用于牙科诊断的深度学习:一种新的方法,用于在周周X射线图上检测隙参与
Yi-Cheng Mao1, Yen-Cheng Huang1, Tsung-Yi Chen2
1Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan.
Bioengineering (Basel, Switzerland)
|July 29, 2023
概括
一种新型的深度学习模型在牙科放射图上准确检测隙干涉 (FI),改善牙周病的诊断. 这种人工智能辅助的方法提高了诊断准确性和患者的治疗结果.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 裂缺陷在牙周病中存在诊断和治疗方面的挑战.
- 在周周放射 (PAs) 上精确检测口干涉 (FI) 对成功的牙周治疗至关重要.
研究的目的:
- 开发和评估一种基于深度学习的方法,用于检测周周放射图上的孔缺陷.
- 使用人工智能提高牙科诊断的准确性和效率.
主要方法:
- 收集和预处理了一套300张周周放射图的数据集.
- 使用转移学习和微调技术训练了一个卷积神经网络 (CNN) 模型.
- 采用了创新的图像掩盖技术和改进的自适应值预处理方法.
主要成果:
- 拟议的深度学习模型在检测隙干涉方面实现了高达94.97%的整体准确性.
- 裂缺陷的识别率在92.96%至94.97%之间.
- 细分算法超越了传统方法和现有的CNN技术.
结论:
- 深度学习提供了一种高度准确和高效的方法,用于检测周围放射图上的孔缺陷.
- 人工智能辅助的牙科诊断,特别是牙周病,显示出改善治疗计划和患者结果的巨大潜力.
- 这项技术可以更早地检测牙异常,促进及时干预和更好的结果.
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