Amirouche Farid, Prosper Aashik Mathew, Mzeihem Majd
Department of Orthopaedic Surgery, University of Illinois at Chicago, Chicago, IL, USA.
Orthopaedic and Spine Institute, Department of Orthopaedic Surgery, Northshore University Health System, University of Chicago Pritzker School of Medicine, 9669 Kenton Avenue, Skokie, IL, 60076, USA.
BMC Med Imaging. 2025 Aug 5;25(1):316. doi: 10.1186/s12880-025-01669-2.
In emergency departments, residents and physicians interpret X-rays to identify fractures, with distal radius fractures being the most common in children. Skilled radiologists typically ensure accurate readings in well-resourced hospitals, but rural areas often lack this expertise, leading to lower diagnostic accuracy and potential delays in treatment. Machine learning systems offer promising solutions by detecting subtle features that non-experts might miss. Recent advancements, including YOLOv8 and its attention-mechanism models, YOLOv8-AM, have shown potential in automated fracture detection. This study aims to refine the YOLOv8-AM model to improve the detection of distal radius fractures in pediatric patients by integrating targeted improvements and new attention mechanisms.
We enhanced the YOLOv8-AM model to improve pediatric wrist fracture detection, maintaining the YOLOv8 backbone while integrating attention mechanisms such as the Convolutional Block Attention Module (CBAM) and the Global Context (GC) block. We optimized the model through hyperparameter tuning, implementing data cleaning, augmentation, and normalization techniques using the GRAZPEDWRI-DX dataset. This process addressed class imbalances and significantly improved model performance, with mean Average Precision (mAP) increasing from 63.6 to 66.32%.
The iYOLOv8 models demonstrated substantial improvements in performance metrics. The iYOLOv8 + GC model achieved the highest precision at 97.2%, with an F1-score of 67% and an mAP50 of 69.5%, requiring only 3.62 h of training time. In comparison, the iYOLOv8 + ECA model reached 96.7% precision, significantly reducing training time from 8.54 to 2.16 h. The various iYOLOv8-AM models achieved an average accuracy of 96.42% in fracture detection, although performance for detecting bone anomalies and soft tissues was lower due to dataset constraints. The improvements highlight the model's effectiveness in pathological detection of the pediatric distal radius, suggesting that integrating these AI models into clinical practice could significantly enhance diagnostic efficiency.
Our improved YOLOv8-AM model, incorporating the GC attention mechanism, demonstrated superior speed and accuracy in pediatric distal radius fracture detection while reducing training time. Future research should explore additional features to further enhance detection capabilities in other musculoskeletal areas, as this model has the potential to adapt to various fracture types with appropriate training.
Not applicable.
在急诊科，住院医师和医生通过解读X线片来识别骨折，其中桡骨远端骨折是儿童中最常见的骨折类型。在资源充足的医院，技术娴熟的放射科医生通常能确保准确的读片结果，但农村地区往往缺乏这种专业知识，导致诊断准确性降低，治疗可能会延迟。机器学习系统通过检测非专家可能遗漏的细微特征提供了有前景的解决方案。包括YOLOv8及其注意力机制模型YOLOv8-AM在内的最新进展已在自动骨折检测中显示出潜力。本研究旨在通过整合有针对性的改进和新的注意力机制来优化YOLOv8-AM模型，以提高儿科患者桡骨远端骨折的检测能力。
我们增强了YOLOv8-AM模型以改善儿科腕部骨折检测，保留YOLOv8骨干架构，同时整合诸如卷积块注意力模块（CBAM）和全局上下文（GC）块等注意力机制。我们通过超参数调整来优化模型，使用GRAZPEDWRI-DX数据集实施数据清理、增强和归一化技术。这一过程解决了类别不平衡问题，并显著提高了模型性能，平均精度均值（mAP）从63.6%提高到了66.32%。
iYOLOv8模型在性能指标上有显著提升。iYOLOv8 + GC模型达到了最高精度97.2%，F1分数为67%，mAP50为69.5%，训练时间仅需3.62小时。相比之下，iYOLOv`8 + ECA模型精度达到96.7%，训练时间从8.54小时大幅减少至2.16小时。各种iYOLOv8-AM模型在骨折检测中的平均准确率为96.42%，不过由于数据集的限制，检测骨骼异常和软组织的性能较低。这些改进凸显了该模型在儿科桡骨远端病理检测中的有效性，表明将这些人工智能模型整合到临床实践中可显著提高诊断效率。
我们改进的纳入GC注意力机制的YOLOv8-AM模型在儿科桡骨远端骨折检测中展现出卓越的速度和准确性，同时减少了训练时间。未来的研究应探索更多特征以进一步增强在其他肌肉骨骼区域的检测能力，因为该模型有潜力通过适当训练适应各种骨折类型。
不适用。
