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With the increasing prevalence of vertebral fractures, accurate diagnosis and prognostication are essential. This study assesses the effectiveness of AI in diagnosing and predicting vertebral fractures through a systematic review and meta-analysis. A comprehensive search across major databases selected studies utilizing AI for vertebral fracture diagnosis or prognosis. Out of 14,161 studies initially identified, 79 were included, with 40 undergoing meta-analysis. Diagnostic models were stratified by pathology: non-pathological vertebral fractures, osteoporotic vertebral fractures, and vertebral compression fractures. The primary outcome measure was AUROC. AI showed high accuracy in diagnosing and predicting vertebral fractures: predictive AUROC = 0.82, osteoporotic vertebral fracture diagnosis AUROC = 0.92, non-pathological vertebral fracture diagnosis AUROC = 0.85, and vertebral compression fracture diagnosis AUROC = 0.87, all significant (p < 0.001). Traditional models had the highest median AUROC (0.90) for fracture prediction, while deep learning models excelled in diagnosing all fracture types. High heterogeneity (I² > 99%, p < 0.001) indicated significant variation in model design and performance. AI technologies show considerable promise in improving the diagnosis and prognostication of vertebral fractures, with high accuracy. However, observed heterogeneity and study biases necessitate further research. Future efforts should focus on standardizing AI models and validating them across diverse datasets to ensure clinical utility.

Introduction Lumbar disc herniation (LDH) is one of the most common causes of lower back pain, radiculopathy, and functional impairment. Intra-articular (IA) steroid injections, including transforaminal (TFESI), interlaminar (IESI), and caudal (CESI) epidural steroid injections, are commonly administered to alleviate these symptoms when surgery is not indicated or opted for. This systematic review and meta-analysis evaluates the efficacy of these injection modalities in reducing pain and disability in LDH patients. Methods Following PRISMA, 19,664 studies on IA steroid injections for LDH were screened, yielding 41 eligible studies. Random-effects and fixed effects meta-analyses computed pooled standardized mean changes (SMC), depending on heterogeneity (I 2 ). Results TFESI showed strong short-term efficacy, with the greatest pooled NRS improvement of -5.15 (95% CI: -6.59, -3.72, p < 0.001, I 2  = 99.14%) at 3 months and the largest VAS reduction of -30.53 (95% CI: -43.89, -17.17, p < 0.001, I 2  = 99.99%) at 3 months. CESI had the highest ODI improvement at 1 month (-18.99, 95% CI: -26.88, -11.10, p < 0.001, I 2  = 99.35%), while IESI demonstrated the greatest ODI reduction at 6 months (-16.06, 95% CI: -16.83, -15.28, p < 0.001, I 2  = 18.85%). Conclusion This meta-analysis suggests that IA injections may relieve LDH symptoms, with TFESI showing the greatest pain relief and functional improvement. However, significant heterogeneity calls for standardized protocols and further research. Demographic factors minimally influenced outcomes, whereas methodological variability underscores treatment complexity. Future studies should emphasize methodological consistency and personalized approaches to optimize patient outcomes.