Area of research
Health Informatics · Molecular Biology
Research interest
Research interests include Artificial Intelligence in Healthcare and Education, Congenital heart defects research, Congenital Heart Disease Studies, and Cardiac Imaging and Diagnostics.
The MI-CLAIM-GEN checklist for generative artificial intelligence in health
Self-supervised learning for label-free segmentation in cardiac ultrasound
PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation
Epistasis regulates genetic control of cardiac hypertrophy
Novel Techniques in Imaging Congenital Heart Disease
Learning epistatic polygenic phenotypes with Boolean interactions
Deep‐learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection
Domain-guided data augmentation for deep learning on medical imaging
Proceedings of the NHLBI Workshop on Artificial Intelligence in Cardiovascular Imaging
Epistasis regulates genetic control of cardiac hypertrophy
Deciphering epistatic genetic regulation of cardiac hypertrophy
Machine Learning and the Future of Cardiovascular Care
An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease
Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist
Fast and accurate view classification of echocardiograms using deep learning
Distinct myocardial lineages break atrial symmetry during cardiogenesis in zebrafish
Little Fish, Big Data: Zebrafish as a Model for Cardiovascular and Metabolic Disease
A mutation in the atrial-specific myosin light chain gene (MYL4) causes familial atrial fibrillation