Automated ultrasound doppler angle estimation using deep learning

• URL: http://arxiv.org/abs/2508.04243v1
• Date: Wed, 06 Aug 2025 09:28:07 GMT
• Title: Automated ultrasound doppler angle estimation using deep learning
• Authors: Nilesh Patil, Ajay Anand,
• Abstract summary: Incorrect angle estimation is a leading cause of error in Doppler-based blood velocity measurements.<n>We propose a deep learning-based approach for automated Doppler angle estimation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Angle estimation is an important step in the Doppler ultrasound clinical workflow to measure blood velocity. It is widely recognized that incorrect angle estimation is a leading cause of error in Doppler-based blood velocity measurements. In this paper, we propose a deep learning-based approach for automated Doppler angle estimation. The approach was developed using 2100 human carotid ultrasound images including image augmentation. Five pre-trained models were used to extract images features, and these features were passed to a custom shallow network for Doppler angle estimation. Independently, measurements were obtained by a human observer reviewing the images for comparison. The mean absolute error (MAE) between the automated and manual angle estimates ranged from 3.9{\deg} to 9.4{\deg} for the models evaluated. Furthermore, the MAE for the best performing model was less than the acceptable clinical Doppler angle error threshold thus avoiding misclassification of normal velocity values as a stenosis. The results demonstrate potential for applying a deep-learning based technique for automated ultrasound Doppler angle estimation. Such a technique could potentially be implemented within the imaging software on commercial ultrasound scanners.

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