在诊断牙异常和疾病的深度学习:一个系统的审查
Esra Sivari1, Guler Burcu Senirkentli2, Erkan Bostanci3
1Department of Computer Engineering, Cankiri Karatekin University, Cankiri 18100, Turkey.
Diagnostics (Basel, Switzerland)
|August 12, 2023
概括
深度学习显示出使用图像分析诊断牙科疾病和异常的前景. 需要进一步的研究来规范方法并确保临床安全性和适用性.
科学领域:
- 口腔和牙健康 牙和牙的健康
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 深度学习 (DL) 在口腔和牙科诊断中的应用正在获得引力.
- 为了分析从2019年到2023年5月的牙科成像中的DL研究,进行了一项系统性审查.
研究的目的:
- 系统地编制和分析使用DL诊断牙异常和疾病的研究.
- 在牙科DL应用中评估数据集,方法,性能指标和可解释AI (XAI).
- 引导未来的临床牙科DL的研究和开发.
主要方法:
- 在Medline (PubMed) 和谷歌学者 (2019-May 2023) 的系统文献搜索.
- 对101项入围研究的分析,重点关注DL用于牙科图像的分类,物体检测和细分.
- 评估数据集特征,常见架构,性能指标,XAI使用情况以及人与人工智能的比较研究.
主要成果:
- 分类是最常见的DL任务 (n=51),全景射线图是最常用的图像类型 (n=55).
- 报告了高性能,灵敏度/回忆和准确度是经常使用的指标.
- 有限使用可解释的人工智能 (n=22) 和人类-人工智能比较研究 (n=21).
结论:
- 深度学习显示出改善牙科诊断和治疗规划的巨大潜力.
- 标准化的方法,性能指标和严格的临床适用性测试对于安全和可重复的DL实施至关重要.
- 进一步的研究应该集中在提高牙科DL工具的透明度和临床验证上.
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