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

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.

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.

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.

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.