Moustafa Ibrahim M, Khowailed Iman Akef, Zadeh Shima A Mohammad, Ozsahin Dilber Uzun, Mustapha Mubarak Taiwo, Oakley Paul A, Harrison Deed E
Department of Physiotherapy, College of Health Sciences, University of Sharjah, 27272, Sharjah, United Arab Emirates.
Neuromusculoskeletal Rehabilitation Research Group, RIMHS-Research Institute of Medical and Health Sciences, University of Sharjah, 27272, Sharjah, United Arab Emirates.
Sci Rep. 2025 Jul 2;15(1):22804. doi: 10.1038/s41598-025-05390-6.
This study leveraged machine learning (ML) models to explore the relationship between three-dimensional (3D) spinal alignment parameters and clinical outcomes in patients suffering from fibromyalgia syndrome (FMS). A cohort of 303 FMS patients, diagnosed according to the 2016 American College of Rheumatology criteria, underwent comprehensive assessments of sagittal imbalance, coronal imbalance, vertebral rotation, pelvic obliquity, pelvic torsion, and pelvic rotation using a validated 3D imaging system. Clinical outcomes, included the fibromyalgia impact questionnaire (FIQ), pain catastrophizing scale (PCS), Pittsburgh sleep quality index (PSQI), and algometric pain scores. Five ML models were employed: Fast Kolmogorov-Arnold Networks with Bee Colony Optimization (FastKAN-BCO), FastKAN with LBFGS, Multilayer Perceptron with LBFGS (MLP-LBFGS), Multilayer Perceptron with ADAM (MLP-ADAM), and linear regression. Among the models tested, FastKAN-BCO demonstrated the highest R-squared value (0.95) for algometric pain, while the MLP-LBFGS model achieved superior performance for PCS (R = 0.94), FIQ (R = 0.88), and PSQI (R = 0.97) predictions. Sagittal imbalance and pelvic obliquity were identified as key predictors of symptom severity. Stratification revealed that individuals with more pronounced pelvic asymmetry and vertebral rotation exceeding 10° experienced increased symptom intensity. The contribution of vertebral rotation was nonlinear, indicating a threshold-dependent impact. This study illustrates the potential of ML techniques to uncover complex associations between 3D spinal alignment and FMS outcomes, offering a foundation for personalized diagnostic and therapeutic approaches. The results emphasize the critical role of postural dysfunction in FMS and highlight the potential of advanced ML models.
本研究利用机器学习（ML）模型，探讨纤维肌痛综合征（FMS）患者的三维（3D）脊柱排列参数与临床结局之间的关系。根据2016年美国风湿病学会标准诊断的303例FMS患者队列，使用经过验证的3D成像系统，对矢状面失衡、冠状面失衡、椎体旋转、骨盆倾斜、骨盆扭转和骨盆旋转进行了全面评估。临床结局包括纤维肌痛影响问卷（FIQ）、疼痛灾难化量表（PCS）、匹兹堡睡眠质量指数（PSQI）和压痛计疼痛评分。采用了五个ML模型：带蜂群优化的快速柯尔莫哥洛夫 - 阿诺德网络（FastKAN - BCO）、带LBFGS的FastKAN、带LBFGS的多层感知器（MLP - LBFGS）、带ADAM的多层感知器（MLP - ADAM）和线性回归。在测试的模型中，FastKAN - BCO在压痛计疼痛方面表现出最高的决定系数（R²）值（0.95），而MLP - LBFGS模型在PCS（R = 0.94）、FIQ（R = 0.88）和PSQI（R = 0.97）预测方面表现更优。矢状面失衡和骨盆倾斜被确定为症状严重程度的关键预测因素。分层分析显示，骨盆不对称更明显且椎体旋转超过10°的个体症状强度增加。椎体旋转的影响是非线性的，表明存在阈值依赖性影响。本研究说明了ML技术在揭示3D脊柱排列与FMS结局之间复杂关联方面的潜力，为个性化诊断和治疗方法提供了基础。结果强调了姿势功能障碍在FMS中的关键作用，并突出了先进ML模型的潜力。
