Objective
To develop and externally validate a dual-mechanism deep learning (DL) model that integrates vertebral segmentation and lesion detection for automated evaluation of lumbar degeneration and structured report generation on plain radiographs.
Methods
In this retrospective study, 5,964 patients who underwent standing anteroposterior and lateral lumbar radiographs at a single institution and 600 patients from a public dataset (BUU-Spine) were included. Vertebral corners from T11–L5 (and S1 on lateral views) and 7 degenerative findings (scoliosis, straightened/preserved lordosis, spondylolisthesis, disc space narrowing, osteophytes, vertebral compression, and abdominal aortic calcification) were annotated by 3 spine surgeons. Two independently trained, parallel networks were developed, including a ResNet-based segmentation network and a YOLOv8-based detection network. A rule-based integration strategy reconciled both outputs and generated structured diagnostic reports. Segmentation accuracy, quantitative measurement agreement, diagnostic performance, and clinical acceptability of reports were evaluated.
Results
Intra- and interobserver landmark distances within 3 mm reached 96% and >95%, respectively. On the internal test set, the percentage of correct keypoints within 3 mm was 95.7%–98.6%, with intraclass correlation coefficients of 0.84–0.89 and Pearson correlation coefficient (r) of 0.90–0.94 for key radiographic parameters. The segmentation- and detection-based models achieved precision of 92.2%–96.9% and 91.7%–95.5%, and recall of 91.6%–94.8% and 93.3%–95.2%, respectively. Under the dual-positive condition, the integrated model yielded the highest precision (93.8%–97.3%), whereas the any-positive condition achieved the highest recall (94.1%–97.6%). Of 596 automatically generated structured reports, 557 (93.4%) were deemed clinically acceptable.
Conclusion
The proposed dual-mechanism DL framework enables accurate, multilesion assessment of lumbar degeneration and generation of clinically acceptable structured reports from plain radiographs, supporting workflow optimization in lumbar spine imaging.

Objective
Posterior apophyseal ring fracture (PARF) is an uncommon disorder that is usually accompanied by lumbar disc herniation (LDH). The aim of this study to describe the 8-year experience of performing minimally invasive treatment of PARF, giving particular attention to surgical technique and clinical outcome.
Methods
We reviewed 1,324 consecutive patients with LDH seen in our department between 2013 and 2020. Forty-eight patients (3.63%) were enrolled who were diagnosed with PARF associated with LDH and underwent transforminal endoscopic lumbar discectomy (TELD). Mean duration of the final postoperative follow-up was 5.1 years. The control group was comprised of 50 patients diagnosed with LDH without PARF at the same facility. Data on clinical outcomes were analyzed.
Results
The mean operation time in the PARF group was 105.4 minutes, which was longer than the mean operation time of the control group (83.9 minutes) (p = 0.001). Surgical complications, including dural tears (6.3%) and surgical instrument rupture (4.2%) were more common in the PARF group (p = 0.025). However, there was no significant difference in the proportion of excellent and good results and recurrence rates between the LDH patients with and those without PARF, respectively.
Conclusion
TELD is a safe and effective minimally invasive approach for the treatment of PARF. However, minimally invasive techniques may require longer operation time and steeper learning curve for inexperienced surgeons. The separation and removal of bone fragments, a key step in the procedure, requires patience and care to prevent rupture, residual surgical instruments, and leakage of cerebrospinal fluid.