arXiv:2508.04243v2 Announce Type: replace-cross 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 s
The increasing maturity of deep learning techniques allows for their application to complex medical image analysis problems that previously relied on manual or less precise methods.
Accurate angle estimation in Doppler ultrasound is critical for reliable blood velocity measurements, impacting clinical diagnoses and treatment decisions.
This development introduces an automated, potentially more accurate, and less operator-dependent method for a key step in Doppler ultrasound diagnostics.
- · Medical AI companies
- · Cardiovascular diagnostics
- · Patients with vascular conditions
- · Ultrasound equipment manufacturers
- · Ultrasound technicians relying on manual angle estimation
Improved diagnostic accuracy and efficiency in vascular assessment using Doppler ultrasound.
Reduced variability in diagnoses, leading to more standardized and potentially earlier interventions for patients.
Integration of similar AI-driven improvements across a wider range of medical imaging modalities, driving overall healthcare automation and precision.
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Read at arXiv cs.AI