AIMC Topic: Cardiac Resynchronization Therapy

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Characterization of cardiac resynchronization therapy response through machine learning and personalized models.

Computers in biology and medicine Aug 13, 2024
INTRODUCTION: The characterization and selection of heart failure (HF) patients for cardiac resynchronization therapy (CRT) remain challenging, with around 30% non-responder rate despite following current guidelines. This study aims to propose a nove...
Male Middle Aged Heart Failure Cardiac Resynchronization Therapy Models, Cardiovascular Humans Machine Learning Precision Medicine Female Aged
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Machine learning of ECG waveforms and cardiac magnetic resonance for response and survival after cardiac resynchronization therapy.

Computers in biology and medicine May 22, 2024
Cardiac resynchronization therapy (CRT) can lead to marked symptom reduction and improved survival in selected patients with heart failure with reduced ejection fraction (HFrEF); however, many candidates for CRT based on clinical guidelines do not ha...
Electrocardiography Female Magnetic Resonance Imaging Aged Signal Processing, Computer-Assisted Humans Male Machine Learning Middle Aged Heart Failure Cardiac Resynchronization Therapy
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Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review.

Heart failure reviews Oct 20, 2023
The aim of the presented review is to summarize the literature data on the accuracy and clinical applicability of artificial intelligence (AI) models as a valuable alternative to the current guidelines in predicting cardiac resynchronization therapy ...
Humans Treatment Outcome Artificial Intelligence Heart Failure Cardiac Resynchronization Therapy
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A method using deep learning to discover new predictors from left-ventricular mechanical dyssynchrony for CRT response.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology Aug 1, 2022
BACKGROUND: Studies have shown that the conventional parameters characterizing left ventricular mechanical dyssynchrony (LVMD) measured on gated SPECT myocardial perfusion imaging (MPI) have their own statistical limitations in predicting cardiac res...
Ventricular Dysfunction, Left Ventricular Function, Left Myocardial Perfusion Imaging Stroke Volume Deep Learning Heart Failure Cardiac Resynchronization Therapy Humans
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A multimodal deep learning model for cardiac resynchronisation therapy response prediction.

Medical image analysis Apr 20, 2022
We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the 'nnU-Net' segmentation model ...
Humans Echocardiography Deep Learning Heart Heart Failure Cardiac Resynchronization Therapy
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Prediction of response to cardiac resynchronization therapy using a multi-feature learning method.

The international journal of cardiovascular imaging Nov 23, 2020
We hypothesized that a multiparametric evaluation, based on the combination of electrocardiographic and echocardiographic parameters, could enhance the appraisal of the likelihood of reverse remodeling and prognosis of favorable clinical evolution to...
Time Factors Electrocardiography Treatment Outcome Europe Recovery of Function Male Ventricular Function, Left Stroke Volume Middle Aged Echocardiography, Doppler Cardiac Resynchronization Therapy Devices Predictive Value of Tests Signal Processing, Computer-Assisted Heart Failure Cardiac Resynchronization Therapy Image Interpretation, Computer-Assisted Humans Machine Learning Decision Support Techniques Retrospective Studies Female Aged
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Machine Learning of 12-Lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients With Differential Outcomes.

Circulation. Arrhythmia and electrophysiology Jun 14, 2020
BACKGROUND: Cardiac resynchronization therapy (CRT) improves heart failure outcomes but has significant nonresponse rates, highlighting limitations in ECG selection criteria: QRS duration (QRSd) ≥150 ms and subjective labeling of left bundle branch b...
Heart-Assist Devices Heart Transplantation Bundle-Branch Block Recovery of Function Predictive Value of Tests Signal Processing, Computer-Assisted Heart Failure Cardiac Resynchronization Therapy Time Factors Electrocardiography Treatment Outcome Unsupervised Machine Learning Female Risk Factors Humans Aged Retrospective Studies Male Ventricular Function, Left Stroke Volume Middle Aged Risk Assessment Disease Progression Diagnosis, Computer-Assisted
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A Shifting Paradigm in Robotic Heart Surgery: From Single-Procedure Approach to Establishing a Robotic Heart Center of Excellence.

Innovations (Philadelphia, Pa.) May 8, 2020
Cardiac Resynchronization Therapy Arrhythmias, Cardiac Atrial Fibrillation Mitral Valve Cardiomyopathy, Hypertrophic History, 21st Century Coronary Artery Bypass History, 20th Century Patient Positioning Robotics Minimally Invasive Surgical Procedures Robotic Surgical Procedures Cardiac Surgical Procedures Coronary Artery Bypass, Off-Pump Humans Endoscopy Aortic Valve Nobel Prize Myocardial Revascularization Congenital Abnormalities Myocardial Bridging Tricuspid Valve Pericardiectomy Lead Poisoning
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Can machine learning improve patient selection for cardiac resynchronization therapy?

PloS one Oct 3, 2019
RATIONALE: Multiple clinical trials support the effectiveness of cardiac resynchronization therapy (CRT); however, optimal patient selection remains challenging due to substantial treatment heterogeneity among patients who meet the clinical practice ...
Outcome Assessment, Health Care Male Humans ROC Curve Machine Learning Models, Theoretical Female Patient Selection Aged Cardiac Resynchronization Therapy
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Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines.

Circulation. Arrhythmia and electrophysiology Jun 20, 2019
BACKGROUND: Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines.
Baltimore Middle Aged Male Humans Retrospective Studies Predictive Value of Tests Risk Assessment Progression-Free Survival Ventricular Function, Left Disease Progression Machine Learning Patient Selection Clinical Decision-Making Practice Guidelines as Topic Stroke Volume Time Factors Heart Failure Decision Support Techniques Female Heart-Assist Devices Recovery of Function Echocardiography Cardiac Resynchronization Therapy Risk Factors Heart Transplantation Aged Ohio
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