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[基于深度学习网络模型的横向脑图自动诊断分类]

S Chang1, S F Wang1, F F Zuo2

  • 1Department of Orthodontics, Capital Medical University School of Stomatology, Beijing 100050, China.

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology
|June 5, 2023
PubMed
概括
此摘要是机器生成的。

开发了一种人工智能 (AI) 模型,用于快速分类侧向脑图在正牙诊断中. 这种人工智能工具在识别骨和牙模式方面取得了很高的准确性,有助于临床决策.

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科学领域:

  • 牙科 牙科是指牙科的专业.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • ортодонтика诊断严重依赖于侧向脑图.
  • 精确的骨和牙模式的分类对于有效的治疗规划至关重要.
  • 目前的手动分析可能耗时,并且受观察者之间的变化影响.

研究的目的:

  • 开发和验证基于人工智能 (AI) 的诊断分类模型,用于横向脑图.
  • 提供一个快速可靠的工具,用于正牙诊断.
  • 从横向脑图分类八个常见的诊断项目.

主要方法:

  • 经验丰富的正牙医整理和注释了2894张横向头脑图的数据集.
  • 使用DenseNet121架构构建了AI分类模型.
  • 用准确度,精度,灵敏度,特异性和曲线下面面积 (AUC) 来评估模型性能.

主要成果:

  • 成功建立了横向脑图的自动分类模型.
  • 该模型在五种分类方面实现了高准确度 (80% - 90%),包括骨面部图案和切口的倾斜度.
  • 所有分类的平均AUC为≥0.90,每张图像在0.012秒内快速完成诊断.

结论:

  • 一个基于人工智能的自动分类模型使用DenseNet121成功地开发了侧向脑图.
  • 该模型能够快速准确地分类关键的正诊断项目.
  • 这种人工智能工具可以作为一个有价值的参考,用于正牙诊断和治疗规划.