METHODS: 1870 AIS patients with remaining growth potential were identified. Curve progression was defined by a Cobb angle increase in the major curve of ≥6° between first visit and skeletal maturity in curves that exceeded 25°. Separate prediction modules were developed for i) clinical data, ii) global/regional spine X-rays, and iii) hand X-rays. The hand X-ray module performed automated image classification and segmentation tasks towards estimation of skeletal maturity and bone mineral density. A late fusion strategy integrated these domains towards the prediction of progressive curves at first clinic visit.
RESULTS: Composite model performance was assessed on a validation cohort and achieved an accuracy of 83.2% (79.3-83.6%, 95% confidence interval), sensitivity of 80.9% (78.2-81.9%), specificity of 83.6% (78.8-84.1%) and an AUC of 0.84 (0.81-0.85), outperforming single modality prediction models (AUC 0.65-0.78).
CONCLUSIONS: The composite prediction model achieved a high degree of accuracy. Upon incorporation into school-aged screening programs, patients at-risk of progression may be prioritized to receive urgent specialist attention, more frequent follow-up, and pre-emptive treatment.
BACKGROUND: Funding from The Society for the Relief of Disabled Children was awarded to GKHS.
方法:确定了1870例具有剩余生长潜能的AIS患者。曲线进展定义为首次就诊和超过25°的骨骼成熟度之间的主曲线中≥6°的Cobb角增加。为i)临床数据开发了单独的预测模块,ii)全球/区域脊柱X射线,和iii)手部X射线。手动X射线模块执行自动图像分类和分割任务,以估计骨骼成熟度和骨矿物质密度。晚期融合策略将这些领域整合到首次临床就诊时的渐进曲线预测中。
结果:在验证队列中评估了复合模型的性能,并达到了83.2%的准确性(79.3-83.6%,95%置信区间),灵敏度为80.9%(78.2-81.9%),特异性为83.6%(78.8-84.1%),AUC为0.84(0.81-0.85),优于单模态预测模型(AUC0.65-0.78)。
结论:复合预测模型实现了高度的准确性。在纳入学龄儿童筛查计划后,有进展风险的患者可优先接受紧急专科护理,更频繁的随访,先发制人的治疗。
背景:来自残疾儿童救济协会的资金被授予GKHS。