What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Magnetic resonance imaging
- Cardiology
Stefan Neubauer focuses on Internal medicine, Cardiology, Magnetic resonance imaging, Endocrinology and Heart failure.
His research on Internal medicine frequently connects to adjacent areas such as Surgery.
His Cardiology research is multidisciplinary, relying on both Radiology and Diastole.
His Magnetic resonance imaging study combines topics in areas such as Cardiac function curve, Nuclear medicine, Heart disease, Pathology and Angiology.
His work focuses on many connections between Heart failure and other disciplines, such as Perfusion, that overlap with his field of interest in Circulatory system.
Stefan Neubauer studied Hypertrophic cardiomyopathy and Left ventricular hypertrophy that intersect with Stenosis.
His most cited work include:
- The failing heart--an engine out of fuel. (1575 citations)
- Left ventricular non-compaction - Insights from cardiovascular magnetic resonance imaging (797 citations)
- Left ventricular non-compaction - Insights from cardiovascular magnetic resonance imaging (797 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Internal medicine, Cardiology, Magnetic resonance imaging, Endocrinology and Heart failure.
Angiology, Ejection fraction, Myocardial infarction, Coronary artery disease and Cardiac function curve are the subjects of his Internal medicine studies.
Stefan Neubauer studies Stroke volume, a branch of Ejection fraction.
Many of his studies on Cardiology involve topics that are commonly interrelated, such as Diastole.
His biological study spans a wide range of topics, including Nuclear medicine, Nuclear magnetic resonance and Pathology.
His study in Creatine kinase, Phosphocreatine, Creatine, Metabolism and Muscle hypertrophy is done as part of Endocrinology.
He most often published in these fields:
- Internal medicine (68.15%)
- Cardiology (53.36%)
- Magnetic resonance imaging (31.49%)
What were the highlights of his more recent work (between 2017-2021)?
- Internal medicine (68.15%)
- Cardiology (53.36%)
- Magnetic resonance imaging (31.49%)
In recent papers he was focusing on the following fields of study:
Stefan Neubauer mostly deals with Internal medicine, Cardiology, Magnetic resonance imaging, Ejection fraction and Artificial intelligence.
His study looks at the intersection of Internal medicine and topics like Gastroenterology with Fatty liver.
Many of his studies involve connections with topics such as Diastole and Cardiology.
The various areas that Stefan Neubauer examines in his Magnetic resonance imaging study include Ventricle, Biobank, Disease and Nuclear medicine.
Stefan Neubauer works mostly in the field of Ejection fraction, limiting it down to concerns involving Cardiac function curve and, occasionally, Cardiac output.
His studies deal with areas such as Fibrosis and Cardiomyopathy as well as Hypertrophic cardiomyopathy.
Between 2017 and 2021, his most popular works were:
- Automated cardiovascular magnetic resonance image analysis with fully convolutional networks (224 citations)
- Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data (170 citations)
- Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data (170 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Magnetic resonance imaging
- Cardiology
Stefan Neubauer mainly focuses on Internal medicine, Cardiology, Magnetic resonance imaging, Artificial intelligence and Segmentation.
His work on Endocrinology expands to the thematically related Internal medicine.
His research brings together the fields of Prospective cohort study and Cardiology.
The study incorporates disciplines such as Ventricle, Angiology, Gastroenterology and In vivo in addition to Magnetic resonance imaging.
His Artificial intelligence research incorporates themes from Biobank, Machine learning and Pattern recognition.
His Image segmentation study in the realm of Segmentation connects with subjects such as Metric.
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