ObjectiveTo summarize the research progress on spontaneous facet fusion (SFF) after lumbar spine surgery, and provide reference for further research on SFF. Methods The definition, development, clinical significance, and related influence factors of SFF were throughout reviewed by referring to relevant domestic and foreign literature in recent years. Results SFF is a phenomenon of joint space disappearance and fusion of upper and lower articular processes, which starts in a ring shape from the outermost edges to the central regions. Currently reported SFF occurred after posterior lumbar pedicle screw fixation. SFF may increase the stability of surgical segments and relieve clinical symptoms of patients. SFF is closely related to the method of lumbar internal fixation, facet osteoarthritis, interbody fusion, age, body mass index, type B fracture (according to AO classification), and the operative segment. Conclusion Most reported SFF occur after posterior lumbar pedicle screw fixation, which can increase lumbar stability, but the mechanism and influencing factors remain to be further clarified.
Objective To summarize the effect of cage height on outcomes of lumbar interbody fusion surgery and the importance of the cage height selection. Methods The related literature was widely reviewed to summarize the research progress on the complications caused by inappropriate height of the cage and the methods of selecting cage height. Results Inappropriate height of the cage can lead to endplate injury, cage subsidence, internal fixation failure, adjacent segmental degeneration, over-distraction related pain, insufficient indirect decompression, instability of operation segment, poor interbody fusion, poor sequence of spine, and cage displacement. At present, the selection of the cage height is based on the results of the intraoperative model test, which is reliable but high requirements for surgical experience and hard to standardize. ConclusionThe inappropriate height of the cage may have an adverse impact on the postoperative outcome of patients. It is important to develop a selection standard of the cage height by screening the related influential factors.
Objective To develop an automatic diagnostic tool based on deep learning for lumbar spine stability and validate diagnostic accuracy. Methods Preoperative lumbar hyper-flexion and hyper-extension X-ray films were collected from 153 patients with lumbar disease. The following 5 key points were marked by 3 orthopedic surgeons: L4 posteroinferior, anterior inferior angles as well as L5 posterosuperior, anterior superior, and posterior inferior angles. The labeling results of each surgeon were preserved independently, and a total of three sets of labeling results were obtained. A total of 306 lumbar X-ray films were randomly divided into training (n=156), validation (n=50), and test (n=100) sets in a ratio of 3∶1∶2. A new neural network architecture, Swin-PGNet was proposed, which was trained using annotated radiograph images to automatically locate the lumbar vertebral key points and calculate L4, 5 intervertebral Cobb angle and L4 lumbar sliding distance through the predicted key points. The mean error and intra-class correlation coefficient (ICC) were used as an evaluation index, to compare the differences between surgeons’ annotations and Swin-PGNet on the three tasks (key point positioning, Cobb angle measurement, and lumbar sliding distance measurement). Meanwhile, the change of Cobb angle more than 11° was taken as the criterion of lumbar instability, and the lumbar sliding distance more than 3 mm was taken as the criterion of lumbar spondylolisthesis. The accuracy of surgeon annotation and Swin-PGNet in judging lumbar instability was compared. Results ① Key point: The mean error of key point location by Swin-PGNet was (1.407±0.939) mm, and by different surgeons was (3.034±2.612) mm. ② Cobb angle: The mean error of Swin-PGNet was (2.062±1.352)° and the mean error of surgeons was (3.580±2.338)°. There was no significant difference between Swin-PGNet and surgeons (P>0.05), but there was a significant difference between different surgeons (P<0.05). ③ Lumbar sliding distance: The mean error of Swin-PGNet was (1.656±0.878) mm and the mean error of surgeons was (1.884±1.612) mm. There was no significant difference between Swin-PGNet and surgeons and between different surgeons (P>0.05). The accuracy of lumbar instability diagnosed by surgeons and Swin-PGNet was 75.3% and 84.0%, respectively. The accuracy of lumbar spondylolisthesis diagnosed by surgeons and Swin-PGNet was 70.7% and 71.3%, respectively. There was no significant difference between Swin-PGNet and surgeons, as well as between different surgeons (P>0.05). ④ Consistency of lumbar stability diagnosis: The ICC of Cobb angle among different surgeons was 0.913 [95%CI (0.898, 0.934)] (P<0.05), and the ICC of lumbar sliding distance was 0.741 [95%CI (0.729, 0.796)] (P<0.05). The result showed that the annotating of the three surgeons were consistent. The ICC of Cobb angle between Swin-PGNet and surgeons was 0.922 [95%CI (0.891, 0.938)] (P<0.05), and the ICC of lumbar sliding distance was 0.748 [95%CI(0.726, 0.783)] (P<0.05). The result showed that the annotating of Swin-PGNet were consistent with those of surgeons. ConclusionThe automatic diagnostic tool for lumbar instability constructed based on deep learning can realize the automatic identification of lumbar instability and spondylolisthesis accurately and conveniently, which can effectively assist clinical diagnosis.