用AI分析普通膝关节正位片，自动判断骨力线异常，提升置换手术预测精度
The Role of Radiographic Knee Alignment in Total Knee Replacement Outcomes and Opportunities for Artificial Intelligence-Driven Assessment
- 基于标准膝关节正位片开发AI算法，自动评估骨力线
- 可识别股骨/胫骨畸形，辅助术前规划和假体选择
- 适合骨科医生、临床研究者关注术前评估效率提升
膝骨关节炎（OA）是全球最常见的致残性疾病之一。全膝关节置换术（TKR）是终末期膝关节炎的主要治疗手段，但约10%患者术后不满意。患者报告结局量表（PROMs）常用于评估手术效果。若能提前预测不良预后，将有助于优化患者筛选与管理策略。放射学膝关节力线是预测TKR结局和长期关节健康的關鍵生物标志物。股骨或胫骨畸形会直接影响手术方案、假体选择及术后恢复。传统力线测量依赖人工判读，耗时且需长腿侧位片，而临床常用标准前后位（AP）膝关节片。在标准膝关节片上实现自动化力线评估，有望显著提升膝关节炎诊疗流程的效率。
原文摘要 · Abstract (English)
Knee osteoarthritis (OA) is one of the most widespread and burdensome health problems [1-4]. Total knee replacement (TKR) may be offered as treatment for end-stage knee OA. Nevertheless, TKR is an invasive procedure involving prosthesis implantation at the knee joint, and around 10% of patients are dissatisfied following TKR [5,6]. Dissatisfaction is often assessed through patient-reported outcome measures (PROMs) [7], which are usually completed by patients and assessed by health professionals to evaluate the condition of TKR patients. In clinical practice, predicting poor TKR outcomes in advance could help optimise patient selection and improve management strategies. Radiographic knee alignment is an important biomarker for predicting TKR outcomes and long-term joint health. Abnormalities such as femoral or tibial deformities can directly influence surgical planning, implant selection, and postoperative recovery [8,9]. Traditional alignment measurement is manual, time-consuming, and requires long-leg radiographs, which are not always undertaken in clinical practice. Instead, standard anteroposterior (AP) knee radiographs are often the main imaging modality. Automated methods for alignment assessment in standard knee radiographs are potentially clinically valuable for improving efficiency in the knee OA treatment pathway.
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