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脑度图像分析中的人工智能方法 - 一个系统的叙事审查

Katarzyna Zaborowicz1, Maciej Zaborowicz2, Katarzyna Cieślińska1

  • 1Department of Orthodontics and Facial Malformations, Poznan University of Medical Sciences, Bukowska 70, 60-812 Poznań, Poland.

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概括

头计分析中的人工智能 (AI) 为正牙诊断提供了临床上可接受的准确性. 人工智能工具提高了可重复性,减少了分析时间,支持对面生长和治疗计划的临床决策.

关键词:
人工智能的人工智能是人工智能.脑力测量分析的头脑测量分析宫脊椎成熟期 (CVM) 是指宫脊椎的成熟期.卷积神经网络 (CNN) 是一种神经网络.深度学习是一种深度学习.标志检测 标志检测 标志检测机器学习是机器学习.神经网络的神经网络的神经网络矯正牙科 矯正牙科是指矯正牙科的專業.整形手术是指整形手术.

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科学领域:

  • 矯正牙科 矯正牙科是一種矯正牙科.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 信息技术的进步,包括人工智能,对于正牙诊断至关重要.
  • 头脑测量图像对于正牙治疗计划至关重要,它提供了关于面生长的数据.
  • 机器学习和自动化越来越多地用于分析这些图像.

研究的目的:

  • 审查当前的AI应用在脑表图像分析中.
  • 专注于从Scopus和Web of Science数据库中在2020-2025年间发表的研究.

主要方法:

  • 对20项关键研究进行系统审查.
  • 分析人工智能模型,包括CNN,YOLO,BCNN,ANN等.
  • 在地标检测和分类任务中的绩效评估.

主要成果:

  • 人工智能里程碑检测误差为1-2毫米,在临床限制内.
  • CNN和YOLO模型与牙医准确度相匹配; BCNN提供不确定性估计.
  • ANN模型在宫椎成熟度 (CVM) 上达到95%的准确性;选工具达到了96.3%的一致性.

结论:

  • 人工智能工具在头脑测量分析中显示出临床上可接受的准确性.
  • 人工智能提高了可重复性,并减少了半自动化工作流程中的分析时间.
  • 人工智能显示出作为CVM和手术资格评估的可靠工具的潜力,等待更广泛的验证.