Deep Learning-Assisted Lumbar Degeneration Evaluation Using Plain Radiographs: Development and Validation of a Novel Dual-Mechanism Architecture

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.