通过深度学习对下关节裂参与周围放射图的分类
Katerina Vilkomir1, Cody Phen2, Fiondra Baldwin2
1Department of Computer Science, East Carolina University, Greenville, NC, USA.
Imaging science in dentistry
|October 7, 2024
概括
一个深度学习算法准确地分类了牙X射线中的下毛裂干涉 (FI). 这种人工智能工具对检测牙周病非常有希望,有助于牙医在诊断和管理中.
科学领域:
- 牙科 牙科 牙科 牙科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 裂参与 (FI) 是牙上牙周病进展的关键指标.
- 精确的FI分类对于有效的治疗计划和患者的治疗结果至关重要.
- 对FI的放射性解释可能是主观的,耗时的.
研究的目的:
- 开发和评估一种深度学习算法,用于在周围放射图中分类下毛裂干涉 (FI).
- 评估ResNet-18模型对自动FI检测的准确性和适用性.
主要方法:
- 一组带有健康或FI下牙的周周放射图的数据集被策划和注释.
- 该ResNet-18卷积神经网络模型使用PyTorch.ch进行了训练和验证.
- 使用包括精度,灵敏度,特异性和ROC分析在内的指标来评估性能.
主要成果:
- 在测试组中,ResNet-18算法在分类健康与FI下牙方面实现了96.47%的准确性.
- 该模型在各种评估指标上表现出强的表现,表明其可靠性.
结论:
- 开发的深度学习算法是一个有前途的工具,用于从X射线图分类下毛FI.
- 这种人工智能方法可以作为一种有价值的补充工具,用于早期发现和管理牙周病.
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