Deep learning framework for automated frame selection in kidney ultrasound
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View abstract on PubMed
Summary
This summary is machine-generated.Automated deep learning efficiently selects optimal kidney ultrasound frames, improving diagnostic consistency. The YOLO11x-cls model achieved perfect classification for good quality frames, enhancing clinical workflows.
Area Of Science
- Medical Imaging
- Artificial Intelligence
- Ultrasound Technology
Background
- Manual selection of kidney ultrasound frames is time-consuming and subjective.
- This variability can impact the reliability of clinical assessments.
Purpose Of The Study
- To develop and evaluate an automated deep learning framework for selecting optimal frames from kidney ultrasound videos.
- To enhance the efficiency and consistency of kidney ultrasound interpretation.
Main Methods
- A dataset of 1,203 kidney ultrasound frames from 211 patients was curated and annotated.
- Several convolutional neural network models, including YOLOv11x-cls, were trained and compared for frame classification.
- The YOLO11x-cls model was optimized and evaluated using 5-fold patient-level cross-validation.
Main Results
- The YOLO11x-cls model outperformed baseline architectures, achieving 100% F1-score for good quality frames.
- The framework attained an average cross-validation accuracy of 90% with minimal performance variance.
- The proposed method demonstrates robust and efficient automated best-frame selection.
Conclusions
- The developed YOLO-based deep learning pipeline offers a promising solution for automated best-frame selection in kidney ultrasound.
- This technology can reduce manual effort and improve diagnostic reliability and reproducibility in clinical settings.
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