PDF(Pubmed)
Abstract：
UNASSIGNED: To evaluate the diagnostic accuracy of artificial intelligence-based algorithms in identifying neck of femur fracture on a plain radiograph.
UNASSIGNED: Systematic review and meta-analysis.
UNASSIGNED: PubMed, Web of science, Scopus, IEEE, and the Science direct databases were searched from inception to 30 July 2023.
UNASSIGNED: Eligible article types were descriptive, analytical, or trial studies published in the English language providing data on the utility of artificial intelligence (AI) based algorithms in the detection of the neck of the femur (NOF) fracture on plain X-ray.
UNASSIGNED: The prespecified primary outcome was to calculate the sensitivity, specificity, accuracy, Youden index, and positive and negative likelihood ratios. Two teams of reviewers (each consisting of two members) extracted the data from available information in each study. The risk of bias was assessed using a mix of the CLAIM (the Checklist for AI in Medical Imaging) and QUADAS-2 (A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies) criteria.
UNASSIGNED: Of the 437 articles retrieved, five were eligible for inclusion, and the pooled sensitivity of AIs in diagnosing the fracture NOF was 85%, with a specificity of 87%. For all studies, the pooled Youden index (YI) was 0.73. The average positive likelihood ratio (PLR) was 19.88, whereas the negative likelihood ratio (NLR) was 0.17. The random effects model showed an overall odds of 1.16 (0.84-1.61) in the forest plot, comparing the AI system with those of human diagnosis. The overall heterogeneity of the studies was marginal (I2 = 51%). The CLAIM criteria for risk of bias assessment had an overall >70% score.
UNASSIGNED: Artificial intelligence (AI)-based algorithms can be used as a diagnostic adjunct, benefiting clinicians by taking less time and effort in neck of the femur (NOF) fracture diagnosis.
UNASSIGNED: PROSPERO CRD42022375449.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s43465-024-01130-6.
UNASSIGNED: Systematic review and meta-analysis.
UNASSIGNED: PubMed, Web of science, Scopus, IEEE, and the Science direct databases were searched from inception to 30 July 2023.
UNASSIGNED: Eligible article types were descriptive, analytical, or trial studies published in the English language providing data on the utility of artificial intelligence (AI) based algorithms in the detection of the neck of the femur (NOF) fracture on plain X-ray.
UNASSIGNED: The prespecified primary outcome was to calculate the sensitivity, specificity, accuracy, Youden index, and positive and negative likelihood ratios. Two teams of reviewers (each consisting of two members) extracted the data from available information in each study. The risk of bias was assessed using a mix of the CLAIM (the Checklist for AI in Medical Imaging) and QUADAS-2 (A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies) criteria.
UNASSIGNED: Of the 437 articles retrieved, five were eligible for inclusion, and the pooled sensitivity of AIs in diagnosing the fracture NOF was 85%, with a specificity of 87%. For all studies, the pooled Youden index (YI) was 0.73. The average positive likelihood ratio (PLR) was 19.88, whereas the negative likelihood ratio (NLR) was 0.17. The random effects model showed an overall odds of 1.16 (0.84-1.61) in the forest plot, comparing the AI system with those of human diagnosis. The overall heterogeneity of the studies was marginal (I2 = 51%). The CLAIM criteria for risk of bias assessment had an overall >70% score.
UNASSIGNED: Artificial intelligence (AI)-based algorithms can be used as a diagnostic adjunct, benefiting clinicians by taking less time and effort in neck of the femur (NOF) fracture diagnosis.
UNASSIGNED: PROSPERO CRD42022375449.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s43465-024-01130-6.
摘要：
■评估基于人工智能的算法在X线平片上识别股骨颈骨折的诊断准确性。
■系统评价和荟萃分析。
■PubMed,WebofScience,Scopus,IEEE,从开始到2023年7月30日搜索了科学直接数据库。
■符合条件的文章类型是描述性的，分析，或以英语发表的试验研究提供了有关基于人工智能（AI）的算法在X线平片上检测股骨颈（NOF）骨折中的实用性的数据。
■预设的主要结局是计算灵敏度，特异性，准确度,尤登指数,以及正负似然比。两个评审小组（每个小组由两名成员组成）从每个研究中的可用信息中提取数据。使用CLAIM（医学成像AI检查表）和QUADAS-2（诊断准确性研究质量评估的修订工具）标准的组合来评估偏倚风险。
■在检索到的437篇文章中，五人有资格入选，AI诊断骨折NOF的合并敏感性为85%，特异性为87%。对于所有的研究,合并尤登指数（YI）为0.73。平均正似然比（PLR）为19.88，而负似然比（NLR）为0.17。随机效应模型显示，森林地块的总体赔率为1.16(0.84-1.61)，将人工智能系统与人类诊断系统进行比较。研究的总体异质性是边缘的(I2=51%)。偏倚风险评估的CLAIM标准总体得分>70%。
■基于人工智能（AI）的算法可以用作诊断辅助，通过减少股骨颈（NOF）骨折诊断的时间和精力，使临床医生受益。
■PROSPEROCRD42022375449。
■在线版本包含补充材料，可在10.1007/s43465-024-01130-6获得。
■系统评价和荟萃分析。
■PubMed,WebofScience,Scopus,IEEE,从开始到2023年7月30日搜索了科学直接数据库。
■符合条件的文章类型是描述性的，分析，或以英语发表的试验研究提供了有关基于人工智能（AI）的算法在X线平片上检测股骨颈（NOF）骨折中的实用性的数据。
■预设的主要结局是计算灵敏度，特异性，准确度,尤登指数,以及正负似然比。两个评审小组（每个小组由两名成员组成）从每个研究中的可用信息中提取数据。使用CLAIM（医学成像AI检查表）和QUADAS-2（诊断准确性研究质量评估的修订工具）标准的组合来评估偏倚风险。
■在检索到的437篇文章中，五人有资格入选，AI诊断骨折NOF的合并敏感性为85%，特异性为87%。对于所有的研究,合并尤登指数（YI）为0.73。平均正似然比（PLR）为19.88，而负似然比（NLR）为0.17。随机效应模型显示，森林地块的总体赔率为1.16(0.84-1.61)，将人工智能系统与人类诊断系统进行比较。研究的总体异质性是边缘的(I2=51%)。偏倚风险评估的CLAIM标准总体得分>70%。
■基于人工智能（AI）的算法可以用作诊断辅助，通过减少股骨颈（NOF）骨折诊断的时间和精力，使临床医生受益。
■PROSPEROCRD42022375449。
■在线版本包含补充材料，可在10.1007/s43465-024-01130-6获得。
