人工智能计算机断层扫描模型用于辨别威尔姆斯和非威尔姆斯瘤:系统审查和元分析
Helvécio Neves Feitosa Filho1, Rian Vilar Lima1, João Filipe Cavalcante Uchoa Furtado1
1Universidade de Fortaleza, Escola de Medicina, Fortaleza, CE, Brazil.
Jornal brasileiro de nefrologia
|October 10, 2025
概括
人工智能 (AI) 模型在CT扫描上显示中度灵敏度和高特异性,用于识别威尔姆斯瘤. 由于研究有限和异质性,需要进一步验证.
科学领域:
- 医疗成像医学成像
- 在瘤学中使用人工智能
- 诊断准确性研究 诊断准确性研究
背景情况:
- 威尔姆斯瘤是一种儿科癌,需要准确的早期检测.
- 计算机断层扫描 (CT) 扫描对于诊断威尔姆斯瘤至关重要.
- 人工智能 (AI) 在提高威尔姆斯瘤诊断准确度方面的作用正在调查中.
研究的目的:
- 系统地审查和元分析人工智能模型在CT扫描上识别威尔姆斯瘤的有效性.
- 评估AI算法用于威尔姆斯瘤检测的诊断性能指标 (灵敏度,特异性,AUC).
- 评估AI作为威尔姆斯瘤诊断途径中的辅助工具的潜力.
主要方法:
- 按照PRISMA 2020指南,在主要数据库 (MEDLINE,Embase,Web of Science,Cochrane) 进行系统的文献搜索.
- 包括诊断研究报告的灵敏度,特异性和AUC基于AI的威尔姆斯瘤检测在CT.
- 使用R进行随机效应元分析,包括逻辑转换和SROC曲线分析,并进行异质性评估.
主要成果:
- 分析了四项涉及177例威尔姆斯瘤病例和62例对照病例的研究.
- 综合人工智能模型的性能:灵敏度为63.9% (95% CI:0.533-0.734),特异性为82.8% (95% CI:0.716-0.902).
- 曲线下的总面积 (AUC) 为0.831 (95% CI:0.607-0.883),表明诊断能力中等.
结论:
- 人工智能模型在CT扫描上表现出适度灵敏度和高特异性,用于识别威尔姆斯瘤.
- 总体AUC为0.831,表明AI作为诊断成像中的辅助工具具有前景.
- 有限的研究数量和方法异质性需要谨慎的解释和在不同人群中进一步验证.
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