ArticleScientific reports2020
Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Screening Patients for Elevated Left Ventricular End-Diastolic Pressure Using a Novel Noninvasive Brachial Cuff-ECG System: A Multicenter Validation Study.Journal of the American Heart Association · 2026Article
- A Computationally Efficient and Causal Frequency Domain Formalism for Hemodynamics Allowing for Nonlinearities and Generalized Coupling Conditions.International journal for numerical methods in biomedical engineering · 2025Article
- Personalized pulse wave propagation modeling to improve vasopressor dosing management in patients with severe traumatic brain injury.PLoS computational biology · 2025Article
- Developing technologies to assess vascular ageing: a roadmap from VascAgeNet.Physiological measurement · 2024Review
- Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research.American journal of physiology. Heart and circulatory physiology · 2024Review
- Non-invasive assessment of stroke volume and cardiovascular parameters based on peripheral pressure waveform.PLoS computational biology · 2024Article
- Novel theory and potential applications of central diastolic pressure decay time constant.Scientific reports · 2024Article
- Noninvasive hemodynamic indices of vascular aging: an in silico assessment.American journal of physiology. Heart and circulatory physiology · 2023Article
- Instantaneous detection of acute myocardial infarction and ischaemia from a single carotid pressure waveform in rats.European heart journal open · 2023Article
- Non-Invasive Pressure-Only Aortic Wave Intensity Evaluation Using Hybrid Fourier Decomposition-Machine Learning Approach.IEEE transactions on bio-medical engineering · 2023Article
- Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach.Sensors (Basel, Switzerland) · 2023Article
- Cardiac output estimated from an uncalibrated radial blood pressure waveform: validation in anFrontiers in bioengineering and biotechnology · 2023Article
- Prediction of Visual Acuity after anti-VEGF Therapy in Diabetic Macular Edema by Machine Learning.Journal of diabetes research · 2022Article
- Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research.European heart journal. Digital health · 2021Article
- Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: AnFrontiers in bioengineering and biotechnology · 2021Article
- Estimation of Left Ventricular End-Systolic Elastance From Brachial Pressure WaveformFrontiers in bioengineering and biotechnology · 2021Article
- AI-Based Estimation of End-Systolic Elastance From Arm-Pressure and Systolic Time Intervals.Frontiers in artificial intelligence · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac and aortic characteristics are crucial for cardiovascular disease detection. However, noninvasive estimation of aortic hemodynamics and cardiac contractility is still challenging. This paper investigated the potential of estimating aortic systolic pressure (aSBP), cardiac output (CO), and end-systolic elastance (E
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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.