AI Article Synopsis
- The CERTAINTY study aims to improve the identification of individuals at low risk for ventricular arrhythmia (VA) among those who may need implantable cardioverter-defibrillators (ICDs) to reduce complications.* -
- This study utilizes deep learning techniques to analyze cine cardiac magnetic resonance (CMR) images, developing two neural networks: one for extracting cardiac features (Cine Fingerprint Extractor) and another for predicting VA risk (Risk Predictor).* -
- Findings show that a cine risk score derived from CMR images effectively differentiates between patients with and without VA, suggesting that cine CMR can enhance risk predictions in patients being considered for primary prevention ICDs.*
Article Abstract
Better models to identify individuals at low risk of ventricular arrhythmia (VA) are needed for implantable cardioverter-defibrillator (ICD) candidates to mitigate the risk of ICD-related complications. We designed the CERTAINTY study (CinE caRdiac magneTic resonAnce to predIct veNTricular arrhYthmia) with deep learning for VA risk prediction from cine cardiac magnetic resonance (CMR). Using a training cohort of primary prevention ICD recipients (n = 350, 97 women, median age 59 years, 178 ischemic cardiomyopathy) who underwent CMR immediately prior to ICD implantation, we developed two neural networks: Cine Fingerprint Extractor and Risk Predictor. The former extracts cardiac structure and function features from cine CMR in a form of cine fingerprint in a fully unsupervised fashion, and the latter takes in the cine fingerprint and outputs disease outcomes as a cine risk score. Patients with VA (n = 96) had a significantly higher cine risk score than those without VA. Multivariate analysis showed that the cine risk score was significantly associated with VA after adjusting for clinical characteristics, cardiac structure and function including CMR-derived scar extent. These findings indicate that non-contrast, cine CMR inherently contains features to improve VA risk prediction in primary prevention ICD candidates. We solicit participation from multiple centers for external validation.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8608832 | PMC |
| http://dx.doi.org/10.1038/s41598-021-02111-7 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Assessing Acute Pericarditis with T1 Mapping: A Supportive Contrast-Free CMR Marker.
Tomography
November 2024
Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy.
Objective: The purpose of this study was to explore the impact of pericardial T1 mapping as a potential supportive non-contrast cardiovascular magnetic resonance (CMR) parameter in the diagnosis of acute pericarditis. Additionally, we investigated the relationship between T1 mapping values in acute pericarditis patients and their demographic data, cardiovascular risk factors, clinical parameters, cardiac biomarkers, and cardiac function.
Method: This retrospective study included CMR scans in 35 consecutive patients with acute pericarditis (26 males, 45.
Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery.
J Vis Exp
December 2024
School of Biological Science and Medical Engineering, Southeast University; Mathematical Sciences Department, Worcester Polytechnic Institute.
Quantifying the mechanical properties of coronary arterial walls could provide meaningful information for the diagnosis, management, and treatment of coronary artery diseases. Since patient-specific coronary samples are not available for patients requiring continuous monitoring, direct experimental testing of vessel material properties becomes impossible. Current coronary models typically use material parameters from available literature, leading to significant mechanical stress/strain calculation errors.
View Article and Find Full Text PDFReliability of post-contrast deep learning-based highly accelerated cardiac cine MRI for the assessment of ventricular function.
Magn Reson Imaging
December 2024
Department of Radiology, Iwate Medical University, Yahaba, Japan.
Objective: The total examination time can be reduced if high-quality two-dimensional (2D) cine images can be collected post-contrast to minimize non-scanning time prior to late gadolinium-enhanced imaging. This study aimed to assess the equivalency of the pre-and post-contrast performance of 2D deep learning-based highly accelerated cardiac cine (DL cine) imaging by evaluating the image quality and the quantification of biventricular volumes and function in the clinical setting.
Material And Methods: Thirty patients (20 men, mean age 53.
Post-hoc out-of-distribution detection for cardiac MRI segmentation.
Comput Med Imaging Graph
December 2024
ICMUB, Université de Bourgogne, Dijon, France. Electronic address:
In real-world scenarios, medical image segmentation models encounter input images that may deviate from the training images in various ways. These differences can arise from changes in image scanners and acquisition protocols, or even the images can come from a different modality or domain. When the model encounters these out-of-distribution (OOD) images, it can behave unpredictably.
View Article and Find Full Text PDFPrevalence of Mitral Annular Disjunction at Cardiac MRI: Results from a Multicenter Registry.
Radiol Cardiothorac Imaging
December 2024
From the Department of Radiology, School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58-60, 20132 Milan, Italy (A. Palmisano, E.B., S.B., D.V., A.E.); Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Milan, Italy (A. Palmisano, E.B., M.C., D.V., A.E.); Academic Radiology Department of Translational Research, University of Pisa, Pisa, Italy (G.D.A., C.D.G., M.A., D.P., L.F., E.N.); Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, Department of Radiodiagnostics, Università di Brescia-Spedali Civili, Brescia, Italy (P.R., N.d.M., M.R., D.F.); Department of Emergency Radiology, University Hospital Careggi, Florence, Italy (A.R., S.P., V.M.); Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy (L.M., G.C., N.G.); Department of Surgical Sciences, University of Turin, Turin, Italy (D.T., M.G., R.F.); Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, L'Aquila, Italy (P.P.); Department of Biotechnological and Applied Clinical Science, University of L'Aquila, L'Aquila, Italy (E.D.C.); Diagnostic and Interventional Radiology Unit, BIOMORF Department, University Hospital Policlinico, Messina, Italy (T.D., L.R.M.L., A.B.); Department of Radiology, Santa Maria delle Grazie Hospital, Pozzuoli, Italy (S.D.); Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy (A. Ponsiglione, R.A., M.I.); Department of Radiology, AOU Cagliari, University of Cagliari, Cagliari, Italy (M.P., R.C., L.S.); Department of Radiology, Ospedale del Mare-ASL NA1 Centro, Naples, Italy (G.F., C.L.); Department of Radiology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy (V.S., S.S.); Department of Radiology, Ospedale Maggiore della Carità University Hospital, Novara, Italy (A.S., A.C.); and IRCCS Azienda Ospedaliero-Universitaria di Bologna, S. Orsola Hospital, University of Bologna, Italy (L.L.).
Purpose To determine the prevalence of mitral annular disjunction (MAD) in patients undergoing cardiac MRI for various clinical indications and to assess the association of MAD with arrhythmia, mitral valve prolapse (MVP), and myocardial alteration. Materials and Methods This study analyzed data from a retrospective observational registry of consecutive patients undergoing cardiac MRI for different clinical indications. Cardiac MRI examinations were performed from January 2019 to June 2019 at 13 Italian hospitals.
View Article and Find Full Text PDFWant 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!