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相关概念视频

Atomic Force Microscopy01:08

Atomic Force Microscopy

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Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...
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相关实验视频

Updated: Jun 3, 2025

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System
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用原子力显微镜和机器学习检测人类膀上皮癌细胞.

Mikhail Petrov1, Nadezhda Makarova1, Amir Monemian2

  • 1Department of Mechanical Engineering, Tufts University, Medford, MA 02155, USA.

Cells
|January 10, 2025
PubMed
概括

原子力显微镜 (AFM) 与机器学习相结合,可以准确识别膀癌细胞. 这种技术得到了多道成像的增强,显示出非侵入性膀癌检测的巨大潜力.

关键词:
人工智能的人工智能是人工智能.原子力显微镜的原子力显微镜.癌症 癌症 癌症 癌症 癌症影像成像技术 影像成像技术机器学习是机器学习.纳米医药是一种纳米医药.声模式 声模式

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相关实验视频

Last Updated: Jun 3, 2025

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科学领域:

  • 生物医学工程 生物医学工程
  • 在瘤学瘤学.
  • 纳米技术纳米技术

背景情况:

  • 非侵入性膀癌检测至关重要.
  • 具有机器学习的原子力显微镜 (AFM) 显示出从尿液中识别膀癌细胞的前景.
  • 之前的研究由于患者队列较小和统计学意义较小而存在局限性.

研究的目的:

  • 为了证实AFM识别膀癌细胞的能力.
  • 提高基于AFM的膀癌检测的准确性和统计意义.
  • 为临床开发建立一个严格的技术基础.

主要方法:

  • 利用了基因净化的人类膀上皮细胞系的受控模型系统.
  • 应用AFM分析细胞粘附图,并将癌细胞与非恶性细胞进行比较.
  • 使用机器学习算法处理的AFM数据,包括Ringing模式中的多通道成像.

主要成果:

  • 使用标准AFM粘附图,以91%的准确度达到0.97的ROC曲线下的面积 (AUC).
  • 通过多通道AFM成像增强检测,达到AUC0.99和93%的准确性.
  • 在受控模型系统中证明了统计学上显著的结果 (p < 0.0001).

结论:

  • 与机器学习相结合的AFM是一种非常准确的方法来识别膀癌细胞.
  • 多通道AFM成像进一步提高了检测准确度.
  • 这项研究为基于AFM的膀癌检测提供了强有力的技术验证,支持未来的临床应用.