ReviewFrontiers in cardiovascular medicine2019
Artificial Intelligence for Cardiac Imaging-Genetics Research.
Review in Frontiers in cardiovascular medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 27 citations in OpenAlex.
- Advances in cardiac devices and bioelectronics augmented with artificial intelligence.The Journal of physiology · 2026Review
- Cardiovascular Genetic Epidemiology in the Genome-Wide Era: From Association Discovery to Mechanistic Dissection and Clinical Translation.Cardiovascular drugs and therapy · 2026Review
- Unique Subclavian Vascular Ring Anomaly: Insights from CT Angiography.Life (Basel, Switzerland) · 2025Article
- Cutting-edge AI tools revolutionizing scientific research in life sciences.Biotechnologia · 2025Review
- Genetic basis of right and left ventricular heart shape.Nature communications · 2024Article
- Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week.Journal of the American College of Cardiology · 2024Review
- Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.Journal of Korean medical science · 2023Review
- Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging.PLOS digital health · 2023Article
- Emerging role of artificial intelligence in cardiac electrophysiology.Cardiovascular digital health journal · 2022Review
- Performance of artificial intelligence for biventricular cardiovascular magnetic resonance volumetric analysis in the clinical setting.The international journal of cardiovascular imaging · 2022Article
- Epicardial and pericardial fat analysis on CT images and artificial intelligence: a literature review.Quantitative imaging in medicine and surgery · 2022Review
- The Propagation of Racial Disparities in Cardiovascular Genomics Research.Circulation. Genomic and precision medicine · 2021Review
- A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2021Article
- A Roadmap to Gene Discoveries and Novel Therapies in Monogenic Low and High Bone Mass Disorders.Frontiers in endocrinology · 2021Review
- A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning.BioMed research international · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.
Indexed as
Identifiers
What Socratic 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 Socratic graph.