人工智能能能找到空洞虫病的预测和诊断吗?
Lucy Bennett1, Lucy Tiplady1, Greig Taylor2
1Dental Core Trainee, Newcastle Dental Hospital, Newcastle upon Tyne, UK.
Evidence-based dentistry
|July 27, 2025
概括
人工智能在检测和预测幼儿 (ECC) 方面显示出显著的前景. 这次系统性审查强调了AI.
科学领域:
- 牙科 牙科是指牙科的专业.
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
背景情况:
- 幼儿 (ECC) 是全球口腔健康的一个重大挑战.
- 准确的检测和预测对于及时干预和预防策略至关重要.
- 传统的ECC诊断方法可能是主观的,耗时的.
研究的目的:
- 系统地审查和元分析人工智能 (AI) 算法用于检测和预测幼儿 (ECC) 的使用.
- 评估人工智能的性能,包括机器学习 (ML) 和深度学习 (DL),在ECC诊断和预后.
主要方法:
- 在主要数据库 (PubMed,Scopus,Embase,Web of Science,IEEE) 和灰色文献中进行了全面的文献搜索,截至2024年5月.
- 包括在2010年至2024年期间发表的使用AI (ML,DL,NN) 进行ECC检测/预测的研究.
- 数据的提取和合成涉及描述性总结和从21项选定的研究中对敏感性和特异性的定量元分析.
主要成果:
- 基于人工智能的方法,特别是DL算法,在ECC检测方面表现出很高的性能 (精度为78-86%,灵敏度为67-96%,特异性为81-99%).
- 人工智能模型显示了ECC预测的可变但有希望的结果 (准确度60-100%,灵敏度20-100%,特异性54-94%).
- 在ECC检测/预测中对AI的聚合灵敏度和特异性分别为80%和81%,具有统计学上显著的影响.
结论:
- 人工智能具有很大的潜力,可以改善早期儿童的检测和预测.
- 进一步的研究对于完善人工智能技术和有效地将其整合到儿科牙科实践中至关重要.
- 人工智能应用可以彻底改变幼儿ECC的诊断和管理方式.
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