PDF(Pubmed)
Abstract：
BACKGROUND: Adolescent idiopathic scoliosis (AIS) affects up to 5% of the population. The efficacy of school-aged screening remains controversial since it is uncertain which curvatures will progress following diagnosis and require treatment. Patient demographics, vertebral morphology, skeletal maturity, and bone quality represent individual risk factors for progression but have yet to be integrated towards accurate prognostication. The objective of this work was to develop composite machine learning-based prediction model to accurately predict AIS curves at-risk of progression.
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.
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.
摘要：
背景：青少年特发性脊柱侧凸（AIS）影响多达5％的人口。学龄儿童筛查的有效性仍然存在争议，因为不确定哪些曲率会在诊断后发展并需要治疗。患者人口统计学，椎体形态学,骨骼成熟度,和骨质量代表了进展的个体风险因素，但尚未被整合到准确的预后中。这项工作的目的是开发基于机器学习的复合预测模型，以准确预测处于进展风险的AIS曲线。
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。
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。
