Purpose
This study aims to identify risk factors and changes in spino-pelvic parameters associated with Sacroiliac (SI) joint degeneration.
Materials and Methods
This multicenter retrospective study included 472 patients who underwent fusion surgery at three hospitals between March 2021 and February 2024. SI joint degeneration was assessed using seven indicators: sclerotic changes, erosion, osteophyte formation, intra-articular bone formation, joint space narrowing, intra-articular gas formation, and subchondral cysts. CT scans were performed preoperatively and 6 months postoperatively. The patients were divided into two groups: those with progression of SI joint degeneration and those without. Standing whole spine lateral X-rays were used to measure a total of 10 spinopelvic parameters both preoperatively and at 6 months postoperatively. Statistical analysis was performed using two-sample t-tests and multivariable logistic regression.
Results
Among the 472 patients, 135 (28.6%) showed progression of SI joint degeneration. When comparing the two groups, age (p=0.022), alcohol consumption (p=0.001), smoking (p<0.001), and S1 involvement (p=.04) were associated with SI joint degeneration. Regarding spino-pelvic parameters, patients with SI joint degeneration exhibited significant changes in thoracic kyphosis (p=0.017) and pelvic tilt (p=0.049).
Conclusions
Sacrum fixation, smoking, alcohol consumption, and age can be significant risk factors for SIJ degeneration following lumbar fusion surgery.

Purpose
Revision lumbar surgery following posterior decompression is technically challenging because epidural adhesions and altered anatomy increase the risk of complications during posterior re-entry. Surgical approaches that avoid the previously operated corridor may reduce these risks. Biportal endoscopic lumbar interbody fusion using an extraforaminal approach allows direct neural decompression and interbody fusion through a new surgical corridor, which may be advantageous in revision settings. However, clinical evidence regarding this technique in revision surgery remains limited. To evaluate the clinical and radiological outcomes of biportal endoscopic revision extraforaminal lumbar interbody fusion (BE-REFLIF) performed at lumbar segments previously treated with central decompression.
Materials and Methods
This study is Single-center retrospective case series.We retrospectively reviewed 20 consecutive patients who underwent single-level BE-REFLIF as revision surgery after prior central decompression between September 2017 and June 2024. Clinical outcomes were assessed using the visual analogue scale (VAS) for back and leg pain, the Oswestry Disability Index (ODI), and the EuroQol-5D (EQ-5D). Radiological outcomes included disc height, segmental alignment, lumbar lordosis, fusion status, and cage subsidence. Perioperative data and postoperative complications were also analyzed.
Results
Significant improvements were observed in all clinical outcome measures during follow-up. Mean VAS scores for back and leg pain and ODI decreased significantly over time (p < 0.001). Radiological analysis demonstrated significant restoration of disc height, improvement in segmental alignment, and maintenance of lumbar lordosis. Solid fusion was achieved in 85% of patients at the final follow-up, and cage subsidence occurred in 25% of cases without the need for reoperation. Perioperative complications included dural tears in 10% of patients, epidural hematoma in 5%, and surgical site infection in 5%, with no instrumentation-related failures.
Conclusions
Biportal endoscopic revision extraforaminal lumbar interbody fusion demonstrated favorable clinical and radiological outcomes in patients undergoing revision surgery after previous central decompression. By utilizing an extraforaminal corridor that avoids scarred posterior tissues, BE-REFLIF allows effective direct decompression and interbody fusion with an acceptable complication profile. This technique may represent a viable and less invasive option for selected patients requiring revision lumbar fusion.

Purpose
To evaluate the association between lumbar lordosis and age using an AI-based automated measurement model applied to a large dataset of standing lateral spinal radiographs.
Materials and Methods
This retrospective study analyzed 904 high-quality radiographs selected from 2,397 images acquired between 2019 and 2021. Lumbar lordosis was defined as the angle between the superior endplates of L1 and S1 and automatically measured using a validated deep learning model. Subjects were categorized into nine age groups. One-way ANOVA compared lumbar lordosis across age groups, and Pearson correlation assessed the relationship between age and lumbar lordosis.
Results
Lumbar lordosis ranged from 0° to 84° (mean 45.9°±13.4°). The highest mean value was in the 10–19-year group (52.1°), and the lowest in the ≥80-year group (39.6°). Minimum values decreased to 0° in individuals aged ≥60 years. No significant differences were found across age groups (p=0.561). A weak but significant negative correlation was observed between age and lumbar lordosis (r=–0.247, p<0.0001).
Conclusions
AI-based automated measurement enabled efficient large-scale analysis and revealed a wide distribution of lumbar lordosis with a gradual age-related decline. These findings highlight the value of AI in spinal alignment assessment.

Purpose
This study evaluates the performance of Claude and GPT LLM Vision APIs for automated clinical questionnaire processing in spine surgery by comparing accuracy, efficiency, reproducibility, and cost-effectiveness.
Methods
Clinical questionnaires from 56 patients (336 total pages) were processed using a Python 3.12-based system incorporating PDF preprocessing, image enhancement via OpenCV, and direct LLM Vision analysis. Both models were evaluated on 26 questionnaire items (1,456 data points) using accuracy comparison, processing time measurement, token utilization analysis, and intra-class correlation coefficient (ICC) assessment through three independent iterations.
Results
GPT achieved 98.83% accuracy (1,439/1,456) compared to Claude's 97.94% (1,426/1,456). Both models processed questionnaires in 27 seconds per set, representing 68% time reduction versus manual entry (85 seconds). GPT demonstrated 59% cost advantage ($0.023 vs. $0.056 per questionnaire), while Claude showed superior reproducibility (ICC 0.98 vs. 0.96). GPT achieved 100% accuracy across 21 items versus Claude's 17 items. Error analysis identified predominantly handwriting recognition (52%) and image quality issues (28%), with 89% of errors successfully flagged for review.
Conclusions
Both models achieve clinical-grade performance exceeding 90% accuracy. GPT demonstrates superior accuracy and cost-effectiveness, while Claude provides better reproducibility. Model selection should be guided by institutional priorities regarding accuracy, reproducibility, and operational scale.

Objective
This study aimed to evaluate the stability of cement-augmented pedicle screws in patients with osteoporosis of the thoracolumbar spine, with a focus on reducing mechanical failures compared with non-augmented screws.
Methods
A retrospective analysis was conducted on 119 patients who underwent thoracolumbar fusion surgery between 2011 and 2022. The incidence of mechanical failures—including pull-out, screw loosening, and cage protrusion—was compared between patients treated with cement-augmented pedicle screws and those without augmentation.
Results
Cement augmentation was associated with a significant reduction in overall mechanical failures. The incidence of mechanical failure was significantly lower in the cement-augmented group compared with the non-augmented group (20.4% vs. 41.4%, p=0.018). Although individual complications such as pull-out, cage protrusion, and screw loosening were less frequent in the cement-augmented group, these differences were not statistically significant. However, the overall reduction in mechanical failures was statistically significant. Fusion rates were higher in the cement-augmented group than in the non-augmented group, although the difference was not significant (79.6% vs. 70.0%, p=0.337). Importantly, patients without mechanical failures had significantly higher fusion rates than those with failures (82.5% vs. 56.41%, p=0.0048).
Conclusions
Cement-augmented pedicle screws significantly reduce the risk of mechanical failures in thoracolumbar fusion surgery for patients with osteoporosis. Mechanical stability strongly influences fusion success, highlighting the importance of preventing mechanical failure to optimize surgical outcomes. These findings support cement augmentation as an effective strategy to enhance the durability of pedicle screw fixation and should be considered in surgical planning for patients with osteoporosis.

Study Design
Retrospective comparative study.
Purpose
To evaluate and compare the clinical outcomes and complication profiles of decompression alone versus decompression with instrumented fusion in elderly patients aged 75 and older with lumbar spinal stenosis. Overview of Literature: Lumbar spinal stenosis is a common cause of disability in elderly patients. The decision between decompression alone and fusion surgery in the geriatric population remains controversial due to surgical risks and comorbidities.
Methods
A retrospective analysis of 121 patients aged ≥75 years treated either with laminectomy alone (n=60) or with posterior lumbar interbody fusion (PLIF, n=61) from April 2016 to December 2022. Baseline characteristics, perioperative parameters, and postoperative outcomes were compared.
Results
There were no significant differences in baseline characteristics. The PLIF group showed longer operative times, greater blood loss, and longer hospital stay, but similar complication rates. Both groups showed significant postoperative improvement in VAS, ODI, and EQ-5D scores.
Conclusions
Decompression alone and fusion surgery both provide substantial clinical benefit in elderly patients with spinal stenosis. With careful selection, fusion may be safely considered even in the elderly.

Purpose
To develop and validate a deep learning–based artificial intelligence (AI) model for automated measurement of lumbar lordosis (LL) angles from whole spine lateral radiographs.
Materials and Methods
A total of 888 lateral spine X-rays (2019–2021) were retrospectively collected and annotated with four anatomical keypoints (L1 and S1 vertebral landmarks). An AI model using Detectron2 with a Keypoint R-CNN and ResNeXt-101 backbone was trained with data augmentation. Performance was evaluated on 50 test images, comparing AI results to manual annotations by two orthopedic surgeons using intraclass correlation coefficient (ICC), Pearson’s correlation, and Bland–Altman analysis.
Results
The model achieved an average precision of 71.63 for bounding boxes and 86.61 for keypoints. ICCs between AI and human raters ranged from 0.918 to 0.962. Pearson correlation coefficients were r=0.849 and r=0.903. Bland–Altman analysis showed minor underestimation biases (–3.42° and –4.28°) with acceptable agreement.
Conclusions
The AI model showed excellent agreement with expert measurements and high reliability in LL angle assessment. Despite a slight underestimation, it offers a scalable, consistent tool for clinical use. Further studies should evaluate generalizability and interpretability in broader settings.

Citations

• Deep Learning–based AI Analysis of the Correlation Between Lumbar Lordosis and Age
  Soo-Bin Lee, Ja-Yeong Yoon, Dong-Sik Chae, Sang-Bum Kim, Young-Seo Park, Kyung-Yil Kang, Min-Kyu Lee
  Journal of Advanced Spine Surgery.2025; 15(2): 78.     CrossRef
• Efficacy of Biportal Endoscopic Decompression for Lumbar Spinal Stenosis: A Meta-Analysis With Single-Arm Analysis and Comparative Analysis With Microscopic Decompression and Uniportal Endoscopic Decompression
  Shuangwen Lv, Haiwen Lv, Yupeng He, Xiansheng Xia
  Operative Neurosurgery.2024; 27(2): 158.     CrossRef