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Updated: Jun 11, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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最佳培训 积极的样本大小确定深度学习与CBCT的验证图像痕识别认可.

Yanlin Wang1, Gang Li1, Xinyue Zhang1

  • 1National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Device & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology, Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, Beijing 100080, China.

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概括

确定最佳的牙数量对于深度学习训练至关重要. 使用单臂客观性能标准 (OPC) 的新方法预测了这个样本大小,提高了虫识别的准确性.

关键词:
在CBCT中,CBCT是CBCT.深度学习是一种深度学习.牙腐烂是指牙的腐烂.口腔辐射学口腔辐射学培训组 培训组 培训组

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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:

  • 深度学习模型培训需要平衡样本大小,资源和时间.
  • 在牙科成像中精确检测牙损伤对于诊断和治疗计划至关重要.

研究的目的:

  • 提出和验证一种方法来确定最佳的积极样本大小,用于训练谷识别的深度学习模型.
  • 评估深度学习模型的性能与牙科放射科医生对比,用于检测从圆束计算机断层扫描 (CBCT) 图像中的.

主要方法:

  • 应用单臂客观绩效标准 (OPC) 与指定的灵敏度值来计算最佳训练集大小.
  • 在CBCT图像上训练并验证了U-Net,YOLOv5n和CariesDetectNet模型,其中包含不同数量的牙.
  • 评估模型性能,并使用独立数据集与两个牙科放射科医生进行比较.

主要成果:

  • 在训练组中大约有250颗牙,可以达到最佳的模型性能.
  • 通过高精度 (0.9929),灵敏度 (0.9307),特异性 (0.9989),F1-Score (0.9590) 和子相似性 (0.9435),U-Net实现了卓越的性能.
  • 深度学习模型在虫识别方面表现出比牙科放射学家更高的准确性.

结论:

  • 在CBCT图像上训练深度学习模型的积极样本大小可预测.
  • 单臂客观性能标准 (OPC) 提供了一种可靠的方法来计算最佳样本大小,提高诊断准确性.