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A modular microscopic smartphone attachment for imaging and quantification of multiple fluorescent probes using

Muhammad A Sami1, Muhammad Tayyab1, Priya Parikh1

  • 1Department of Electrical and Computer Engineering, School of Engineering, Rutgers The State University of New Jersey, Piscataway, NJ, USA. umer.hassan@rutgers.edu.

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Summary

A new modular smartphone microscope attachment overcomes limitations of current models, enabling multi-fluorophore imaging and compatibility with various devices. Its performance closely matches benchtop microscopes, with AI accurately counting cells and beads.

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Area of Science:

  • Biomedical Engineering
  • Optical Microscopy
  • Mobile Health Technology

Background:

  • Smartphone-based microscopes offer cost-effective alternatives to benchtop models but have limitations.
  • Existing systems are restricted to single fluorophores, fixed magnification, and specific smartphone models.
  • Usability is often confined to traditional glass slides and cover slips.

Purpose of the Study:

  • To develop a modular smartphone-based microscopic attachment addressing current limitations.
  • To enable multi-fluorophore imaging and variable magnification using interchangeable components.
  • To ensure compatibility with diverse smartphone models and various sample substrates.

Main Methods:

  • Designed a modular attachment with swappable filters and lenses for flexible fluorophore and magnification selection.
  • Tested the attachment with multiple smartphones (Nokia Lumia 1020, Samsung Galaxy S9+, iPhone XS) and sample types (slides, cover slips, microfluidic devices).
  • Quantified resolution using a 1951 USAF target, compared performance to a benchtop microscope using polystyrene beads and blood cells, and developed artificial neural networks (ANNs) for particle counting.

Main Results:

  • Achieved a maximum resolution of 3.9 μm.
  • Demonstrated high correlation (R2=0.99) with benchtop microscope performance for bead and cell imaging.
  • Developed ANNs for particle counting that showed no statistically significant difference compared to ImageJ (p-value=0.97).

Conclusions:

  • The modular smartphone microscope attachment significantly enhances the capabilities of portable microscopy.
  • It offers a versatile, cost-effective solution for multi-fluorophore imaging and analysis across various sample types and devices.
  • The integration of ANNs provides a reliable method for automated particle quantification, comparable to established software.