plaques (HRPs) on computed tomography angiography (CTA), we sought to define qualitative quantitative CTA predictors of abnormal 18F-sodium fluoride uptake (18F-NaF) by positron emission (PET).Patients undergoing were screened for HRP. Those who presented ≥3 adverse plaque features (APFs) including positive remodelling; low attenuation (LAP, <30 HU), spotty calcification; obstructive stenosis ≥50%; volume >100 mm3 recruited...
We sought to examine the association of epicardial adipose tissue (EAT) quantified on chest computed tomography (CT) with extent pneumonia and adverse outcomes in patients coronavirus disease 2019 (COVID-19).
We sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver disease [NAFLD] epicardial adipose tissue [EAT] measures) with long-term risk major adverse cardiovascular events (MACE) in asymptomatic individuals.This was a post-hoc analysis prospective EISNER (Early-Identification Subclinical Atherosclerosis by Noninvasive Imaging Research) study participants who underwent baseline coronary artery...
Coronary <sup>18</sup>F-sodium fluoride (<sup>18</sup>F-NaF) PET and CT angiography–based quantitative plaque analysis have shown promise in refining risk stratification patients with coronary artery disease. We combined both of these novel imaging approaches to develop an optimal machine-learning model for the future myocardial infarction stable <b>Methods:</b> Patients known disease underwent <sup>18</sup>F-NaF angiography on a hybrid PET/CT scanner. Machine-learning by extreme gradient...