混合更快的R-CNN用于牙编号在基于国际牙科联合会系统的牙周放射
Yong-Shao Su1, I Elizabeth Cha2, Yi-Cheng Mao3
1Division of Periodontics, Department of Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan.
Diagnostics (Basel, Switzerland)
|November 27, 2025
概括
一个新的深度学习工具在牙科放射图中准确编号牙,提高诊断准确性和效率. 这种由人工智能驱动的系统通过克服从周周放射图片中识别牙的挑战来增强临床实践.
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:
- 准确的牙编号对于牙科诊断至关重要,通常使用国际牙科联合会的系统.
- 周周放射 (PA) 角度的变化对精确的牙识别构成挑战.
- 现有的数据集可能由于缺失牙或广泛的冠状损失而存在局限性.
研究的目的:
- 开发一种基于深度学习的工具,用于在PA放射图中准确识别和编号牙.
- 提高牙科实践中的诊断效率和准确性.
- 为应对PA角度变化和数据限制所带来的挑战.
主要方法:
- 开发一种混合更快的基于区域的卷积神经网络 (R-CNN) 技术.
- 为PA牙编号实施定制损失函数,以加快训练.
- 创建一个牙编号位置辅助定位算法来处理缺失的牙和冠状损失.
主要成果:
- 使用基于变压器的NextViT-Faster R-CNN混合模型实现了95.16%的最大精度.
- 与传统方法相比,证明了至少8.5%的精度增加.
- 通过拟议的辅助本地化算法,培训时间减少了19.8%.
结论:
- 开发的AI工具有效地克服了PA牙编号方面的挑战.
- 该方法提高了临床效率,并降低了人工智能辅助牙科诊断中误诊的风险.
- 这些发现支持将先进的AI工具整合到日常牙科实践中.
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