Brink Farah W, Adler Brent, Bambach Sven, Lo Charmaine B, Rust Steven, Bartlett Christopher W, Bradshaw Logan, Henry M Katherine, Messer Diana
Nationwide Children's Hospital, 700 Children's Drive, Columbus, OH, 43205, USA.
The Ohio State University Wexner Medical Center, Columbus, USA.
Pediatr Radiol. 2025 May;55(6):1257-1269. doi: 10.1007/s00247-025-06223-4. Epub 2025 Apr 22.
Estimating time-since-injury of healing fractures is imprecise, encompassing excessively wide timeframes. Most injured children are evaluated at non-children's hospitals, yet pediatric radiologists can disagree with up to one in six skeletal imaging interpretations from referring community hospitals. There is a need to improve image interpretation by considering additional methods for fracture dating.
To train and validate deep learning models to correctly estimate the age of pediatric accidental long bone fractures.
This secondary data analysis used radiographic images of accidental long bone fractures in children <6 years at the time of injury seen at a large Midwestern children's hospital between 2000-2016. We built deep learning models both to classify fracture images into different age groups and to directly estimate fracture age (time-since-injury). We used cross-validation to evaluate model performance across various metrics, including confusion matrices, sensitivity/specificity, and activation maps for age classification, and mean absolute error (MAE) and root mean squared error (RMSE) for age estimation.
Our study cohort contained 2,328 radiographs from 399 patients. Overall, our models performed above baselines for fracture age classification and estimation, both when trained/validated across all bones and on specific bone types. The best model was able to estimate fracture age for any long bone with a MAE of 6.2 days and with 68% of estimates falling within 7 days of the correct fracture age.
Our study successfully demonstrated that, for radiographic dating of accidental fractures of long bones, deep learning models can estimate time-since-injury with above-baseline accuracy.
估算愈合骨折的受伤时间并不精确，时间范围过于宽泛。大多数受伤儿童是在非儿童医院接受评估的，然而儿科放射科医生可能会对来自转诊社区医院的骨骼影像解读中高达六分之一的结果存在分歧。需要通过考虑骨折日期测定的其他方法来改进影像解读。
训练并验证深度学习模型，以正确估算小儿意外长骨骨折的年龄。
这项二次数据分析使用了2000年至2016年间在一家大型中西部儿童医院就诊时受伤的6岁以下儿童意外长骨骨折的X光影像。我们构建了深度学习模型，既用于将骨折影像分类到不同年龄组，也用于直接估算骨折年龄（受伤时间）。我们使用交叉验证来评估模型在各种指标上的性能，包括混淆矩阵、敏感度/特异度以及年龄分类的激活图，以及年龄估算的平均绝对误差（MAE）和均方根误差（RMSE）。
我们的研究队列包含来自399名患者的2328张X光片。总体而言，无论是在对所有骨骼进行训练/验证时，还是在特定骨类型上，我们的模型在骨折年龄分类和估算方面的表现均高于基线水平。最佳模型能够以6.2天的平均绝对误差估算任何长骨的骨折年龄，且68%的估算结果落在正确骨折年龄的7天范围内。
我们的研究成功表明，对于长骨意外骨折的X光片日期测定，深度学习模型能够以高于基线的准确率估算受伤时间。
