Prediction of Accuracy and Screw Size by Pedicle Anatomic Parameters and Screws in Idiopathic Scoliosis With Freehand Screw Placement Based on Machine Learning

Abstract

\n Study Design: A retrospective study.Objective: To investigate a machine learning algorithm to explore the influence of pedicle morphological parameters and pedicle screw size on safe screw placement in the treatment of idiopathic scoliosis with freehand. And a model was built to guide the selection of screwMethods: We analyzed 52 patients with idiopathic scoliosis who underwent correction surgery in our hospital from June 2012 to December 2019, including 17 males and 35 females aged 10-20 years. All pedicle screws were placed by freehand. Preoperative and postoperative X-ray and CT scans of whole spine were performed to measure Cobb Angle and pedicle morphological parameters, including transverse diameter of the pedicle, sagittal diameter of the pedicle, length of the pedicle channel, rotation angle of vertebrae, angle of the sagittal plane of pedicle and angle of the horizontal plane of pedicle. Screw penetration grading was also evaluated. Random forest were used to build a machine learning model to help the decision making of choosing an appropriate screw based on pedicle parameters and screw size.Results: A total of 888 screws and pedicles were included. The satisfactory rate of screw placement was 88.5%. The pedicle screw size was analyzed and predicted based on screw penetration and pedicle morphological parameters. The AUROC of random forest classification model achieved 0.712. The goodness of fit(R2) was 0.546.Conclusion: Our model could provide guidance for the doctor to choose the length of the screw before surgery, and the classification model could also give a preliminary prediction of whether there would be anterior screw penetration based on the pedicle parameters.