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人工智能用于检测使用标签高效的自我监督学习方法检测外部宫吸收.

Hossein Mohammad-Rahimi1, Omid Dianat2, Reza Abbasi3

  • 1Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

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概括
此摘要是机器生成的。

自主监督学习 (SSL) 模型在检测内牙-牙周病变 (ECR) 和将其与放射图上的牙区分开来方面表现有前途. 这些人工智能模型的性能优于传统方法,减少了对广泛标记数据的需求.

关键词:
人工智能的人工智能是人工智能.皮肤腐烂,皮肤腐烂.诊断 诊断 诊断 诊断 诊断 诊断外部宫再吸收 - 外部宫再吸收吸收再吸收的方法自主监督学习学习

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科学领域:

  • 人工智能在牙科中的应用
  • 医学成像分析 医学成像分析
  • 机器学习用于诊断.

背景情况:

  • 检测内-牙周病变 (ECR) 并将其与牙区分开来,对于准确的牙科诊断至关重要.
  • 传统方法通常需要大型的标记数据集,这可能是耗时且昂贵的.

研究的目的:

  • 评估标签效率高的自主监督学习 (SSL) 模型在检测ECR和区分其与虫病方面的有效性.
  • 将各种SSL模型的性能与放射分析的传统深度学习基线进行比较.

主要方法:

  • 收集了带有和没有ECR缺陷的牙的周周 (PA) 放射图.
  • 使用两位内牙科医生来使用PA和CBCT成像来确定地面真相.
  • 实施和评估了九个SSL模型 (例如,DINO,MoCo v2) 和七个基线深度学习模型.
  • 采用十倍交叉验证和持久测试套件来进行可靠的模型评估.

主要成果:

  • 大多数SSL模型的表现明显超过了转移学习基线.
  • DINO模型实现了最高的平均精度 (85.64%) 和测试组精度 (84.09%).
  • MoCo v2显示了最高的召回率 (77.37%) 和F1得分 (82.93%).

结论:

  • 人工智能,特别是基于SSL的模型,可以有效地帮助临床医生检测ECR并将其与区分开来.
  • SSL模型为传统方法提供了可行的替代方案,减少了对大型标记数据集的依赖.
  • 这项研究突出了SSL在通过改进的放射分析来推进牙科诊断方面的潜力.