通过人工智能增强尿细胞病理学:系统性审查
Fatima Nabiyouni1, Paul Z Chiou1
1Department of Clinical Laboratory and Medical Imaging Sciences, Rutgers University, The State University of New Jersey, Newark, NJ, United States.
American journal of clinical pathology
|February 5, 2026
概括
人工智能 (AI) 在改善尿液细胞病理学以检测尿癌 (UC) 方面表现有前途. 人工智能提高了诊断的准确性和效率,有可能减少临床环境中的病理学家工作量.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 病理学 病理学 病理学
- 人工智能的人工智能
背景情况:
- 尿癌 (UC) 的检测依赖于精确的尿细胞病理学.
- 病理学家的工作量和诊断变异性在UC诊断中提出了挑战.
研究的目的:
- 评估人工智能 (AI) 增强尿细胞病理学在检测泌尿腺癌 (UC).
- 评估AI对诊断灵敏度,准确性,效率和病理学家工作量的影响.
主要方法:
- 按照PRISMA指南进行系统审查.
- 在PubMed,谷歌学者,EMBASE和ScienceDirect (2018年1月至2025年7月) 中进行了搜索.
- 包括英语研究,将AI应用于尿细胞学以检测UC,报告敏感性和特异性.
主要成果:
- 11项研究 (116-2641例) 符合纳入标准.
- 人工智能模型,主要是卷积神经网络,对于高度UC (HGUC) 显示出高灵敏度 (63-100%) 和特异性 (61.8-100%).
- 人工智能有可能改善UC检测并简化临床工作流程.
结论:
- 人工智能显示出作为用于检测HGUC的尿细胞病理学诊断辅助的显著潜力.
- 人工智能可以提高细胞病理学的诊断准确性和效率.
- 为了广泛实施人工智能,需要进行标准化和大规模验证研究.
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