Artificial intelligence analysis of the relationship between subcutaneous fat volume in CT images and cervical kyphosis after laminoplasty

Abstract

This retrospective observational study aimed to explore the associations of AI-assisted CT-quantified C4/C5 skeletal muscle and adipose tissue indices with postoperative cervical kyphosis and long-term functional outcomes in patients undergoing laminoplasty. Postoperative cervical kyphosis is a prevalent complication of laminoplasty with incompletely elucidated pathogenesis, and the role of neck muscle and adipose tissue in this complication lacks validation via standardized 3D quantification. We enrolled 114 patients with cervical spondylosis who underwent laminoplasty at Wuhan Union Hospital between 2018 and 2022, excluding those with severe comorbidities. Preoperative CT scans (obtained within 3 months before surgery) were processed using a ResU-Net model to quantify C4/C5 tissue indices. Statistical analyses (SPSS 27.0) included multivariate logistic regression, and receiver operating characteristic (ROC) curves with the Youden index were used to determine predictive thresholds. Postoperative kyphosis was diagnosed based on routine follow-up cervical X-rays. Multivariate logistic regression revealed that C4 and C5 subcutaneous fat volume (SFV) were independently associated with postoperative kyphosis (p<0.05). The combined model integrating tissue indices and clinical variables achieved area under the curve (AUC) values of 0.706 (C4 SFV) and 0.717 (C5 SFV) (p<0.05). AI-assisted CT-quantified C4/C5 SFV is correlated with postoperative cervical kyphosis. Integration of CT-derived tissue metrics and clinical indicators improves the prediction of laminoplasty outcomes, providing a data-driven foundation for optimizing surgical planning and postoperative rehabilitation in cervical spondylosis patients.