需要填充的空和需要根管治疗的空的自动区分使用机器学习
Mehmet Sinan Oruç1,2, İmam Şamil Yetik3, Özgür Karaman İncekürk4
1Electrical and Electronics Engineering, TOBB University of Economics and Technology, Ankara, Türkiye. m.oruc@etu.edu.tr.
Oral radiology
|November 1, 2025
概括
一个深度学习系统帮助牙医诊断牙并推治疗方法. 检测器-2在识别虫和建议适当的干预措施方面取得了最高的准确性,支持新手和经验丰富的从业者.
科学领域:
- 人工智能在牙科中的应用
- 对于医学成像的深度学习
- 牙科诊断 牙科诊断 牙科诊断
背景情况:
- 新手牙医需要在诊断需要填充或根管治疗的牙方面得到支持.
- 准确的牙损伤识别对于确定适当的牙科治疗计划至关重要.
- 区分不同类型的 (无,单或多) 影响治疗决策.
研究的目的:
- 开发一个基于深度学习的推系统,用于牙损伤的诊断和治疗计划.
- 帮助牙医,特别是那些在职业生涯早期的牙医,识别虫类型和必要的治疗方法.
- 评估不同深度学习模型在虫检测和治疗建议中的有效性.
主要方法:
- 利用1253个增强的咬翼图像来检测第一牙的.
- 使用了三个细分方法:YOLOv8,U-Net和Detectron-2.
- 引入了一种新的推系统,以根据检测到的脏病来确定治疗类型.
主要成果:
- 在基于像素标签的虫检测中,Detectron-2的准确度最高,为95.03%.
- 在治疗建议方面,Detectron-2获得了88.09%的成功率,超过了YOLOv8 (70.23%) 和U-Net (61.90%).
- 开发的系统被认为是可接受的辅助治疗建议工具.
结论:
- 深度学习系统是所有经验水平的牙医的宝贵诊断工具.
- 基于学习的细分方法显示出在治疗建议中临床应用的巨大潜力.
- 拟议的系统可以提高牙科的诊断准确性和治疗规划.
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