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<p>This study aims to estimate the diagnostic accuracy of deep learning (DL) models for automated detection/classification of spinal degenerative disease (SDD) on spine MRI and explore clinically relevant heterogeneity. We searched Ovid MEDLINE, Ovid Embase and Web of Science (January 2010–5 December 2025) for diagnostic accuracy studies of DL applied to spine MRI with reconstructible 2 × 2 data (TP/FP/FN/TN). Risk of bias was assessed with QUADAS-2. Pooled sensitivity and specificity were synthesised using hierarchical bivariate/HSROC models with a prespecified arm-selection hierarchy. Prespecified subgroup/sensitivity analyses examined spinal region, severity threshold, validation type and target focus. Fourteen studies (2020–2025) were included from 2363 records. Sample sizes ranged from 29 to 2991. Overall pooled sensitivity was 0.94 (95% CI 0.89–0.97) and specificity 0.95 (0.90–0.97) (LR + 17.5; LR − 0.06). Stenosis-focused studies showed lower pooled sensitivity/specificity (0.88/0.92) than studies targeting broader degenerative changes (0.96/0.96). Excluding small studies (<i>n</i> ≤ 50) yielded similar estimates (sensitivity 0.95; specificity 0.95; 12 studies). No study was low risk across all QUADAS-2 domains; 9/14 had ≥ 1 high-risk domain. Deeks’ test showed no evidence of small-study effects (<i>p</i> = 0.28). DL models show high pooled accuracy for SDD detection on MRI, but clinical readiness is constrained by risk of bias, predominantly retrospective single-centre designs, subjective reference standards and limited external validation; prospective multicentre evaluations with prespecified clinically meaningful thresholds are needed.</p>

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Diagnostic Accuracy of Deep Learning for Automated Detection of Spinal Degenerative Disease on MRI: A Systematic Review and Meta-Analysis

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Kalab Yigermal Gete,
  • Piya Durga,
  • Bisrat Abate Bekele,
  • Rebecca Haile Tesfay,
  • Nathan Jibat,
  • Abiy Bete Assefa,
  • Mikias Engeda Gebre,
  • Kassahun Anteneh Yimer,
  • Natnael Bekuretsion Kassahun,
  • Belete Achamyelew Ayele

摘要

This study aims to estimate the diagnostic accuracy of deep learning (DL) models for automated detection/classification of spinal degenerative disease (SDD) on spine MRI and explore clinically relevant heterogeneity. We searched Ovid MEDLINE, Ovid Embase and Web of Science (January 2010–5 December 2025) for diagnostic accuracy studies of DL applied to spine MRI with reconstructible 2 × 2 data (TP/FP/FN/TN). Risk of bias was assessed with QUADAS-2. Pooled sensitivity and specificity were synthesised using hierarchical bivariate/HSROC models with a prespecified arm-selection hierarchy. Prespecified subgroup/sensitivity analyses examined spinal region, severity threshold, validation type and target focus. Fourteen studies (2020–2025) were included from 2363 records. Sample sizes ranged from 29 to 2991. Overall pooled sensitivity was 0.94 (95% CI 0.89–0.97) and specificity 0.95 (0.90–0.97) (LR + 17.5; LR − 0.06). Stenosis-focused studies showed lower pooled sensitivity/specificity (0.88/0.92) than studies targeting broader degenerative changes (0.96/0.96). Excluding small studies (n ≤ 50) yielded similar estimates (sensitivity 0.95; specificity 0.95; 12 studies). No study was low risk across all QUADAS-2 domains; 9/14 had ≥ 1 high-risk domain. Deeks’ test showed no evidence of small-study effects (p = 0.28). DL models show high pooled accuracy for SDD detection on MRI, but clinical readiness is constrained by risk of bias, predominantly retrospective single-centre designs, subjective reference standards and limited external validation; prospective multicentre evaluations with prespecified clinically meaningful thresholds are needed.