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人工智能模型用于牙科创伤学中的应用.

T Bani-Hani1, M Wedyan2, R Al-Fodeh3

  • 1Division of Pediatric Dentistry, Preventive Dentistry Department, Faculty of Dentistry, Jordan University of Science and Technology, P.O.Box 3030, Irbid, 22110, Jordan. tgbanihani@just.edu.jo.

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PubMed
概括
此摘要是机器生成的。

这项研究介绍了一种使用深度学习的人工智能 (AI) 模型,用于从放射图片中分类牙骨折. 人工智能模型在识别不同类型的牙骨折方面表现出高准确性,帮助牙医进行诊断.

关键词:
人工智能的人工智能是人工智能.美国有线电视新闻网 (CNN)卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.牙骨折是指牙骨折的发生.

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科学领域:

  • 牙科创伤学 牙科创伤学
  • 人工智能在牙科中的应用
  • 医学成像分析 医学成像分析

背景情况:

  • 医疗保健系统正在迅速推进新诊断技术,包括人工智能 (AI).
  • 人工智能在牙科中的应用正在出现,但其在牙科创伤学中的应用仍然有限.
  • 这项研究解决了对人工智能驱动的工具在诊断牙骨折的需求.

研究的目的:

  • 开发和评估一种基于深度学习,卷积神经网络 (CNN) 的模型,用于检测和分类牙骨折.
  • 为了评估模型在区分各种类型的牙骨折的准确性,使用周围牙的X射线图.

主要方法:

  • 牙科专家编辑和注释了一组数据集,其中包括72张围牙X射线图,显示了108颗骨折的牙.
  • 使用数据增强技术来提高数据集的稳定性.
  • 使用Python实现了CNN模型,数据分为80%用于培训和20%用于测试,并包含交叉验证.

主要成果:

  • 人工智能模型在区分不同类型的骨折方面取得了很高的准确性:不复杂的皇冠骨折 (96.0%),复杂的皇冠骨折 (96.3%),皇冠根骨折 (99.1%) 和根骨折 (97.2%).
  • 对所有四种类型的牙骨折进行分类的整体准确率为78.7%.
  • 该模型在区分特定骨折类别方面表现出强的表现.

结论:

  • 拟议的AI模型在分类牙科骨折方面表现出色,在儿科牙科和牙科创伤中提供了创新的应用.
  • 该研究建议将该模型扩展到更大的数据集,并探索其与全景放射图的使用,以获得更广泛的临床应用.
  • 人工智能驱动的诊断工具可以显著帮助经验较少的牙医做出准确和及时的牙伤治疗决策.