用于检测真菌感染的基于人工智能的诊断工具的进展:系统性审查
Noir M Albuqami1, Lina M Alkahtani2, Yara A Alharbi2
1College of Nursing, King Saud bin Abdulaziz University for Health Sciences, Jeddah 21423, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|February 13, 2026
概括
人工智能 (AI) 工具在改善真菌感染诊断方面表现有前途,比传统方法提供更快,更准确的结果. 进一步的研究是需要广泛的临床采用.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 计算生物学 计算生物学
- 传染性疾病 传染性疾病
背景情况:
- 真菌感染对全球健康构成威胁,尤其对免疫力低下的人来说.
- 传统的诊断方法 (显微镜,培养,PCR) 在灵敏度,速度和可访问性方面都有局限性.
- 人工智能 (AI) 和机器学习 (ML) 为真菌感染诊断提供了潜在的进步.
研究的目的:
- 系统地审查和评估人工智能驱动的真菌感染诊断工具的进展,精度和有效性.
- 将人工智能工具的性能与传统诊断程序进行比较.
- 确定人工智能在真菌感染诊断中的当前局限性和未来方向.
主要方法:
- 根据PRISMA原则进行了系统审查.
- 在主要的科学数据库 (PubMed,ScienceDirect,Web of Science,Ovid) 进行了文献搜索.
- 包括并评估了将人工智能诊断工具与基于灵敏度,特异性和准确性的传统方法进行比较的研究.
主要成果:
- 十一项研究 (6项追溯,5项前性) 符合纳入标准.
- 包括CNN,Faster R-CNN,VGG19和MobileNet在内的人工智能模型显示出更高的诊断准确度,灵敏度和特异性.
- 限制包括数据集质量变化,模型验证和现实世界的适用性.
结论:
- 人工智能驱动的诊断显著提高了真菌感染识别的速度和准确性.
- 进一步的广泛研究和临床验证对于提高模型通用性至关重要.
- 无整合到医疗保健系统需要解决当前的局限性,并确保强大的验证.
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