Accurate automatic measurement of spinopelvic parameters with a one-stage deep learning technique

Abstract

Background: The current method of measuring parameters in spinal imaging manually is time-consuming and prone to inconsistencies. This study proposed and validated a novel method to automate the measurement of pelvic parameters using a one-stage deep learning (DL) model. Methods: Spinopelvic parameters, including pelvic incidence (PI), sacral slope (SS), and pelvic tilt (PT), were measured from full body radiographs of patients by three evaluators and by using our proposed method. Our proposed one-stage DL model was based on keypoint localisation. Landmark localisation error was used to evaluate the performance of landmark localisation. To evaluate the agreement between our method and the human evaluators, the analysis of average error, standard deviation, and intra- and inter-evaluator reliability was conducted using the intraclass correlation coefficient (ICC) and Pearson's correlation coefficient (R). Results:The method achieved excellent measurement performance for spinopelvic parameters. The distribution of the landmark localisation errors was within a reasonable range (median error, 2.28–4.01 mm). ICC values for the assessment of the intra- (range: 0.941–0.996) and inter-evaluator (0.994–0.998) reliability of human evaluators were excellent. The method was able to determine spinopelvic parameters with excellent ICC values (0.919-0.997) and R value (R >0.899, p<0.001, all). Meanwhile, the detection speed of the algorithm was approximately 30 times faster than that of manual measurements of spinopelvic parameters. Conclusions:This one-step automated measurement method is less time-consuming and has excellent reliability and agreement with human evaluators.