Development and validation of an artificial intelligence model to
  accurately predict spinopelvic parameters
Edward S. Harake Joseph R. Linzey Cheng Jiang Rushikesh S. Joshi Mark M. Zaki Jaes C. Jones Siri S. Khalsa John H. Lee Zachary Wilseck Jacob R. Joseph Todd C. Hollon Paul Park
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.