Abstract
Manual selection of optimal frames from kidney ultrasound videos is a time-consuming and subjective process that can introduce variability into clinical assessments. This study presents a fully automated deep learning-based framework designed to identify the most diagnostically informative frames, thereby enhancing the efficiency and consistency of kidney ultrasound interpretation. A curated dataset of 1,203 frames from 211 patients was constructed and annotated by clinical experts into three quality-based categories: Good, Bad, and Null. Multiple convolutional neural network models-including InceptionV3, ResNet34/50, EfficientNet, VGG16, YOLOv8x-cls and YOLO11x-cls-were trained and systematically compared for the task of frame classification. The YOLO11x-cls model, optimized using multi-class cross-entropy loss and evaluated through 5-fold patient-level cross-validation, consistently outperformed the baseline architectures. It achieved perfect classification metrics (F1-score of 100%) on the Good class. Additionally, YOLO11x-cls attained the highest average cross-validation accuracy (90%) with minimal performance variance across folds. These results highlight the potential of the proposed YOLO-based pipeline as a robust and efficient solution for automated best-frame selection in kidney ultrasound imaging. The method holds promise for integration into clinical workflows, where it can reduce manual effort and improve diagnostic reliability and reproducibility.