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<p>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.</p>

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Deep learning framework for automated frame selection in kidney ultrasound

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月25日

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  • Amirali Seraj,
  • Seyed Pedram Monazami,
  • Raheleh Davoodi,
  • Javad Seraj,
  • Hadi Ghattan Kashani,
  • Abdoulreza Sajjadian Moosavi,
  • Masoud Shariat Panahi

摘要

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.