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Purpose <p>To evaluate the performance of Q-Spine, a semi-automatic CAD system, in improving reproducibility, efficiency, and workload in morphometric assessment of upright lumbar spine MRI.</p> Materials and methods <p>This retrospective study included 16 patients (6 men, 10 women; mean age 45&#xa0;years; range 21–80) who underwent upright lumbar MRI. Imaging was performed on a 0.25-T open MRI system using standardized sagittal and axial sequences. Two radiologists (12&#xa0;year vs 2&#xa0;year experience) independently analyzed spinal canal area, sagittal canal thickness, spinal curvature, vertebral collapse, intervertebral angles, foraminal area, vertebral wedging, and spondylolisthesis index, using both manual measurements and the Q-Spine CAD system. Inter- and intra-observer agreement was assessed with Spearman’s correlation, intraclass correlation coefficients (ICC), and Concordance Correlation Coefficients (CCC). Processing times and observer workload (NASA-TLX) were compared between manual and CAD-assisted analyses.</p> Results <p>Compared with manual assessment, Q-Spine significantly improved inter- and intra-observer agreement, particularly for spinal canal area, intervertebral angles, vertebral wedging, and spondylolisthesis index (CCC range: 0.869–0.956 vs 0.800 for manual). Median processing time was reduced by &gt; 90% with Q-Spine (5.3 vs 56.0&#xa0;min, <i>p</i> &lt; 0.0001). Subjective workload scores (NASA-TLX) were significantly lower with Q-Spine, especially for mental demand, temporal demand, and frustration.</p> Conclusion <p>The Q-Spine semi-automatic CAD system enhances reproducibility, dramatically reduces analysis time, and lowers observer workload in morphometric assessment of upright lumbar spine MRI. These findings highlight the potential of AI-assisted tools to improve both diagnostic consistency and workflow efficiency in dynamic spinal imaging.</p>

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Semi-automatic CAD for upright lumbar spine MRI: reproducibility, time efficiency, and workload reduction with Q-Spine

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月17日

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  • Federico Bruno,
  • Federica Antolini,
  • Claudia Tamburello,
  • Chiara Santobuono,
  • Giulia Caldarelli,
  • Roberto Balbi,
  • Antonio Innocenzi,
  • Gaspare Saltarelli,
  • Giovanni Di Cerbo,
  • Pierpaolo Palumbo,
  • Mario Muselli,
  • Francesco Arrigoni,
  • Ernesto Di Cesare,
  • Antonio Barile,
  • Alessandra Splendiani

摘要

Purpose

To evaluate the performance of Q-Spine, a semi-automatic CAD system, in improving reproducibility, efficiency, and workload in morphometric assessment of upright lumbar spine MRI.

Materials and methods

This retrospective study included 16 patients (6 men, 10 women; mean age 45 years; range 21–80) who underwent upright lumbar MRI. Imaging was performed on a 0.25-T open MRI system using standardized sagittal and axial sequences. Two radiologists (12 year vs 2 year experience) independently analyzed spinal canal area, sagittal canal thickness, spinal curvature, vertebral collapse, intervertebral angles, foraminal area, vertebral wedging, and spondylolisthesis index, using both manual measurements and the Q-Spine CAD system. Inter- and intra-observer agreement was assessed with Spearman’s correlation, intraclass correlation coefficients (ICC), and Concordance Correlation Coefficients (CCC). Processing times and observer workload (NASA-TLX) were compared between manual and CAD-assisted analyses.

Results

Compared with manual assessment, Q-Spine significantly improved inter- and intra-observer agreement, particularly for spinal canal area, intervertebral angles, vertebral wedging, and spondylolisthesis index (CCC range: 0.869–0.956 vs 0.800 for manual). Median processing time was reduced by > 90% with Q-Spine (5.3 vs 56.0 min, p < 0.0001). Subjective workload scores (NASA-TLX) were significantly lower with Q-Spine, especially for mental demand, temporal demand, and frustration.

Conclusion

The Q-Spine semi-automatic CAD system enhances reproducibility, dramatically reduces analysis time, and lowers observer workload in morphometric assessment of upright lumbar spine MRI. These findings highlight the potential of AI-assisted tools to improve both diagnostic consistency and workflow efficiency in dynamic spinal imaging.