ReviewMedical image analysis2016
Cardiac image modelling: Breadth and depth in heart disease.
Review in Medical image analysis, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease.Medical image analysis · 2022Article
- Can the WHO 's recommendations of physical activity volume decrease the risk of heart disease in middle and older aged Chinese People: the evidence from a seven year longitudinal survey.BMC geriatrics · 2022Article
- Applications of artificial intelligence in cardiovascular imaging.Nature reviews. Cardiology · 2021Review
- AI tracks a beating heart's function over time.Nature · 2020Article
- Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs.Frontiers in cardiovascular medicine · 2020Article
- The New Wave of Cardiovascular Biomechanics.JACC. Cardiovascular imaging · 2019Article
- Computational models in cardiology.Nature reviews. Cardiology · 2019Review
- Quest for cardiovascular interventions: precise modeling and 3D printing of heart valves.Journal of biological engineering · 2019Review
- SiSSR: Simultaneous subdivision surface registration for the quantification of cardiac function from computed tomography in canines.Medical image analysis · 2018Article
- Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results.The international journal of cardiovascular imaging · 2018Article
- Submillimeter diffusion tensor imaging and late gadolinium enhancement cardiovascular magnetic resonance of chronic myocardial infarction.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2017Article
- A Combined Random Forests and Active Contour Model Approach for Fully Automatic Segmentation of the Left Atrium in Volumetric MRI.BioMed research international · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
With the advent of large-scale imaging studies and big health data, and the corresponding growth in analytics, machine learning and computational image analysis methods, there are now exciting opportunities for deepening our understanding of the mechanisms and characteristics of heart disease. Two emerging fields are computational analysis of cardiac remodelling (shape and motion changes due to disease) and computational analysis of physiology and mechanics to estimate biophysical properties from non-invasive imaging. Many large cohort studies now underway around the world have been specifically designed based on non-invasive imaging technologies in order to gain new information about the development of heart disease from asymptomatic to clinical manifestations. These give an unprecedented breadth to the quantification of population variation and disease development. Also, for the individual patient, it is now possible to determine biophysical properties of myocardial tissue in health and disease by interpreting detailed imaging data using computational modelling. For these population and patient-specific computational modelling methods to develop further, we need open benchmarks for algorithm comparison and validation, open sharing of data and algorithms, and demonstration of clinical efficacy in patient management and care. The combination of population and patient-specific modelling will give new insights into the mechanisms of cardiac disease, in particular the development of heart failure, congenital heart disease, myocardial infarction, contractile dysfunction and diastolic dysfunction.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.