# Automated ultrasound doppler angle estimation using deep learning

Source: [Automated ultrasound doppler angle estimation using deep learning](<https://www.sota2.com/research/paper/68fa7a721e209131cea83481>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-17T05:00:05.807Z

Authors: Nilesh Patil, Ajay Anand

Published: 2025-08-06T09:28:07.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Doppler-angle estimation on Rotation-augmented image corpus Image-level sampling](<https://www.sota2.com/research/sota/doppler-angle-estimation-on-rotation-augmented-image-corpus-image-level-sampling.md>): Doppler-angle estimation; Rotation-augmented image corpus Image-level sampling; MAE (°): 1.96.

- [Doppler-angle estimation on Rotation-augmented image corpus Patient-level sampling](<https://www.sota2.com/research/sota/doppler-angle-estimation-on-rotation-augmented-image-corpus-patient-level-sampling.md>): Doppler-angle estimation; Rotation-augmented image corpus Patient-level sampling; MAE (°): 5.93.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2508.04243>)

[PDF](<https://arxiv.org/pdf/2508.04243.pdf>)