Department of the Child Health Department, Women's Hospital of Nanjing Medical University, (Nanjing Women and Children's Healthcare Hospital), Nanjing, Jiangsu, 21000, China.
School of Pediatrics, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
J Orthop Surg Res. 2024 Aug 29;19(1):522. doi: 10.1186/s13018-024-05003-4.
To clarify the efficacy of artificial intelligence (AI)-assisted imaging in the diagnosis of developmental dysplasia of the hip (DDH) through a meta-analysis.
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.
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.
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.
通过荟萃分析澄清人工智能（AI）辅助成像在发育性髋关节发育不良（DDH）诊断中的疗效。
检索了截至 2024 年 4 月 4 日在 PubMed、Web of Science、Embase 和 The Cochrane Library 数据库中有关 AI 早期 DDH 诊断的相关文献。使用诊断准确性研究质量评估工具评估纳入研究的质量。使用 Revman5.4 和 StataSE-64 软件计算 AI 辅助成像对 DDH 诊断的综合灵敏度、特异性、AUC 值和 DOC 值。
荟萃分析纳入了 13 项研究（6 项前瞻性和 7 项回顾性），共 28 个 AI 模型和 10673 个样本。汇总的灵敏度、特异性、AUC 值和 DOC 值分别为 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）。
AI 辅助成像对 DDH 检测具有较高的诊断效能，提高了早期 DDH 成像检查的准确性。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。
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