Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9885219 | PMC |
| http://dx.doi.org/10.5114/aic.2022.120376 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Left atrial appendage closure guided by fusion of 3D computational modelling on real-time fluoroscopy: A multicenter experience.
Int J Cardiol
January 2025
Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
Article Synopsis
- The study investigates the effectiveness and safety of using a 3D computational model fused with real-time fluoroscopy to guide left atrial appendage closure (LAAC) for patients with non-valvular atrial fibrillation.
- The research included 106 patients who underwent the procedure, achieving a 100% success rate for device implantation and meeting primary efficacy criteria in 89% of cases.
- The approach was deemed safe, with only 1.9% of patients experiencing major complications, suggesting that this technique could enhance LAAC outcomes.
CT-derived simulations to predict outcomes in patients undergoing transcatheter aortic valve implantation with an ACURATE Neo2 valve the PRECISE-TAVI cohort B trial.
Catheter Cardiovasc Interv
November 2024
Department of Cardiology, Cardiovascular Institute, Thoraxcenter, Erasmus University Medical Center, Rotterdam, The Netherlands.
Article Synopsis
- * Out of 65 patients analyzed, 30% experienced more than trace PVL, and the findings showed that while simulations could identify those at risk for significant PVL, they were not effective in predicting PPI.
- * The results indicated a median PVL measurement significantly lower in patients with none-trace leakage compared to those with mild and moderate PVL, suggesting variations in risk levels and outcomes after
Patient-specific computer simulation of transcatheter aortic valve replacement in patients with previous mechanical mitral prosthesis: A case series.
Catheter Cardiovasc Interv
April 2024
Structural Interventional Cardiology, Department of Clinical & Experimental medicine, University Hospital Careggi, Florence, Italy.
Transcatheter aortic valve replacement performed in patients with previously implanted mechanical mitral prosthesis represents a high risk procedure with several potential complications. We report a systematic use of a prediction model based on artificial intelligence to plan the interventional strategy in this challenging scenario.
View Article and Find Full Text PDFCT Attenuation of Periaortic Adipose Tissue in Abdominal Aortic Aneurysms.
Radiol Cardiothorac Imaging
February 2024
From the University of Edinburgh Centre for Cardiovascular Science, University of Edinburgh, 49 Little France Crescent, Chancellor's Building, Room SU.305, Edinburgh EH16 4SB, United Kingdom (S.D., E.T., M.B.J.S., J.N., A.J.F., M.R.D., D.E.N., R.O.F., M.C.W.); School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom (A.J.F.); Department of Medicine, Division of Artificial Intelligence, and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, Calif (D.D.); and Edinburgh Vascular Service, Royal Infirmary of Edinburgh, NHS Lothian, Edinburgh, United Kingdom (R.O.F.).
Purpose To assess periaortic adipose tissue attenuation at CT angiography in different abdominal aortic aneurysm disease states. Materials and Methods In a retrospective observational study from January 2018 to December 2022, periaortic adipose tissue attenuation was assessed at CT angiography in patients with asymptomatic or symptomatic (including rupture) abdominal aortic aneurysms and controls without aneurysms. Adipose tissue attenuation was measured using semiautomated software in periaortic aneurysmal and nonaneurysmal segments of the abdominal aorta and in subcutaneous and visceral adipose tissue.
View Article and Find Full Text PDFClinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study.
EBioMedicine
January 2024
Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address:
Article Synopsis
- Myocardial perfusion imaging (MPI) is widely used to diagnose coronary artery disease, but many patients have normal results; this study explores whether machine learning can identify unique patient profiles among those with normal scans and assess their risk of death or myocardial infarction.
- The research involved a large cohort of over 21,000 patients from an international MPI registry, employing unsupervised clustering to discover four distinct patient phenotypes, revealing differing characteristics and stress testing requirements among them.
- Findings indicated that one specific cluster of patients (Cluster 4), despite having normal scans, faced a significantly higher risk of serious cardiovascular events, suggesting that identifying these phenotypes could enhance risk assessment and patient management.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!