气候变化领域集成服务门户(CCPortal): Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease

CCPortal

DOI	10.1073/PNAS.2100697118
	Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease
	Cai S.; Li H.; Zheng F.; Kong F.; Dao M.; Karniadakis G.E.; Suresh S.
发表日期	2021
ISSN	00278424
卷号	118 期号:13
英文摘要	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-achip, 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. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Blood flow in microaneurysm; Deep learning; Diabetic retinopathy; Image analysis; Three-dimensional flow fields
语种	英语
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180122
作者单位	Division of Applied Mathematics, Brown University, Providence, RI 02912, United States; Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States; School of Biological Sciences, Nanyang Technological University, Singapore, 639798, Singapore; School of Engineering, Brown University, Providence, RI 02912, United States; Nanyang Technological University, Singapore, 639798, Singapore
推荐引用方式 GB/T 7714	Cai S.,Li H.,Zheng F.,et al. Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease[J],2021,118(13).
APA	Cai S..,Li H..,Zheng F..,Kong F..,Dao M..,...&Suresh S..(2021).Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease.Proceedings of the National Academy of Sciences of the United States of America,118(13).
MLA	Cai S.,et al."Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease".Proceedings of the National Academy of Sciences of the United States of America 118.13(2021).