Pedicle screw is currently the most widely used internal fixation technique, which can provide good fusion and multi-plane correction strength within a limited distance22。Luo5 et al. used Meta-analysis to study the application of pedicle screw system for correction surgery, the total complication rate was 6.46%, and the reoperation rate was 1.97%. The correction was good on coronal plane, and the improvement of Cobb angle was also significantly better than that of mixed internal fixed technique. Pedicle screw misplacement is one of the most common complications in correction surgery, and might cause vascular or neurological injure, pedicle fracture, internal fixation failure and postoperative pain and so on3,21. Freehand is a reliable technique with a low misplacement rate from 0.4–27%3,6,7,23, and a low screw-related complication rate(0-1.7%)24. Freehand has advantages of shorter operation time, less blood loss, lower radiation dose and lower economic burden22,25−27, and also has disadvantages such as long learning curve and higher requirements for experience. In this study, 52 patients were treated with freehand, and a total of 888 screws were placed. The average preoperative Cobb angle of the patients was 52.0°±15.7° (21°-103°), and the screw placement satisfaction rate reached 88.5%. No screw-related complications occurred. The overall screw placement in this trial was satisfied.
For patients with idiopathic scoliosis, pedicle dysplasia is the main factor that increases the difficulty of screw placement. Akazawa28 et al. reported that when the pedicle cortex channel was smaller than 1mm, the misplacement rate is high as 31.5%. As for abnormal pedicle, the probability of misplacement of screws with bare hands is 3 times that of normal pedicles.9。Huang2 et al. analyzed 87 patients with idiopathic scoliosis, rate of abnormal pedicles was as high as 65% among 2958 screws. Most of severely abnormal pedicles were located from T2-T10. The independent risk factors for abnormal pedicles included female patients, upper thoracic pedicles, Cobb angle of main curve greater than 70° and concave side pedicles, etc. Lee29et al. studied 182 patients and got a conclusion that the diameter of pedicle gradually decreased from T1 to T4. Gao30et al. studied 60 females(2718 screws) with Lenke type 1 idiopathic scoliosis, and found that TD of concave pedicle was smaller than that of the convex side and the rate of deformity was higher. TD near apical area had the largest variation. Davis1et al found that diameter and height of pedicles near apical area and on concave side were smaller than that of convex side, which causes low accuracy of screw placement, after studied 22 patients. Liljenqvist31 et al. also got an similar conclusion and found that the LC of T5 is the smallest.
At present, the morphological study of pedicle is mostly based on Sarwahi’s standard9: Type A, cancellous bone channel diameter > 4mm; Type B, cancellous bone channel diameter is 2-4mm; Type C, cancellous bone channel diameter < 2mm but cortical channel diameter > 2mm; Type D, cortical channel diameter < 2mm. Type B, C, and D pedicles are abnormal. Although this standard can evaluate the degree of pedicle deformity, there is no proof for its guiding role in clinical practice. In our study, we brought parameters as RA, HA, SA and LC except TD, SD in order to describe the spatial attributes of pedicles more specifically. The parameters were measured via CT, because the actual pedicle parameters has a good correlation with parameters measured on CT31,32。Sarwahi9et al. distinguished cortical channel and cancellous channel. However, we determined the midpoint of the cortical bone to measure TD and SD, which simplified the measurement steps and reduce error from multiple measurements and the limitation of pixels.
In this study, we used machine learning to build a model to explore the relationship between pedicle parameters and screw size and the satisfaction of screw placement and tried to make a prediction of best selection through pedicle parameters. A regression model based on random forest was proposed. We trained the model with all the data set. And we found that this model can only predict if there would be an anterior penetration and the screw length. The AUROC achieved 0.712. The R2 was 0.546. TD and screw width which was concerned by surgeons in previous literatures didn’t show a significant correlation to the accuracy. This might because the interior and lateral breach is more likely to lead to vascular and neurological complications, when selecting screws, safer and more conservative selection might be made.
This is an origin research that combine machine learning and spine surgery, we hope that this can bring new methods into clinical practice. There are still some limitations of this study. First, the sample size was limited, more data should be included to perfect the model with deep learning. Second, although the measurement was performed by several researchers, it still might be subjective error. In the future, we are looking forward to include more center and patients and combine image recognition technology and deep learning together to provide strong evidence for clinical practice.