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Related Experiment Video

Updated: Jan 19, 2026

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis
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A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

Published on: August 12, 2025

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

Young-Jin Moon1, Hyun S Moon2, Dong-Sub Kim3

  • 1Biosignal Analysis and Perioperative Outcome Research Laboratory, Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea. yjmoon@amc.seoul.kr.

Journal of Clinical Medicine
|September 12, 2019
PubMed
Summary

A new deep-learning model accurately estimates stroke volume from arterial blood pressure, especially during critical hemodynamic changes in liver transplant surgeries. This advanced model offers superior precision for intraoperative hemodynamic management.

Keywords:
cardiac outputhemodynamic monitoringintraoperative monitoringmachine learningperioperative carestroke volume

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Area of Science:

  • Cardiology
  • Medical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Stroke volume (SV) estimation from arterial blood pressure (ABP) is crucial but often inaccurate during hemodynamic instability.
  • Existing methods face challenges in precision, particularly in complex surgical settings like liver transplantation.
  • Deep learning (DL) offers a potential solution for improving SV estimation accuracy.

Purpose of the Study:

  • To develop and validate a novel deep-learning model for estimating stroke volume using ABP waveform data.
  • To compare the performance of the DL model against a pre-existing commercialized model (EV1000).
  • To assess the model's efficacy in estimating SV during periods of significant hemodynamic instability.

Main Methods:

  • A convolutional neural network (CNN) was employed to estimate SV from ABP waveforms.
  • The DL model was trained and validated using a large dataset (484,384 samples) from liver transplantation surgeries, with pulmonary artery thermodilution as the gold standard.
  • Performance was evaluated using correlation and concordance analyses on an independent dataset (491,353 samples).

Main Results:

  • The DL model demonstrated acceptable overall performance (r = 0.813, concordance = 74.15%).
  • During the reperfusion phase, a period of severe hemodynamic instability, the DL model significantly outperformed the EV1000 model (correlation: 0.861 vs. 0.570; concordance: 90.6% vs. 75.8%).
  • The DL model showed superior accuracy in situations with rapid hemodynamic changes.

Conclusions:

  • The developed DL-based model provides a more accurate estimation of intraoperative stroke volume compared to existing methods.
  • This model has the potential to guide physicians towards precise intraoperative hemodynamic management.
  • The DL model is particularly promising for managing rapid hemodynamic fluctuations, offering significant clinical utility.