Dr. Bruna Gomes is a cardiologist trained at the Heidelberg University Hospital in Germany. She obtained her medical degree (2008-2014) at the Faculty of Medicine of the University of Coimbra, Portugal. As a cardiologist resident (residency 2014-2021), she obtained considerable experience in distinct clinical settings varying from diverse cardiology wards to echocardiography, interventional cardiology, and cardiac MRI. During this period, she expanded her quantitative skill set to include biostatistics and bioinformatics (with special focus in deep learning and computer vision) to address open issues in diagnosing and treating structural heart disease. Since July 2021 she is a postdoctoral researcher at Stanford University, currently refining her bioinformatics research skills at Prof. Ashley’s lab.
In her spare time, she plays the piano, composes music, travels, and plays virtual reality games.

Honors & Awards

  • Award for the best student of the class 2008-2014, Faculty of Medicine, University of Coimbra, Portugal (2015)
  • Award for the top 3% best students, University of Coimbra, Portugal (2011,2012,2013,2014)
  • Merit scholarship, University of Coimbra, Portugal (2011)
  • Award for the top 3% best students, University of Coimbra, Portugal (2009,2010)
  • Finalist of the XVII Portuguese Mathematics Olympiad, Portuguese Mathematics Olympiad (2008)
  • Merit Prize awarding the best secondary school student, Rotary Club, Portugal (2008)
  • Merit Prize awarding the students with the best secondary school diploma, Ministry of Education, Portugal (2008)

Professional Education

  • Doctor of Medicine, University of Heidelberg (2018)
  • Master of Medicine, Universidade De Coimbra (2014)
  • Cardiology specialist, Department of Cardiology, Pneumology and Angiology, Heidelberg University Hospital, Germany (2021)

Stanford Advisors

Current Research and Scholarly Interests

The increasing availability of very large datasets, along with recent advances in deep learning based tools for automatic extraction of cardiac traits, has led to the discovery of further common variants associated with cardiac disease. However, the genetic underpinnings of valvular heart disease remains understudied. I am interested in developing deep learning techniques to automatically extract cardiac flow information to facilitate genome-wide association studies of cardiac flow traits.

All Publications

  • Genetic architecture of cardiac dynamic flow volumes. Nature genetics Gomes, B., Singh, A., O'Sullivan, J. W., Schnurr, T. M., Goddard, P. C., Loong, S., Amar, D., Hughes, J. W., Kostur, M., Haddad, F., Salerno, M., Foo, R., Montgomery, S. B., Parikh, V. N., Meder, B., Ashley, E. A. 2023


    Cardiac blood flow is a critical determinant of human health. However, the definition of its genetic architecture is limited by the technical challenge of capturing dynamic flow volumes from cardiac imaging at scale. We present DeepFlow, a deep-learning system to extract cardiac flow and volumes from phase-contrast cardiac magnetic resonance imaging. A mixed-linear model applied to 37,653 individuals from the UK Biobank reveals genome-wide significant associations across cardiac dynamic flow volumes spanning from aortic forward velocity to aortic regurgitation fraction. Mendelian randomization reveals a causal role for aortic root size in aortic valve regurgitation. Among the most significant contributing variants, localizing genes (near ELN, PRDM6 and ADAMTS7) are implicated in connective tissue and blood pressure pathways. Here we show that DeepFlow cardiac flow phenotyping at scale, combined with genotyping data, reinforces the contribution of connective tissue genes, blood pressure and root size to aortic valve function.

    View details for DOI 10.1038/s41588-023-01587-5

    View details for PubMedID 38082205

    View details for PubMedCentralID 7612636

  • Defining left ventricular remodeling using lean body mass allometry: a UK Biobank study. European journal of applied physiology Gomes, B., Hedman, K., Kuznetsova, T., Cauwenberghs, N., Hsu, D., Kobayashi, Y., Ingelsson, E., Oxborough, D., George, K., Salerno, M., Ashley, E., Haddad, F. 2023


    PURPOSE: The geometric patterns of ventricular remodeling are determined using indexed left ventricular mass (LVM), end-diastolic volume (LVEDV) and concentricity, most often measured using the mass-to-volume ratio (MVR). The aims of this study were to validate lean body mass (LBM)-based allometric coefficients for scaling and to determine an index of concentricity that is independent of both volume and LBM.METHODS: Participants from the UK Biobank who underwent both CMR and dual-energy X-ray absorptiometry (DXA) during 2014-2015 were considered (n=5064). We excluded participants aged≥70years or those with cardiometabolic risk factors. We determined allometric coefficients for scaling using linear regression of the logarithmically transformed ventricular remodeling parameters. We further defined a multiplicative allometric relationship for LV concentricity (LVC) adjusting for both LVEDV and LBM.RESULTS: A total of 1638 individuals (1057 female) were included. In subjects with lower body fat percentage (<25% in males,<35% in females, n=644), the LBM allometric coefficients for scaling LVM and LVEDV were 0.85±0.06 and 0.85±0.03 respectively (R2=0.61 and 0.57, P<0.001), with no evidence of sex-allometry interaction. While the MVR was independent of LBM, it demonstrated a negative association with LVEDV in (females: r=-0.44, P<0.001; males: -0.38, P<0.001). In contrast, LVC was independent of both LVEDV and LBM [LVC=LVM/(LVEDV0.40*LBM0.50)] leading to increased overlap between LV hypertrophy and higher concentricity.CONCLUSIONS: We validated allometric coefficients for LBM-based scaling for CMR indexed parameters relevant for classifying geometric patterns of ventricular remodeling.

    View details for DOI 10.1007/s00421-022-05125-9

    View details for PubMedID 36617359