What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Cardiology
- Heart failure
Bart Bijnens mostly deals with Cardiology, Internal medicine, Strain rate, Systole and Strain rate imaging.
His Cardiology research is multidisciplinary, incorporating elements of Basal and Diastole.
His research in Strain rate intersects with topics in Anatomy, Deformation, Ultrasonic sensor, Biomedical engineering and Heart rate.
Bart Bijnens has included themes like Hemodynamics, Aortic valve stenosis, Asymptomatic and Perfusion in his Systole study.
His Strain rate imaging research includes themes of Ultrasound and Medical imaging.
His Heart disease study combines topics from a wide range of disciplines, such as QRS complex, Ventricular dyssynchrony and Dilated cardiomyopathy.
His most cited work include:
- Regional Strain and Strain Rate Measurements by Cardiac Ultrasound: Principles, Implementation and Limitations (851 citations)
- Strain and strain rate imaging: a new clinical approach to quantifying regional myocardial function. (519 citations)
- Myocardial function defined by strain rate and strain during alterations in inotropic states and heart rate (334 citations)
What are the main themes of his work throughout his whole career to date?
Bart Bijnens spends much of his time researching Internal medicine, Cardiology, Strain rate imaging, Strain rate and Artificial intelligence.
His Internal medicine study often links to related topics such as Deformation.
His Cardiology research integrates issues from Diastole and Systole.
His Strain rate research incorporates themes from Ultrasonic sensor and Deformation.
His study explores the link between Ultrasonic sensor and topics such as Biomedical engineering that cross with problems in Ultrasound.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Computer vision and Pattern recognition.
He most often published in these fields:
- Internal medicine (62.65%)
- Cardiology (61.85%)
- Strain rate imaging (12.85%)
What were the highlights of his more recent work (between 2016-2021)?
- Internal medicine (62.65%)
- Cardiology (61.85%)
- Artificial intelligence (10.71%)
In recent papers he was focusing on the following fields of study:
Bart Bijnens mainly investigates Internal medicine, Cardiology, Artificial intelligence, Ventricle and Fetus.
His work on Ventricular pressure, Ejection fraction and Cardiac function curve as part of general Internal medicine study is frequently linked to In patient and Basal septal hypertrophy, therefore connecting diverse disciplines of science.
Bart Bijnens studies Cardiology, namely Ventricular remodeling.
His Ventricle study combines topics in areas such as Stroke volume, Simulation and Anatomy.
His study looks at the relationship between Stroke volume and topics such as BLOOD FILLED, which overlap with Heart failure.
His work deals with themes such as Diabetes mellitus, Blood flow, Cohort study and Heart disease, which intersect with Fetus.
Between 2016 and 2021, his most popular works were:
- Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. (55 citations)
- Machine learning analysis of left ventricular function to characterize heart failure with preserved ejection fraction (47 citations)
- Persistence of Cardiac Remodeling in Preadolescents With Fetal Growth Restriction. (32 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Cardiology
- Artificial intelligence
His main research concerns Cardiology, Internal medicine, Ventricle, Endurance training and Artificial intelligence.
His specific area of interest is Cardiology, where Bart Bijnens studies Ventricular remodeling.
He integrates Internal medicine and In patient in his studies.
Bart Bijnens interconnects Stroke volume and Anatomy in the investigation of issues within Ventricle.
His Endurance training study integrates concerns from other disciplines, such as Fibrosis, Treadmill, Athlete's heart and Sports medicine.
His studies in Artificial intelligence integrate themes in fields like Surface, Computer vision, Heart failure with preserved ejection fraction, Machine learning and Topology.
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