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基于深度学习的方法:使用牙去除策略的自动化牙炎症分级模型.

Chang Wen1,2, Xueying Bai1, Jiaxin Yang1

  • 1State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, #237 Luoyu Road, Hongshan District, Wuhan, China.

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PubMed
概括

一个新的深度学习模型使用先进的特征提取方法准确评估牙炎症. 这种人工智能工具通过精确地从图像中分类炎症严重程度来增强牙周炎的诊断.

关键词:
辅助诊断是一种辅助诊断.深度学习是一种深度学习.牙炎症 牙炎症在口腔内拍摄的照片图像.牙周病是牙周病的一种疾病.

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科学领域:

  • 牙周病学和牙科成像学
  • 医疗保健中的人工智能
  • 计算机视觉用于医学诊断

背景情况:

  • 牙炎炎症分级对于牙周炎的评估至关重要.
  • 目前评估牙炎症的方法可能是主观的,耗时的.
  • 需要客观和自动化的评估工具来提高诊断的准确性和效率.

研究的目的:

  • 开发一个深度学习 (DL) 网络,用于自动评估牙炎症.
  • 实施一种新的特征提取方法,以提高DL模型性能.
  • 评估拟议的DL模型在识别和分类牙炎症方面的准确性和灵敏性.

主要方法:

  • 使用T分布式静态邻近嵌入 (t-SNE) 来减少维度.
  • 开发了一个基于DenseNet的卷积神经网络 (CNN) 模型,用于牙炎症识别.
  • 实现了一种新的牙去除算法和Grad-CAM++编码器用于计算机视觉注意力分析.

主要成果:

  • 实现了0.727 ± 0.117的平均交叉点在欧盟 (MIoU) 上,用于牙炎的识别.
  • 获得的准确率为5个炎症程度,范围从73.68%到79.22%.
  • 使用Grad-CAM++显著增加了对牙组织的注意力比率 (总体为51.82%至78.21%).

结论:

  • 拟议的DL模型具有新型特征提取,为牙炎症评估提供了高准确度和灵敏度.
  • 自动化系统提供了牙炎的客观分级,有助于牙周炎的诊断.
  • 这种人工智能驱动的方法有可能改善牙周科的临床工作流程和患者结果.