A multidisciplinary team of computational and clinical researchers from Johns Hopkins University, led by Yingnan Zhang, a Ph.D. candidate at Trayanova Lab, has made significant strides in cardiac research through the development of an innovative strategy called the “genotype-specific digital twin” (Geno-DT). This cutting-edge approach involves integrating personal genetics into Read more…
Natalia Trayanova, Murray B. Sachs Professor in Johns Hopkins University’s Department of Biomedical Engineering, is part of an international research team selected to receive $8 million from the Leducq Foundation for a project that aims to find new therapies for heart disease by studying the effects of stimulating nerves. Read more here.
A team of computational and clinical researchers, led by first author Eric Sung, PhD, discover in their latest research that fat intermingling with scar tissue is the driving force behind the development of heart rhythm disturbances. This prospective study was completed by two clinical centers, one at Johns Hopkins University Read more…
The three awards will enable ADVANCE researchers to develop deep learning learning methods to understand the recurrence of atrial fibrillation following catheter ablation, artificial intelligence to predict the risk of sudden death in Sarcoidosis, and computational approaches for arrhythmia risk prediction in Arrhythmogenic Right Ventricular Cardiomyopathy. Principal investigator Dr. Natalia Read more…
Access the PDF of their essay here or view directly in The Lancet.
We have received a lot of publicity following Dr. Dan Popescu’s publication in Nature Cardiovascular Research. The cover of the journal’s April issue was designed by Dr. Kimberly Popescu (Dan’s wife!), featuring a schematic of a neural network embedded in cardiac tissue. Dr. Natalia Trayanova was recently interviewed by NBC Washington, Read more…
A new artificial intelligence-based approach can predict if and when a patient could die of cardiac arrest. The technology, built on raw images of patient’s diseased hearts and patient backgrounds, significantly improves on doctor’s predictions and stands to revolutionize clinical decision making and increase survival from sudden and lethal cardiac Read more…
In proof-of-concept study of 10 patients with atrial fibrillation, personalized models accurately predict where surgeons should destroy diseased heart tissue.
Natalia Trayanova will be inducted into the Women in Technology Hall of Fame in a ceremony on June 10, 2019.
Read about her award from the Whiting School. Read more…