Aditi Roy

A survey of pathways for mechano-electric coupling in the atria

M. Varela, Jack Lee, Marta Varela, 2020, Progress in biophysics and molecular biology.

Evaluation of accuracy of patient-specific non-linear biomechanical models used to predict intraoperative brain shift

Aditi Roy, R. R. Garlapati, A. Mostayed, 2013 .

Identifying locations of re-entrant drivers from patient-specific distribution of fibrosis in the left atrium

T. Schaeffter, J. Hancox, R. Macleod, 2020, PLoS Comput. Biol..

Image-Based Computational Evaluation of the Effects of Atrial Wall Thickness and Fibrosis on Re-entrant Drivers for Atrial Fibrillation

Marta Varela, Aditi Roy, Oleg Aslanidi, 2018, Front. Physiol..

Evolution of Epicardial Rotors into Breakthrough Waves During Atrial Fibrillation in 3D Canine Biatrial Model with Detailed Fibre Orientation

M. Varela, O. Aslanidi, Aditi Roy, 2021, 2021 Computing in Cardiology (CinC).

Investigating Strain as a Biomarker for Atrial Fibrosis Quantified by Patient Cine MRI Data

H. Chubb, O. Aslanidi, A. Vecchi, 2020, 2020 Computing in Cardiology.

In-silico drug trials for precision medicine in atrial fibrillation: From ionic mechanisms to electrocardiogram-based predictions in structurally-healthy human atria

B. Rodríguez, A. Bueno-Orovio, R. Sachetto, 2022, Frontiers in Physiology.

Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning

P. Bates, O. Aslanidi, Jichao Zhao, 2021 .

Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning

P. Bates, O. Aslanidi, Jichao Zhao, 2021, Frontiers in Physiology.

Development of a Deep Learning Method to Predict Optimal Ablation Patterns for Atrial Fibrillation

Paul A. Bates, Marta Varela, Oleg V. Aslanidi, 2019, 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).

Reinforcement Learning to Improve Image-Guidance of Ablation Therapy for Atrial Fibrillation

O. Aslanidi, A. Zeidan, Aditi Roy, 2021, Frontiers in Physiology.

Biomechanical modeling provides more accurate data for neuronavigation than rigid registration

R. Kikinis, K. Miller, S. Warfield, 2015 .