ReviewBiophysical reviews2023
Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning.
Review in Biophysical reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
What it found
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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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in computational modeling of thrombosis: Bridging mechanistic insights and clinical translation.Journal of thrombosis and thrombolysis · 2026Pooled it
- Lipid monitoring using non-invasive measurement technologies and machine learning: a systematic review.Archives of gynecology and obstetrics · 2026Pooled it
- Multimodal Fusion of Echocardiogram Images and Electronic Medical Records for Heart Disease Screening: Retrospective Algorithm Development and Validation Study.JMIR medical informatics · 2026Article
- Coupling CFD and Machine Learning to Assess Flow Properties in Porous Scaffolds for Tissue Engineering.Micromachines · 2025Article
- Sensitivity of Left Atrial Flow Dynamics to Echocardiographic and Computed Tomography Data.Journal of cardiovascular translational research · 2025Article
- Review and Comparative Evaluation of Mobile Apps for Cardiovascular Risk Estimation: Usability Evaluation Using mHealth App Usability Questionnaire.JMIR mHealth and uHealth · 2025Review
- PINNing cerebral blood flow: analysis of perfusion MRI in infants using physics-informed neural networks.Frontiers in network physiology · 2025Article
- Effect of upper body venoarterial ECMO on systemic hemodynamics and oxygenation: A computational study.Computers in biology and medicine · 2024Article
- The Role of Visualization in Estimating Cardiovascular Disease Risk: Scoping Review.JMIR public health and surveillance · 2024Article
- CardioRiskNet: A Hybrid AI-Based Model for Explainable Risk Prediction and Prognosis in Cardiovascular Disease.Bioengineering (Basel, Switzerland) · 2024Article
- Systematic characterization and automated alignment of coronary tree geometries.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases.Diagnostics (Basel, Switzerland) · 2024Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and scanning time of current blood flow imaging techniques, and accurate detection of cardiovascular diseases using a plethora of data collected by wearable sensors.
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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.