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Related Concept Videos

Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
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To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
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Related Experiment Video

Updated: Sep 5, 2025

Author Spotlight: Overcoming Anti-VEGF Resistance Through Advanced Vascular Morphology Assessment in Choroidal Neovascularization
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Photoplethysmogram based vascular aging assessment using the deep convolutional neural network.

Hangsik Shin1, Gyujeong Noh2,3, Byung-Moon Choi4

  • 1Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. hangsik.shin@gmail.com.

Scientific Reports
|July 5, 2022
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Summary
This summary is machine-generated.

This study introduces a deep learning method using photoplethysmogram (PPG) signals to estimate vascular age, offering a non-invasive approach to cardiovascular risk assessment. The explainable AI model accurately estimates age and identifies key PPG waveform features contributing to vascular aging insights.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiovascular Physiology

Background:

  • Arterial stiffness, a marker of vascular aging, is crucial for cardiovascular risk assessment.
  • Non-invasive methods for evaluating vascular age are needed to complement traditional risk factors.

Purpose of the Study:

  • To develop and validate a deep learning model for non-invasive vascular age estimation using photoplethysmogram (PPG) signals.
  • To enhance the interpretability of the age estimation model using explainable artificial intelligence (AI).

Main Methods:

  • A deep learning model comprising convolutional and fully connected layers was trained on segmented PPG data from 752 adults (aged 20-89).
  • Explainable AI (Grad-Cam) was utilized to visualize the contribution of PPG waveform characteristics to age estimation.
  • Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Pearson's correlation coefficient, and coefficient of determination.

Main Results:

  • The model achieved an MAE of 8.1 years and RMSE of 10.0 years.
  • A Pearson's correlation coefficient of 0.61 and a coefficient of determination of 0.37 were obtained.
  • Grad-Cam analysis highlighted the systolic peak of the PPG waveform as a significant contributor to age estimation.

Conclusions:

  • The developed convolutional neural network-based explainable AI model provides an effective method for vascular age estimation from PPG signals.
  • This approach offers a non-invasive, data-driven alternative for cardiovascular risk assessment, outperforming models requiring manual feature engineering.
  • The explainability feature provides insights into the PPG waveform characteristics driving vascular aging assessment.