目的:通过荟萃分析阐明人工智能(AI)辅助成像在髋关节发育不良(DDH)诊断中的功效。
方法:在PubMed,WebofScience,Embase,和Cochrane图书馆数据库,直到2024年4月4日。诊断准确性研究的质量评估工具用于评估纳入研究的质量。使用Revman5.4和StataSE-64软件计算组合灵敏度,特异性,AUC值,AI辅助成像诊断DDH的DOC值。
结果:荟萃分析包括13项研究(6项前瞻性和7项回顾性研究),包括28个AI模型和总共10,673个样本。摘要敏感性,特异性,AUC值,DOC值为99.0%(95%CI:97.0-100.0%),94.0%(95%CI:89.0-96.0%),99.0%(95%CI:98.0-100.0%),和1342(95%CI:469-3842),分别。
结论:AI辅助成像对DDH检测具有很高的诊断效能,提高早期DDH影像学检查的准确性。需要更多的前瞻性研究来进一步证实AI辅助成像对早期DDH诊断的价值。
OBJECTIVE: To clarify the efficacy of artificial intelligence (AI)-assisted imaging in the diagnosis of developmental dysplasia of the hip (DDH) through a meta-analysis.
METHODS: Relevant literature on AI for early DDH diagnosis was searched in PubMed, Web of Science, Embase, and The Cochrane Library databases until April 4, 2024. The Quality Assessment of Diagnostic Accuracy Studies tool was used to assess the quality of included studies. Revman5.4 and StataSE-64 software were used to calculate the combined sensitivity, specificity, AUC value, and DOC value of AI-assisted imaging for DDH diagnosis.
RESULTS: The meta-analysis included 13 studies (6 prospective and 7 retrospective) with 28 AI models and a total of 10,673 samples. The summary sensitivity, specificity, AUC value, and DOC value were 99.0% (95% CI: 97.0-100.0%), 94.0% (95% CI: 89.0-96.0%), 99.0% (95% CI: 98.0-100.0%), and 1342 (95% CI: 469-3842), respectively.
CONCLUSIONS: AI-assisted imaging demonstrates high diagnostic efficacy for DDH detection, improving the accuracy of early DDH imaging examination. More prospective studies are needed to further confirm the value of AI-assisted imaging for early DDH diagnosis.