探索放射数据库研究的方法方法,用于心脏病学为人工智能提供食物:系统性审查
Amadou Diaw Ndiaye1, Marie Agnès Gasqui2,3, Fabien Millioz4
1Service d'Odontologie Conservatrice-Endodontie, Université Cheikh Anta Diop, Dakar, Senegal, adnndiaye@gmail.com.
Caries research
|February 11, 2024
概括
使用人工智能检测牙腐烂的研究表明科学质量较低. 制定标准化指南对于提高AI算法可靠性和牙科临床应用至关重要.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 与牙医相比,计算机辅助诊断系统在诊断成像方面表现出更高的效率和准确性.
- 机器学习和深度学习越来越多地被用于牙科放射分析.
- 牙腐烂的分类,检测和细分是人工智能在牙科中的关键应用.
研究的目的:
- 系统地审查和评估使用机器学习和深度学习进行放射数据库上的牙损伤分析的研究的方法方法.
- 评估该领域现有研究的质量,并确定现有研究方法的弱点.
主要方法:
- 在主要数据库 (MEDLINE,Embase,IEEE Xplore,Web of Science) 进行了系统的文献搜索,直到2022年12月.
- 使用牙科放射数据库进行虫分析的符合条件的研究由两名独立审查员进行评估,第三名审查员对差异进行了评估.
- 使用CLAIM和QUADAS-AI的检查清单来评估方法质量.
主要成果:
- 在325篇选文章中,包括了35项研究,咬翼放射是最常见的 (n=17) 和检测是最常见的任务 (n=15).
- 卷积神经网络是主要使用的模型. CLAIM项目的平均完整性为49%,这表明有显著的方法缺陷.
- 大多数研究都是单心的,只有9%的研究使用了外部测试集. 据 QUADAS-AI 发现,43% 的研究对标准参考领域存在偏差的风险较低.
结论:
- 开发用于牙损伤的AI算法研究的整体科学质量很低.
- 改善研究设计,验证和建立标准化准则是提高可重现性和可通用性的必要.
- 标准化的指导方针对于人工智能在牙科中成功临床应用至关重要.
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