ReviewBiomedicines2022
Interplay between Artificial Intelligence and Biomechanics Modeling in the Cardiovascular Disease Prediction.
Review in Biomedicines, 2022. 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.
- An artificial intelligence and numerical simulation-based strategy for multi-objective optimization of hemodynamics in curved vessels.Physical and engineering sciences in medicine · 2026Article
- Machine Learning Models for Objective Assessment of Vascular Anastomoses Using Computational Fluid Dynamics for Surgical Skill Training-A Retrospective Study.Journal of clinical medicine · 2026Article
- Deep learning for cardiovascular disease: a comprehensive review of detection and risk forecasting.Frontiers in artificial intelligence · 2026Review
- Biomechanical research using advanced micro-nano devices: In-Vitro cell Characterization focus.Journal of advanced research · 2025Review
- Digital twins and Big AI: the future of truly individualised healthcare.NPJ digital medicine · 2025Article
- Predicting lower body joint moments and electromyography signals using ground reaction forces during walking and running: An artificial neural network approach.Gait & posture · 2025Article
- The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease.Biomedicines · 2025Review
- Computational modeling of drug-eluting balloons in peripheral artery disease: Mechanisms, optimization, and translational insights.Computational and structural biotechnology journal · 2025Review
- The next frontier in healthcare: perspectives and discussion on building trust and societal acceptance of digital humans in the essential framework of impact biomechanics.Frontiers in bioengineering and biotechnology · 2025Review
- How Gait Nonlinearities in Individuals Without Known Pathology Describe Metabolic Cost During Walking Using Artificial Neural Network and Multiple Linear Regression.Applied sciences (Basel, Switzerland) · 2024Article
- Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials.Cardiovascular engineering and technology · 2024Review
- Technological Advances in the Diagnosis of Cardiovascular Disease: A Public Health Strategy.International journal of environmental research and public health · 2024Review
- Ground Reaction Forces and Joint Moments Predict Metabolic Cost in Physical Performance: Harnessing the Power of Artificial Neural Networks.Applied sciences (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Cardiovascular disease (CVD) is the most common cause of morbidity and mortality worldwide, and early accurate diagnosis is the key point for improving and optimizing the prognosis of CVD. Recent progress in artificial intelligence (AI), especially machine learning (ML) technology, makes it possible to predict CVD. In this review, we first briefly introduced the overview development of artificial intelligence. Then we summarized some ML applications in cardiovascular diseases, including ML-based models to directly predict CVD based on risk factors or medical imaging findings and the ML-based hemodynamics with vascular geometries, equations, and methods for indirect assessment of CVD. We also discussed case studies where ML could be used as the surrogate for computational fluid dynamics in data-driven models and physics-driven models. ML models could be a surrogate for computational fluid dynamics, accelerate the process of disease prediction, and reduce manual intervention. Lastly, we briefly summarized the research difficulties and prospected the future development of AI technology in cardiovascular diseases.
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.