A deep learning framework using clinical median images reduced human review time for detecting uniformity artifacts in ultrasound transducers by approximately 80% while preserving detection accuracy.
Does a deep learning framework accurately detect uniformity artifacts in ultrasound transducers and reduce human review time?
A deep learning framework can automate ultrasound transducer artifact detection, reducing quality control review time by 80% while maintaining accuracy.
The deep learning framework using clinical median images demonstrated robust performance across several linear and curvilinear transducer models. It could be integrated into the clinical QC workflow by automating artifact detection in an effective and timely manner. In our practice, it can flag median images classified as artifact-present, reducing human review time by approximately 80% while preserving detection accuracy.
Gu et al. (Wed,) conducted a other in Uniformity Artifacts in Ultrasound Transducers. Deep learning framework vs. Human review was evaluated on Artifact detection and human review time. A deep learning framework using clinical median images reduced human review time for detecting uniformity artifacts in ultrasound transducers by approximately 80% while preserving detection accuracy.