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Related Concept Videos

Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

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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A Methodological Approach to Non-invasive Assessments of Vascular Function and Morphology
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Neural network-based arterial diameter estimation from ultrasound data.

Zhuangzhuang Yu1,2, Manolis Sifalakis1, Borbála Hunyadi2

  • 1Department of Signal Processing & Modelling, imec The Netherlands / Holst Centre, Eindhoven, The Netherlands.

PLOS Digital Health
|December 2, 2024
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Summary

Machine learning models accurately track carotid artery diameter from ultrasound, aiding cardiovascular disease prevention. This automated approach minimizes errors and is suitable for portable monitoring.

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Machine Learning

Background:

  • Cardiovascular diseases are a leading cause of mortality.
  • Early detection of carotid artery abnormalities via ultrasound is crucial for prevention.
  • Automated carotid diameter waveform extraction is essential for hemodynamic analysis but challenging due to physiological variability.

Purpose of the Study:

  • Develop data-driven machine learning (ML) models for automated carotid diameter extraction from ultrasound data.
  • Minimize computational complexity for deployment in embedded systems.
  • Track carotid artery diameter without requiring clinician annotation or handcrafted heuristics.

Main Methods:

  • A ML pipeline using two convolutional neural network (NN) models and a smoothing filter was developed.
  • The first NN detects the region of interest (ROI), and the second NN tracks the arterial diameter.
  • Ultrasound signals were acquired at 500Hz, and the ML pipeline was trained using a digital signal processing (DSP)-based approach as reference.

Main Results:

  • The ML pipeline achieved near-perfect temporal alignment with the DSP reference waveform (Pearson correlation coefficient r = 0.87).
  • The mean absolute deviation of arterial diameter prediction was 0.077 mm (1% error).
  • The NN-based approach demonstrated robustness against drift and artifacts.

Conclusions:

  • The proposed ML pipeline offers a fully automated, accurate, and computationally efficient method for carotid artery diameter tracking.
  • This approach eliminates the need for specialist intervention and manual fine-tuning, unlike current clinical practices and conventional DSP methods.
  • The ML pipeline's trainability on small datasets and suitability for A-mode ultrasound frames make it promising for miniaturization and on-line clinical/ambulatory monitoring.