Lam Van, Parida Abhijeet, Dance Sarah, Tabaie Sean, Cleary Kevin, Anwar Syed Muhammad
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA.
Nationwide Children's Hospital, Columbus, OH, USA.
Int J Comput Assist Radiol Surg. 2025 Jul 24. doi: 10.1007/s11548-025-03485-z.
Forearm fractures constitute a significant proportion of emergency department presentations in pediatric population. The treatment goal is to restore length and alignment between the distal and proximal bone fragments. While immobilization through splinting or casting is enough for non-displaced and minimally displaced fractures. However, moderately or severely displaced fractures often require reduction for realignment. However, appropriate treatment in current practices has challenges due to the lack of resources required for specialized pediatric care leading to delayed and unnecessary transfers between medical centers, which potentially create treatment complications and burdens. The purpose of this study is to build a machine learning model for analyzing forearm fractures to assist clinical centers that lack surgical expertise in pediatric orthopedics.
X-ray scans from 1250 children were curated, preprocessed, and manually annotated at our clinical center. Several machine learning models were fine-tuned using a pretraining strategy leveraging self-supervised learning model with vision transformer backbone. We further employed strategies to identify the most important region related to fractures within the forearm X-ray. The model performance was evaluated with and without region of interest (ROI) detection to find an optimal model for forearm fracture analyses.
Our proposed strategy leverages self-supervised pretraining (without labels) followed by supervised fine-tuning (with labels). The fine-tuned model using regions cropped with ROI identification resulted in the highest classification performance with a true-positive rate (TPR) of 0.79, true-negative rate (TNR) of 0.74, AUROC of 0.81, and AUPR of 0.86 when evaluated on the testing data.
The results showed the feasibility of using machine learning models in predicting the appropriate treatment for forearm fractures in pediatric cases. With further improvement, the algorithm could potentially be used as a tool to assist non-specialized orthopedic providers in diagnosing and providing treatment.
前臂骨折在儿科急诊就诊病例中占相当大的比例。治疗目标是恢复远端和近端骨碎片之间的长度和对线。对于无移位和轻度移位骨折，通过夹板或石膏固定就足够了。然而，中度或重度移位骨折通常需要复位以重新对线。然而，当前实践中的适当治疗存在挑战，因为缺乏专业儿科护理所需的资源，导致在医疗中心之间出现延迟和不必要的转诊，这可能会产生治疗并发症和负担。本研究的目的是建立一个用于分析前臂骨折的机器学习模型，以协助缺乏小儿骨科手术专业知识的临床中心。
在我们的临床中心收集、预处理并人工标注了1250名儿童的X线扫描图像。使用利用带有视觉Transformer主干的自监督学习模型的预训练策略对几个机器学习模型进行了微调。我们进一步采用策略来识别前臂X线片中与骨折相关的最重要区域。通过有无感兴趣区域（ROI）检测来评估模型性能，以找到用于前臂骨折分析的最佳模型。
我们提出的策略利用无监督预训练（无标签），然后进行有监督微调（有标签）。在测试数据上进行评估时，使用通过ROI识别裁剪的区域进行微调的模型产生了最高的分类性能，真阳性率（TPR）为0.79，真阴性率（TNR）为0.74，曲线下面积（AUROC）为0.81，精确率-召回率曲线下面积（AUPR）为0.86。
结果表明，使用机器学习模型预测小儿前臂骨折的适当治疗方法是可行的。随着进一步改进，该算法有可能用作辅助非专业骨科医生进行诊断和治疗的工具。
