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相关概念视频

Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over...
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实时组织分类使用新型光学针探针探测器进行剖析.

Lukasz Surazynski1,2, Ville Hassinen1, Miika T Nieminen1,3

  • 1Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.

Applied spectroscopy
|February 19, 2024
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概括

这项研究引入了使用扩散光学光谱学进行实时组织分类的智能针探头,用于核心针活检. 该探头在识别老鼠器官方面取得了近80%的准确性,改善了活检指导.

关键词:
扩散光学光谱学 扩散光学光谱学这是分类分类的分类.核心针活检活检 核心针活检智能探测器是一个智能探测器.

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科学领域:

  • 生物医学工程 生物医学工程
  • 光学光谱学是指光学光谱学.
  • 机器学习 机器学习

背景情况:

  • 核心针活检对于癌症诊断至关重要,通常由超声波指导.
  • 超声波指导需要显著的用户专业知识,并可能会受到患者运动和文物的影响.
  • 一个光学增强的探头可以提供实时组织属性信息,帮助放射科医生.

研究的目的:

  • 开发和描述一个通过扩散光学光谱学增强的核心针活检探针.
  • 评估探针对实时组织分化和器官识别的能力.
  • 为了验证探头在活检过程中向干预放射科医生提供信息的潜力.

主要方法:

  • 设计了一种定制的核心针探头,与扩散光学光谱学集成.
  • 从各种老鼠组织 (血液,脂肪,心脏,脏,肝脏,肺部,肌肉) 中收集了光学光谱.
  • 机器学习分类器 (SVM,k-NN) 被训练并使用k-fold交叉验证进行组织分类进行评估.

主要成果:

  • 探测器成功地从各种生物组织中收集了光学光谱.
  • 机器学习模型证明了基于光学特征区分组织类型的可行性.
  • 性能最好的模型在实时自动分类大鼠器官中实现了近80%的准确性.

结论:

  • 扩散光学光谱学增强的针探针探针显示出对实时组织识别的承诺.
  • 这项技术可以提高核心针活检的精度和安全性.
  • 进一步开发可能会导致改进的干预放射学指导系统.