我们的目标是对评估人工智能(AI)算法在原发性骨肿瘤检测中的诊断性能的研究进行荟萃分析。将它们与其他骨病变区分开来,并将它们与临床医生的评估进行比较。使用与骨肿瘤和AI相关的关键词的组合进行系统搜索。从所有纳入的研究中提取列联表后,我们使用随机效应模型进行了荟萃分析,以确定合并的敏感性和特异性,伴随着他们各自的95%置信区间(CI)。使用改进版本的多变量预测模型(TRIPOD)和预测模型研究偏差风险评估工具(PROBAST)的透明报告评估质量评估。AI算法和临床医生对内部验证测试集检测骨肿瘤的合并敏感性为84%(95%CI:79.88)和76%(95%CI:64.85)。合并的特异性分别为86%(95%CI:81.90)和64%(95%CI:55.72),分别。在外部验证时,AI算法的合并敏感性和特异性分别为84%(95%CI:75.90)和91%(95%CI:83.96),分别。临床医生的相同数字为85%(95%CI:73.92)和94%(95%CI:89.97),分别。有AI辅助的临床医生的敏感性和特异性分别为95%(95%CI:86.98)和57%(95%CI:48.66)。由于潜在的局限性,在解释发现时需要谨慎。需要进一步的研究来弥合科学认识上的这一差距,并促进医疗实践进步的有效实施。
We aim to conduct a meta-analysis on studies that evaluated the diagnostic performance of artificial intelligence (AI) algorithms in the detection of primary bone tumors, distinguishing them from other bone lesions, and comparing them with clinician assessment. A systematic search was conducted using a combination of keywords related to bone tumors and AI. After extracting contingency tables from all included studies, we performed a meta-analysis using random-effects model to determine the pooled sensitivity and specificity, accompanied by their respective 95% confidence intervals (CI). Quality assessment was evaluated using a modified version of Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST). The pooled sensitivities for AI algorithms and clinicians on internal validation test sets for detecting bone neoplasms were 84% (95% CI: 79.88) and 76% (95% CI: 64.85), and pooled specificities were 86% (95% CI: 81.90) and 64% (95% CI: 55.72), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 84% (95% CI: 75.90) and 91% (95% CI: 83.96), respectively. The same numbers for clinicians were 85% (95% CI: 73.92) and 94% (95% CI: 89.97), respectively. The sensitivity and specificity for clinicians with AI assistance were 95% (95% CI: 86.98) and 57% (95% CI: 48.66). Caution is needed when interpreting findings due to potential limitations. Further research is needed to bridge this gap in scientific understanding and promote effective implementation for medical practice advancement.