Abstract：
OBJECTIVE: The aim of the study is to perform a systematic review and meta-analysis comparing the diagnostic performance of artificial intelligence (AI) and human readers in the detection of wrist fractures.
METHODS: This study conducted a systematic review following PRISMA guidelines. Medline and Embase databases were searched for relevant articles published up to August 14, 2023. All included studies reported the diagnostic performance of AI to detect wrist fractures, with or without comparison to human readers. A meta-analysis was performed to calculate the pooled sensitivity and specificity of AI and human experts in detecting distal radius, and scaphoid fractures respectively.
RESULTS: Of 213 identified records, 20 studies were included after abstract screening and full-text review. Nine articles examined distal radius fractures, while eight studies examined scaphoid fractures. One study included distal radius and scaphoid fractures, and two studies examined paediatric distal radius fractures. The pooled sensitivity and specificity for AI in detecting distal radius fractures were 0.92 (95% CI 0.88-0.95) and 0.89 (0.84-0.92), respectively. The corresponding values for human readers were 0.95 (0.91-0.97) and 0.94 (0.91-0.96). For scaphoid fractures, pooled sensitivity and specificity for AI were 0.85 (0.73-0.92) and 0.83 (0.76-0.89), while human experts exhibited 0.71 (0.66-0.76) and 0.93 (0.90-0.95), respectively.
CONCLUSIONS: The results indicate comparable diagnostic accuracy between AI and human readers, especially for distal radius fractures. For the detection of scaphoid fractures, the human readers were similarly sensitive but more specific. These findings underscore the potential of AI to enhance fracture detection accuracy and improve clinical workflow, rather than to replace human intelligence.
METHODS: This study conducted a systematic review following PRISMA guidelines. Medline and Embase databases were searched for relevant articles published up to August 14, 2023. All included studies reported the diagnostic performance of AI to detect wrist fractures, with or without comparison to human readers. A meta-analysis was performed to calculate the pooled sensitivity and specificity of AI and human experts in detecting distal radius, and scaphoid fractures respectively.
RESULTS: Of 213 identified records, 20 studies were included after abstract screening and full-text review. Nine articles examined distal radius fractures, while eight studies examined scaphoid fractures. One study included distal radius and scaphoid fractures, and two studies examined paediatric distal radius fractures. The pooled sensitivity and specificity for AI in detecting distal radius fractures were 0.92 (95% CI 0.88-0.95) and 0.89 (0.84-0.92), respectively. The corresponding values for human readers were 0.95 (0.91-0.97) and 0.94 (0.91-0.96). For scaphoid fractures, pooled sensitivity and specificity for AI were 0.85 (0.73-0.92) and 0.83 (0.76-0.89), while human experts exhibited 0.71 (0.66-0.76) and 0.93 (0.90-0.95), respectively.
CONCLUSIONS: The results indicate comparable diagnostic accuracy between AI and human readers, especially for distal radius fractures. For the detection of scaphoid fractures, the human readers were similarly sensitive but more specific. These findings underscore the potential of AI to enhance fracture detection accuracy and improve clinical workflow, rather than to replace human intelligence.
摘要：
目的：该研究的目的是进行系统综述和荟萃分析，比较人工智能（AI）和人类读者在检测腕关节骨折方面的诊断性能。
方法：本研究遵循PRISMA指南进行了系统评价。在Medline和Embase数据库中搜索了截至2023年8月14日发表的相关文章。所有纳入的研究都报告了AI检测手腕骨折的诊断性能，与人类读者有或没有比较。进行了荟萃分析，以计算AI和人类专家在检测桡骨远端时的合并敏感性和特异性。舟骨骨折。
结果：在213条确定的记录中，经过摘要筛选和全文回顾，纳入了20项研究。九篇文章检查了桡骨远端骨折，而8项研究检查了舟骨骨折。一项研究包括桡骨远端和舟骨骨折，两项研究检查了小儿桡骨远端骨折。合并诊断桡骨远端骨折的AI敏感性和特异性分别为0.92(95%CI0.88-0.95)和0.89(0.84-0.92)。分别。人类读者的相应值为0.95(0.91-0.97)和0.94(0.91-0.96)。对于舟骨骨折，AI的合并敏感性和特异性分别为0.85（0.73-0.92）和0.83（0.76-0.89），而人类专家表现出0.71（0.66-0.76）和0.93（0.90-0.95），分别。
结论：结果表明，人工智能和人类读者的诊断准确性相当，尤其是桡骨远端骨折.为了检测舟骨骨折，人类读者同样敏感，但更具体。这些发现强调了AI在提高骨折检测准确性和改善临床工作流程方面的潜力。而不是取代人类的智慧。
方法：本研究遵循PRISMA指南进行了系统评价。在Medline和Embase数据库中搜索了截至2023年8月14日发表的相关文章。所有纳入的研究都报告了AI检测手腕骨折的诊断性能，与人类读者有或没有比较。进行了荟萃分析，以计算AI和人类专家在检测桡骨远端时的合并敏感性和特异性。舟骨骨折。
结果：在213条确定的记录中，经过摘要筛选和全文回顾，纳入了20项研究。九篇文章检查了桡骨远端骨折，而8项研究检查了舟骨骨折。一项研究包括桡骨远端和舟骨骨折，两项研究检查了小儿桡骨远端骨折。合并诊断桡骨远端骨折的AI敏感性和特异性分别为0.92(95%CI0.88-0.95)和0.89(0.84-0.92)。分别。人类读者的相应值为0.95(0.91-0.97)和0.94(0.91-0.96)。对于舟骨骨折，AI的合并敏感性和特异性分别为0.85（0.73-0.92）和0.83（0.76-0.89），而人类专家表现出0.71（0.66-0.76）和0.93（0.90-0.95），分别。
结论：结果表明，人工智能和人类读者的诊断准确性相当，尤其是桡骨远端骨折.为了检测舟骨骨折，人类读者同样敏感，但更具体。这些发现强调了AI在提高骨折检测准确性和改善临床工作流程方面的潜力。而不是取代人类的智慧。
