Deep Learning-Based Stroke Volume Estimation Outperforms Conventional Arterial Contour Method in Patients with Hemodynamic Instability

Deep Learning-Based Stroke Volume Estimation Outperforms Conventional Arterial Contour Method in Patients with Hemodynamic Instability

Although the stroke volume (SV) estimation by arterial blood pressure has been widely used in clinical practice, its accuracy is questionable, especially during periods of hemodynamic instability. We aimed to create novel SV estimating model based on deep-learning (DL) method. A convolutional neural...

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Bibliographic Details
Main Authors: Young-Jin Moon, Hyun S. Moon, Dong-Sub Kim, Jae-Man Kim, Joon-Kyu Lee, Woo-Hyun Shim, Sung-Hoon Kim, Gyu-Sam Hwang, Jae-Soon Choi
Format: Article
Language:English
Published: MDPI AG 2019-09-01
Series:Journal of Clinical Medicine
Subjects:
machine learning
stroke volume
cardiac output
perioperative care
intraoperative monitoring
hemodynamic monitoring
Online Access:https://www.mdpi.com/2077-0383/8/9/1419

Internet

https://www.mdpi.com/2077-0383/8/9/1419

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