Jorge Corral Acero BSc DPhil MSc

Dr. Jorge Corral Acero graduated, M.S. and B.S., in Chemical Engineering at University of Valladolid, conducted the master thesis at Imperial College of London and completed post-master research stays at UC Berkeley (neuroengineering) and Harvard Medical School (chest imaging).

He was awarded a DPhil in Engineering Science at Oxford University as a Marie Skłodowska-Curie Fellow and is currently funded by the EPSRC British Research Council as a postdoctoral researcher in cardiac computational medicine. His research deploys state-of-art engineering tools to gain a better understanding of how cardiac anatomy modulates human cardiovascular diseases.

Jorge has a wide and diverse international working experience from junior researcher in 6 different research groups (Biomedical Image Analysis, ACIL, Maharbiz´s Group, CPSE, HPP and BIOFORGE) to secondments in 3 companies (IBM, Michelin and Lesaffre). He also actively collaborates with the John Radcliffe hospital and the German Centre for Cardiovascular Research.

• Nominated to the 2021 Jagat Narula Award for Outstanding Scholarship of the Journal of the American College of Cardiology
• Best paper in medical imaging, Functional Imaging and Modelling of the Heart 2019.
• First prize, Left Ventricle Full Quantification Challenge MICCAI 2019.
• EFCE Student Mobility Award 2017: Ranked #2 best European Student of chemical engineering during 2015/16 and 2016/17.
• Berkeley Excellence Program: Selected as 1 of the 3 students from a consortium of 40 European universities to participate in the Berkeley Excellence Program to study in the academic year 2016/2017 at University of California, Berkeley.
• Excellence Scholarship: Ranked #1 most promising international student of the University of Valladolid in 2015 and in 2016.
• Jumping Talent 2016: Selected 1 of the 96 most talented Spanish university students.
• COGITIVA award: #1 industrial engineer of University of Valladolid.

• TEDx Youth@EEB3
• Academia & Industry debate moderator. MICCAI 2020.
• Kings College of London
• University of Oxford; Best Paper Award
• University of Oxford; 3D pattern linked to infarct risks identified by researchers
• Diario de Valladolid 

In the current socio-political framework, nations are facing tightening financial constraints and an aging population. Given this scenario, preventive and personalised medicine is crucial to improve both the efficiency and efficacy of healthcare systems. This is particularly important in cardiovascular disease (CVD), the world leading cause of death, responsible for 42% of the European mortality and costing 169€ billion/year only in Europe. CVD is usually associated with changes in the structure and function of the heart. Nevertheless, their interplay in modulating disease outcomes is not fully understood, resulting in suboptimal treatments. My personal research interest is therefore to gain a better understanding of cardiac structural and functional remodelling, to contribute towards early-stage disease identification and personalised medicine.

This remodelling has been proven to impact survival after acute myocardial infarction (AMI), a disease that accounts for millions of deaths worldwide per year. The burden of AMI and the limited understanding of the way remodelling patterns affect survival is a direct motivation for my research.

Large population studies have been facilitated by the recent expansion of big data in cardiovascular medicine. Likewise, increasing computational power has boosted cardiac modelling, enabling the concept of the ‘digital twin’ of a patient towards precision cardiology. Facilitated by this large dataset and computational power availability, recent developments in deep learning (DL) are revolutionising the medical imaging field. They remove the burden of manual analysis and expand the possibilities of accurate and automated analysis of cardiac images, including cardiac magnetic resonance (CMR) datasets, the gold standard for quantitative assessment of the heart. Altogether, these advances facilitate the integration of anatomy with function in 3D computational models to further disentangle the anatomical features and contraction patterns that modulate CVD outcomes.

The final motivation of my research is the translation to clinical practice, by addressing methodological challenges that allow building knowledge and ultimately improving patient management.

A scenario of breakthroughs emerges, and I am excited to be part of it.

• PIC, Personalised In-Silico Cardiology.
• Our flagship publication, “The ‘Digital Twin’ to enable the vision of precision cardiology” 
• Understanding and Improving Risk Assessment After Myocardial Infarction Using Automated Left Ventricular Shape Analysis: The leading publication is publicly available at: JACC Cardiovascular Imaging