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Related Concept Videos

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Related Experiment Video

Updated: Jan 11, 2026

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Deep Learning-Enabled Diagnosis of Abdominal Aortic Aneurysm Using Pulse Volume Recording Waveforms: An In Silico

Sina Masoumi Shahrbabak1, Byeng Dong Youn2,3, Hao-Min Cheng4

  • 1Mechanical Engineering, University of Maryland, College Park, MD 20742, USA.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary

Deep learning analysis of non-invasive pulse volume recording (PVR) signals shows promise for diagnosing abdominal aortic aneurysm (AAA). This cost-effective approach could enable accessible AAA screening using point-of-care technologies.

Keywords:
abdominal aortic aneurysmarterial pulse waveformdeep learningmachine learningpoint-of-care diagnosticspulse volume recording

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Cardiovascular Diagnostics

Background:

  • Abdominal aortic aneurysm (AAA) diagnosis often relies on invasive or complex imaging techniques.
  • Non-invasive methods for early AAA detection are crucial for improving patient outcomes.
  • Pulse waveform analysis offers a potential avenue for accessible cardiovascular assessment.

Purpose of the Study:

  • To investigate the feasibility of diagnosing abdominal aortic aneurysm (AAA) using deep learning (DL) analysis of non-invasive pulse volume recording (PVR) signals.
  • To develop and evaluate a DL algorithm for estimating AAA severity from PVR waveforms.
  • To compare the performance of a PVR-based DL algorithm against one trained on invasive arterial blood pressure (BP) signals.

Main Methods:

  • Generated synthetic arterial blood pressure (BP) and pulse volume recording (PVR) waveform signals using a systemic arterial circulation model.
  • Simulated various severities of abdominal aortic aneurysm (AAA) within the synthetic cohort.
  • Developed a convolutional neural network (CNN) with continuous property-adversarial regularization for AAA severity estimation from PVR signals.
  • Validated the synthetic data against in vivo findings and compared DL algorithm performance with invasive BP data.

Main Results:

  • The DL-enabled PVR-based algorithm demonstrated robust AAA detection with an area under the ROC curve >0.89.
  • The algorithm showed reasonable accuracy in estimating AAA severity from PVR signals (MAE: 12.6%).
  • Performance was comparable, though slightly lower than, an identical CNN trained on invasive arterial BP signals (MAE: 10.3%).

Conclusions:

  • Deep learning analysis of non-invasive PVR waveform signals presents a feasible, cost-effective method for AAA diagnosis.
  • This approach has the potential to facilitate accessible AAA screening.
  • Operator-agnostic, point-of-care PVR analysis could significantly enhance early detection and management of AAA.