Publications by authors named "T Geva"
Article Synopsis
- Right ventricular outflow dysfunction is a common issue in patients with repaired tetralogy of Fallot, leading to increased health risks as they age.
- The American Heart Association has released an update focusing on how to monitor and treat this condition effectively, including new therapies and techniques for managing complications.
- The statement highlights the importance of understanding how other health issues and patients' perspectives affect their quality of life and includes discussions on when and how to perform pulmonary valve replacements.
Article Synopsis
- The study seeks to improve risk stratification for patients with congenital heart disease (CHD) by using an AI-enhanced electrocardiogram (ECG) tool, addressing a significant gap in current medical practice.* -
- A convolutional neural network was developed and tested on a large set of ECGs from patients at Boston Children's Hospital to predict 5-year mortality, showing good performance compared to traditional indicators like age and heart function metrics.* -
- The model displays potential for timely risk assessment across various ages and types of CHD, helping to guide future monitoring and therapeutic interventions for patients.*
Electrocardiogram-Based Deep Learning to Predict Mortality in Repaired Tetralogy of Fallot.
JACC Clin Electrophysiol
December 2024
Background: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to predict mortality in adults with acquired cardiovascular diseases. However, its application to the growing repaired tetralogy of Fallot (rTOF) population remains unexplored.
Objectives: This study aimed to develop and externally validate an AI-ECG model to predict 5-year mortality in rTOF.
Background: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to detect biventricular pathophysiology. However, AI-ECG analysis remains underexplored in congenital heart disease (CHD).
Objectives: The purpose of this study was to develop and externally validate an AI-ECG model to predict cardiovascular magnetic resonance (CMR)-defined biventricular dysfunction/dilation in patients with CHD.
Patients with congenital heart disease often have cardiac anatomy that deviates significantly from normal, frequently requiring multiple heart surgeries. Image segmentation from a preoperative cardiovascular magnetic resonance (CMR) scan would enable creation of patient-specific 3D surface models of the heart, which have potential to improve surgical planning, enable surgical simulation, and allow automatic computation of quantitative metrics of heart function. However, there is no publicly available CMR dataset for whole-heart segmentation in patients with congenital heart disease.
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