人工智能用于识别糖尿病足的诊断准确性:系统性审查和元分析
Zhigang Chen1, Xinliang Liu2, Simeng Li1
1Department of Gastrointestinal Surgery, The Second People's Hospital of Changzhou, the Third Affiliated Hospital of Nanjing Medical University, Changzhou Medical Center, Nanjing Medical University, Changzhou, 213000, Jiangsu, China.
人工智能 (AI) 在诊断糖尿病足 (DFU) 中表现出高准确性. 这一元分析证实了AI的存在.
科学领域:
- 皮肤病学和医疗信息学
- 医疗保健中的人工智能
- 诊断准确性研究 诊断准确性研究
背景情况:
- 人工智能 (AI) 在皮肤病学中的糖尿病足的诊断准确性需要进一步研究.
- 关于AI在临床DFU诊断中的有效性存在不确定性.
研究的目的:
- 系统地评估和总结AI在识别DFU中的诊断准确性.
- 建立人工智能辅助的DFU临床诊断的理论基础.
主要方法:
- 在主要数据库 (PubMed, WoS, Embase, Scopus, Cochrane, Wanfang, CNKI) 进行了全面的文献搜索,截至2024年11月3日.
- 分析使用灵敏度,特异性,正概率比 (PLR),负概率比 (NLR),诊断几率比 (DOR) 和曲线下的面积 (AUC) 来评估AI性能.
- 使用Deeks的漏斗图形不对称性测试来评估出版偏差.
主要成果:
- 分析包括16项研究,揭示了在DFU检测中AI的高诊断性能.
- 关键指标包括:灵敏度0.89,特异性0.93,PLR 6.31,NLR 0.14,DOR 58.22和AUC 0.97.22,其中包括敏感度0.89,特异性0.93,PLR 6.31,NLR 0.14,DOR 58.22和AUC 0.97.
- 在300-1000个样本的研究中发现了最佳的AI性能,将测试后的概率从10%提高到59%.
结论:
- 人工智能在诊断糖尿病足方面表现出显著的准确性.
- 这些发现支持将人工智能工具集成到临床实践中,用于DFU诊断.
- 进一步的研究可能会改进人工智能算法,以改善DFU管理.
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