Abstract
Objectives: To quantify the magnitude of spinal deformity in adolescent idiopathic scoliosis (AIS), the Cobb angle is measured on X-ray images of the spine. Continuous exposure to X-ray radiation to follow-up the progression of scoliosis may lead to negative side effects on patients. Furthermore, manual measurement of the Cobb angle could lead to up to 10° or more of a difference due to intra/inter observer variation. Therefore, the objective of this study is to identify the Cobb angle by developing an automated radiation-free model, using Machine learning algorithms. Methods: Thirty participants with lumbar/thoracolumbar AIS (15° < Cobb angle < 66°) performed gait cycles. The lumbosacral (L5-S1) joint efforts during six gait cycles of participants were used as features to feed training algorithms. Various regression algorithms were implemented and run. Results: The decision tree regression algorithm achieved the best result with the mean absolute error equal to 4.6° of averaged 10-fold cross-validation. Conclusions: This study shows that the lumbosacral joint efforts during gait as radiation-free data are capable to identify the Cobb angle by using Machine learning algorithms. The proposed model can be considered as an alternative, radiation-free method to X-ray radiography to assist clinicians in following-up the progression of AIS.
Abstract (translated)
目标:量化青少年奇偶性脊柱侧弯(AIS)的脊柱畸形程度,通过测量脊柱X光图像的曲度角度来实现。持续接受X射线照射来跟进AIS的进展可能对患者的健康产生负面影响。此外,手动测量曲度角度可能会导致由于内省/观察者差异造成的误差高达10°或更多。因此,本研究的目标是通过开发一种自动化的无辐射模型,使用机器学习算法来识别曲度角度。方法:30名 Lumbar/thoracolumbar AIS患者进行了步态试验。在参与者的6个步态试验中, Lumbar/S1 联合努力被用作特征,用于喂入训练算法。各种回归算法被实现并运行。结果:决策树回归算法取得了最好的结果,平均绝对误差为4.6°,与10fold cross-validation的平均误差相同。结论:本研究表明,在步态中的 Lumbar/S1 联合努力作为无辐射数据能够使用机器学习算法识别曲度角度。该提议模型可以被视为X射线 radiography的替代方法,以协助临床医生跟进AIS的进展。
URL
https://arxiv.org/abs/2301.12588
