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Related Concept Videos

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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Near Simultaneous Laser Scanning Confocal and Atomic Force Microscopy Conpokal on Live Cells
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Near Simultaneous Laser Scanning Confocal and Atomic Force Microscopy Conpokal on Live Cells

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Deep Learning for Live Cell Shape Detection and Automated AFM Navigation.

Jaydeep Rade1, Juntao Zhang2, Soumik Sarkar2

  • 1Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA.

Bioengineering (Basel, Switzerland)
|October 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning framework for automated atomic force microscopy (AFM) navigation, enabling faster and more efficient biomechanical mapping of live cells. The AI-guided system significantly speeds up sample selection and analysis for bio-AFM applications.

Keywords:
YOLOv3atomic force microscopedeep learningobject detectionvision-based navigation

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Area of Science:

  • Biophysics
  • Microscopy
  • Artificial Intelligence

Background:

  • Atomic force microscopy (AFM) is crucial for high-resolution imaging and mechanical characterization of biomolecules and live cells.
  • AFM enables single-molecule force and binding kinetics measurements but is often time-consuming and requires expert supervision.
  • Artificial intelligence (AI) and deep learning (DL) are emerging in bioimaging, yet their application to AFM for live-cell analysis is limited.

Purpose of the Study:

  • To implement a DL framework for automated cell selection and AFM probe navigation.
  • To establish closed-loop scanner control for high-speed measurement of multiple cell samples.
  • To enhance the efficiency and reduce the time required for AFM-based live-cell characterization.

Main Methods:

  • Developed a DL framework for automatic sample selection based on cell shape for AFM probe navigation.
  • Implemented a closed-loop scanner trajectory control system for automated navigation and high-speed measurements.
  • Integrated image data analysis with smart navigation for AI-guided automation.

Main Results:

  • Achieved a 60x speed-up in AFM navigation compared to traditional methods.
  • Significantly reduced the time spent searching for specific cell shapes in large samples.
  • Demonstrated successful AI-guided intelligent automation for bio-AFM applications.

Conclusions:

  • The implemented DL framework enables efficient, automated AFM navigation and biomechanical mapping of live cells.
  • This AI-driven approach accelerates bio-AFM applications by optimizing sample selection and measurement speed.
  • The study highlights the potential of AI for intelligent automation in advanced microscopy techniques.