ArticleProceedings of the National Academy of Sciences of the United States of America2021
Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 118 citations in OpenAlex.
- Pooled it
- Advances in Machine Learning-Enhanced PBPK Models for Brain-Targeted Drug Delivery via Nanocarriers: A Comprehensive Review.Journal of functional biomaterials · 2026Review
- Acoustic separation and isolation of viruses, small extracellular vesicles and other nanoscale bioparticles.Nature protocols · 2026Review
- MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI.Science advances · 2026Article
- Robust MR-AIV: A Systematic Study of Robustness Improvement and Sensitivity Analysis of MR-AIV.bioRxiv : the preprint server for biology · 2026Article
- Recent Advances in Modeling and Prediction of Blood Glucose in Type 1 Diabetes.Delaware journal of public health · 2026Article
- AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip.Acta pharmaceutica Sinica. B · 2026Review
- Learning in PINNs: Phase transition, diffusion equilibrium, and generalization.Neural networks : the official journal of the International Neural Network Society · 2026Article
- Artificial Intelligence-Aided Microfluidic Cell Culture Systems.Biosensors · 2025Review
- KKANs: Ku̇rková-Kolmogorov-Arnold networks and their learning dynamics.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Numerical simulation of red blood cells migration and platelets margination for blood flow in micro-vessels with fusiform aneurysms.Scientific reports · 2025Article
- MR-AIV revealsbioRxiv : the preprint server for biology · 2025Article
- Organoid-based tissue engineering for advanced tissue repair and reconstruction.Materials today. Bio · 2025Review
- From PINNs to PIKANs: recent advances in physics-informed machine learning.Machine learning for computational science and engineering · 2025Article
- AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks.Science advances · 2025Article
- Article
- Evaluating Blood Flow Speed in Retinal Microaneurysms Secondary to Diabetic Retinopathy Using Variable Interscan Time Analysis OCTA.Translational vision science & technology · 2025Article
- InferringInterface focus · 2024Article
- Coagulo-Net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks.Neural networks : the official journal of the International Neural Network Society · 2024Article
- Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Understanding the mechanics of blood flow is necessary for developing insights into mechanisms of physiology and vascular diseases in microcirculation. Given the limitations of technologies available for assessing in vivo flow fields, in vitro methods based on traditional microfluidic platforms have been developed to mimic physiological conditions. However, existing methods lack the capability to provide accurate assessment of these flow fields, particularly in vessels with complex geometries. Conventional approaches to quantify flow fields rely either on analyzing only visual images or on enforcing underlying physics without considering visualization data, which could compromise accuracy of predictions. Here, we present artificial-intelligence velocimetry (AIV) to quantify velocity and stress fields of blood flow by integrating the imaging data with underlying physics using physics-informed neural networks. We demonstrate the capability of AIV by quantifying hemodynamics in microchannels designed to mimic saccular-shaped microaneurysms (microaneurysm-on-a-chip, or MAOAC), which signify common manifestations of diabetic retinopathy, a leading cause of vision loss from blood-vessel damage in the retina in diabetic patients. We show that AIV can, without any a priori knowledge of the inlet and outlet boundary conditions, infer the two-dimensional (2D) flow fields from a sequence of 2D images of blood flow in MAOAC, but also can infer three-dimensional (3D) flow fields using only 2D images, thanks to the encoded physics laws. AIV provides a unique paradigm that seamlessly integrates images, experimental data, and underlying physics using neural networks to automatically analyze experimental data and infer key hemodynamic indicators that assess vascular injury.
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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.