The lab: Bijnens & Sitges groups

Lab's research themes:

The group aims at developing and investigating computing for understanding each individual heart, in order to improve diagnosis, prognosis and therapy. We work in an interdisciplinary way combining biomedical engineering, physics, and mathematics with basic research, as well as clinical expertise, in order to link technological and theoretical science with physiological and clinical knowledge.

We combine medical imaging with computer models representing all relevant details of the working heart as well as with knowledge about therapies. Additionally, the wealth of information available from any single individual and groups of patients from routine practice and clinical trials can be integrated in an understandable way by using the latest machine learning and artificial intelligence methods.

Merits of the lab:

The unique strength of the group is the ability to combine the latest insights in computing, image analysis, and data science in order to address relevant clinical problems.

For this, we start from a current clinical question in cardiovascular diseases and analyse it first from the pathophysiological point of view. From here, we devise image and data analysis approaches that will help to study the relevant disease processes for the problem under investigation.

This has already lead to relevant contributions in heart failure, cardiac hypertrophy, coronary artery disease, aortic diseases and fetal and paediatric cardiology. Our published results cover clinical cardiology as well as engineering and includes the development of clinician friendly software platforms for data analysis.

Why do we want medical doctors?

Our group is inherently multidisciplinary with profiles ranging from clinical cardiology/paediatrics/obstetrics to engineering, physics and mathematics.

Country: Spain

The position

What’s the main purpose of our research?

Deciphering the underlying cause of left ventricular hypertrophy (LVH) is a highly relevant clinical challenge given the need for a highly different therapeutic approach depending on the aetiology. This requires a combination of an extensive clinical as well as familial assessment, including multimodality imaging and genetics. While cardiac magnetic resonance imaging and genetics contribute to the assessment, they are also expensive and time-consuming. Finding approaches that provide more pathophysiological insight based on clinical combined with echocardiographic and electrocardiographic information would provide a way forward for individualised temporal diagnosis and follow-up.

How we will do it?

Machine learning (ML), by analysing and integrating the full spectrum of accessible data sources (electrocardiogram and echography), together with clinical parameters, might provide a helpful tool. Currently, most ML research relies on deep learning (DL) techniques, with good results, but hard to interpret clinically. Representation learning (RL) allows to find representative and simplified patterns in the complex data available, by organizing individuals by their similarity in the original data. The aim of the current project is to evaluate and validate the clinical value of RL to the whole-cardiac cycle echocardiographic data, compare this approach to DL classification, and explore the added value of the combinatorial use of clinical, ECG and echocardiographic data through ML to identify the cause of unexplained LVH in daily cardiological practice. his will include a multicentre clinical study.

Why is this important?

By exploring the phenotypic presentation of patients with hypertrophic cardiomyopathy, we will create a framework to better categorize patients, stratify their clinical risk, and allow earlier recognition of the disease in genotypic positive patients. In this project, we also aim to better discriminate hypertrophic aetiologies such as (familial) hypertrophic cardiomyopathy and amyloidosis patients from other non-hereditary LVH aetiologies, as well as improve the clinical description of patients presenting with coexisting conditions (like hypertension or aortic valve stenosis).

Who is a good fit for the project?

Experience in clinical cardiac imaging. Basic knowledge of machine learning. Clinical specialist or resident in Cardiology.


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