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Classification of AO/OTA 31A/B femur fractures in X-ray images using YOLOv8 and advanced data augmentation techniques. | LitMetric

Classification of AO/OTA 31A/B femur fractures in X-ray images using YOLOv8 and advanced data augmentation techniques.

Bone Rep

Department of Management, Production, and Design, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino 10129, Italy.

Published: September 2024

AI Article Synopsis

  • - Femur fractures are a major public health issue globally, impacting not only patients but also their families due to high rates of complications and fatalities.
  • - This study utilizes an advanced Deep Learning architecture called YOLOv8 to improve the classification of femur fractures, aiming to assist doctors in providing accurate and timely patient care.
  • - The YOLOv8 model showed impressive metrics, achieving 0.9 accuracy and 0.85 for precision, recall, and F1-score, indicating its effectiveness in refining the existing AO/OTA classification system for better diagnosis.

Article Abstract

Femur fractures are a significant worldwide public health concern that affects patients as well as their families because of their high frequency, morbidity, and mortality. When employing computer-aided diagnostic (CAD) technologies, promising results have been shown in the efficiency and accuracy of fracture classification, particularly with the growing use of Deep Learning (DL) approaches. Nevertheless, the complexity is further increased by the need to collect enough input data to train these algorithms and the challenge of interpreting the findings. By improving on the results of the most recent deep learning-based Arbeitsgemeinschaft für Osteosynthesefragen and Orthopaedic Trauma Association (AO/OTA) system classification of femur fractures, this study intends to support physicians in making correct and timely decisions regarding patient care. A state-of-the-art architecture, YOLOv8, was used and refined while paying close attention to the interpretability of the model. Furthermore, data augmentation techniques were involved during preprocessing, increasing the dataset samples through image processing alterations. The fine-tuned YOLOv8 model achieved remarkable results, with 0.9 accuracy, 0.85 precision, 0.85 recall, and 0.85 F1-score, computed by averaging the values among all the individual classes for each metric. This study shows the proposed architecture's effectiveness in enhancing the AO/OTA system's classification of femur fractures, assisting physicians in making prompt and accurate diagnoses.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11422035PMC
http://dx.doi.org/10.1016/j.bonr.2024.101801DOI Listing

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