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基于图像的牙科诊断中的深度学习应用:系统性审查

Osama Khattak1, Ahmed Shawkat Hashem2, Mohammed Saad Alqarni3

  • 1Department of Restorative Dentistry, College of Dentistry, Jouf University, Sakaka 72311, Saudi Arabia.

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|June 26, 2025
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概括

牙科中的人工智能 (AI) 显示出82%的诊断准确度,改善了牙虫的检测. 挑战包括数据偏差和道德问题,需要仔细整合未来的牙科医疗保健.

关键词:
人工智能模型是AI模型.人工智能的人工智能是人工智能.牙科科学 牙科科学诊断 诊断 诊断 诊断 诊断 诊断机器学习是机器学习.

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科学领域:

  • 牙科信息学 牙科信息学
  • 人工智能在医学中的应用
  • 系统审查和元分析.

背景情况:

  • 人工智能 (AI) 在牙科中越来越多地用于诊断,治疗计划和预后.
  • 本综述系统评估了牙科人工智能模型,评估了它们的性能,局限性和未来的整合潜力.

研究的目的:

  • 识别和评估在牙科应用的AI模型.
  • 评估这些人工智能模型的诊断准确性和预测性能.
  • 讨论在牙科实践中采用人工智能的挑战和伦理考虑.

主要方法:

  • 在PubMed,Scopus和Cochrane图书馆的系统文献搜索.
  • 在947篇已识别的论文中对20项已选定的研究进行了元分析.
  • 评估人工智能模型的诊断准确性,预测性能和潜在偏差.

主要成果:

  • 人工智能模型的平均诊断准确率为82%,主要使用人工神经网络 (ANN) 和卷积神经网络 (CNN).
  • 与传统方法相比,在诊断牙损伤方面观察到显著的改善.
  • 人工智能在检测骨损失,病变,囊等各种疾病和正牙评估方面表现有前途,但在数据偏差,成本和数据安全方面面临挑战.

结论:

  • 人工智能为牙科带来了变革的潜力,提高了诊断精度和治疗计划.
  • 在广泛的临床采用之前,需要对人工智能的好处,缺点和伦理影响进行批判性评估.
  • 未来的研究应该解决与数据,成本和安全相关的障碍,以促进在牙科医疗保健中有效利用人工智能.