Introduction: Preoperative and postoperative sagittal plane assessment is crucial in both spinal deformity and degenerative pathologies. Sagittal malalignment is a well-established cause of poor patient-reported outcomes. The currently available spine measurement software programs require users to identify several landmarks prior to calculating parameters, making them time-consuming and more reliant upon user experience. There is a growing need for an automated analysis tool that measures pelvic parameters with speed, precision, and reproducibility without relying on user-identified landmarks. A new AI algorithm has been developed to measure important radiographic parameters independently.
Aims/Objectives: Aim 1.) To evaluate and demonstrate that an algorithm based on artificial intelligence (AI) can independently determine spinopelvic parameters.
Hypothesis: The novel, fully automatic method will have a high agreement with human measurements for lumbar lordosis (LL), pelvic incidence (PI), pelvic tilt (PT), and sacral slope (SS).
Methods: From a total of 200 lateral lumbar radiographs (preoperative and postoperative images from 100 patients undergoing fusion) five independent observers (4 spinal surgeons, 1 senior researcher) digitally measured LL, PI, PT, and SS. Their parameters were compared with AI algorithm-generated parameters. Mean error (95% confidence interval, standard deviation) and inter-rater reliability were assessed using two-way mixed, single-measure intraclass correlation (ICC). ICC values larger than 0.75 were considered excellent (Ciccetti, Psychol. Assess. 1994).
Results: The novel algorithm’s spinopelvic parameter ICC values were excellent in 98% of preoperative and in 95% of postoperative radiographs (PreOp range: 0.85–0.92, PostOp range: 0.81–0.87). Exemplarily, mean errors are smallest for the PI (PreOp: -0.5° (95%-CI: -1.5°–0.6°; Fig. 1); PostOp: 0.0° (-1.1°–1.2°)) and largest for LL (1.3° (0.3°–2.4°); 3.8° (2.5°–5.0°)).
Conclusion: Novel AI algorithm automated spinopelvic parameter measurements from spine radiographs have a high degree of accuracy comparable to digital measurements by experts. This algorithm can improve physician workflow efficiency and reduce inter-rater and intra-rater measurement errors.
Colin Haines, MD – Virginia Spine Institute
Lindsay Orosz, MS, PA-C – The National Spine Health Foundation
Alexandra E. Thomson, MD, MPH – Virginia Spine Institute
Thomas C. Schuler, MD – Virginia Spine Institute
Christopher R. Good, MD – Virginia Spine Institute
Priyanka Grover, MS – RAYLYTIC GmbH
Marcel Dreischarf, PhD – RAYLYTIC GmbH
Rita Roy, MD – The National Spine Health Foundation
Ehsan Jazini, MD – Virginia Spine Institute
Reviewed by: Dr. Colin Haines, MD, FACS.