A5013607075- Cardiac Imaging and Diagnostics
- Cardiovascular Function and Risk Factors
- Advanced MRI Techniques and Applications
- Artificial Intelligence in Healthcare
- Cardiac pacing and defibrillation studies
- Cardiovascular Disease and Adiposity
- Advanced X-ray and CT Imaging
- Opioid Use Disorder Treatment
- Coronary Interventions and Diagnostics
- Atrial Fibrillation Management and Outcomes
- Cardiac Structural Anomalies and Repair
- Cardiac Valve Diseases and Treatments
- Forensic Toxicology and Drug Analysis
- Substance Abuse Treatment and Outcomes
- Cardiomyopathy and Myosin Studies
- Advanced Statistical Process Monitoring
- Semiconductor materials and devices
- Acute Myocardial Infarction Research
- Cardiac tumors and thrombi
- Machine Learning and Algorithms
- Radiomics and Machine Learning in Medical Imaging
- Pharmaceutical Practices and Patient Outcomes
- Advancements in Semiconductor Devices and Circuit Design
- Cardiac electrophysiology and arrhythmias
- Pharmaceutical Economics and Policy
Inserm
2023-2025
Université Paris Cité
2023-2025
Assistance Publique – Hôpitaux de Paris
2023-2025
Hôpital américain de paris
2025
Institut Cardiovasculaire Paris Sud
2024-2025
Hôpital Privé Jacques Cartier
2024-2025
Siemens (France)
2025
Hôpital Lariboisière
2023-2025
Marqueurs cardiovasculaires en situation de stress
2024-2025
Centre Hospitalier Régional et Universitaire de Nancy
2021
A cardiac MRI model incorporating the ischemic late gadolinium enhancement (LGE) parameters of extent, transmurality, location, and midwall LGE significantly outperformed traditional risk factors in predicting all-cause mortality participants with cardiomyopathy.
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance an ML model that uses both stress cardiac MRI and CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) patients with newly diagnosed CAD. Materials Methods This retrospective study...
Abstract Aims This study aimed to determine in patients undergoing stress cardiovascular magnetic resonance (CMR) whether fully automated artificial intelligence (AI)-based left ventricular ejection fraction (LVEFAI) can provide incremental prognostic value predict death above traditional prognosticators. Methods and results Between 2016 2018, we conducted a longitudinal that included all consecutive referred for vasodilator CMR. LVEFAI was assessed using AI algorithm combines multiple deep...
While few traditional scores are available for risk stratification of patients hospitalized acute heart failure (AHF), the potential benefit machine learning (ML) is not well established. We aimed to assess feasibility and accuracy a supervised ML model including environmental factors predict in-hospital major adverse events (MAEs) in AHF. In April 2021, French national prospective multicentre study included all consecutive intensive cardiac care unit. Patients admitted AHF were analyses. A...
Although some scores based on traditional statistical methods are available for risk stratification in patients hospitalized cardiac intensive care units (CICUs), the interest of machine learning (ML) this field is not well established. We aimed to build an ML model predict in-hospital major adverse events (MAE) CICU. In April 2021, a French national prospective multicentre study involving 39 centres included all consecutive admitted The primary outcome was MAE, including death, resuscitated...