Objective. Achieving appropriate spinopelvic alignment has been shown to be associated with improved clinical symptoms. However, measurement of spinopelvic radiographic parameters is time-intensive and interobserver reliability is a concern. Automated measurement tools have the promise of rapid and consistent measurements, but existing tools are still limited by some degree of manual user-entry requirements. This study presents a novel artificial intelligence (AI) tool called SpinePose that automatically predicts spinopelvic parameters with high accuracy without the need for manual entry. Methods. SpinePose was trained and validated on 761 sagittal whole-spine X-rays to predict sagittal vertical axis (SVA), pelvic tilt (PT), pelvic incidence (PI), sacral slope (SS), lumbar lordosis (LL), T1-pelvic angle (T1PA), and L1-pelvic angle (L1PA). A separate test set of 40 X-rays was labeled by 4 reviewers, including fellowship-trained spine surgeons and a fellowship-trained radiologist with neuroradiology subspecialty certification. Median errors relative to the most senior reviewer were calculated to determine model accuracy on test images. Intraclass correlation coefficients (ICC) were used to assess inter-rater reliability. Results. SpinePose exhibited the following median (interquartile range) parameter errors: SVA: 2.2(2.3)mm, p=0.93; PT: 1.3(1.2){\deg}, p=0.48; SS: 1.7(2.2){\deg}, p=0.64; PI: 2.2(2.1){\deg}, p=0.24; LL: 2.6(4.0){\deg}, p=0.89; T1PA: 1.1(0.9){\deg}, p=0.42; and L1PA: 1.4(1.6){\deg}, p=0.49. Model predictions also exhibited excellent reliability at all parameters (ICC: 0.91-1.0). Conclusions. SpinePose accurately predicted spinopelvic parameters with excellent reliability comparable to fellowship-trained spine surgeons and neuroradiologists. Utilization of predictive AI tools in spinal imaging can substantially aid in patient selection and surgical planning.

翻译：目的：研究表明，获得适当的脊柱-骨盆对齐与改善临床症状相关。然而，脊柱-骨盆影像学参数的测量耗时且观察者间可靠性存疑。自动化测量工具具有实现快速、一致测量的潜力，但现有工具仍受限于一定程度的手动输入需求。本研究提出一种名为SpinePose的新型人工智能工具，可在无需手动输入的情况下自动高精度预测脊柱-骨盆参数。方法：SpinePose基于761张矢状位全脊柱X线片进行训练与验证，用于预测矢状面垂直轴、骨盆倾斜角、骨盆入射角、骶骨倾斜角、腰椎前凸角、T1-骨盆角及L1-骨盆角。另设40张X线片作为独立测试集，由4名评审员（包括接受专科培训的脊柱外科医生及持有神经放射学亚专科认证的放射科医生）进行标注。以相对于资深评审员的中位误差评估模型在测试图像上的准确性，采用组内相关系数评估评审者间信度。结果：SpinePose各参数中位误差（四分位距）为：SVA: 2.2(2.3)mm, p=0.93；PT: 1.3(1.2)°, p=0.48；SS: 1.7(2.2)°, p=0.64；PI: 2.2(2.1)°, p=0.24；LL: 2.6(4.0)°, p=0.89；T1PA: 1.1(0.9)°, p=0.42；L1PA: 1.4(1.6)°, p=0.49。模型预测在所有参数上均表现出卓越可靠性（ICC: 0.91-1.0）。结论：SpinePose能准确预测脊柱-骨盆参数，其可靠性与接受专科培训的脊柱外科医生及神经放射科医生相当。在脊柱影像中应用预测性AI工具可显著辅助患者筛选与手术规划。