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Advancing Hydrogel-Based 3D Cell Culture Systems: Histological Image Analysis and AI-Driven Filament

Lucio Assis Araujo Neto1,2, Alessandra Maia Freire1,3, Luciano Paulino Silva1,2,3

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Summary

Artificial intelligence (AI) using Google's Teachable Machine effectively distinguished between stretched and unstretched hydrogel filaments based on histological images. The AI model showed promise for analyzing biopolymeric hydrogel structures.

Keywords:
Teachable Machineartificial intelligenceconfusion matrixhydrogelmachine learning

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Area of Science:

  • Biomaterials Science
  • Materials Engineering
  • Artificial Intelligence

Background:

  • Machine learning analyzes images by training algorithms to recognize patterns, with broad applications including medicine and automation.
  • Histological cross-sections reveal tissue layers crucial for microscopic analysis.
  • Artificial intelligence (AI) offers potential for interpreting complex biological and material structures.

Purpose of the Study:

  • To utilize Google's Teachable Machine platform for applying AI in interpreting histological cross-section images of hydrogel filaments.
  • To assess the efficacy of AI in differentiating between various states of hydrogel filament structures.

Main Methods:

  • 3D hydrogel filaments were fabricated using varying ratios of sodium alginate and gelatin polymers with a cross-linking agent.
  • Filaments were subjected to stretching until rupture using an extensometer.
  • Cross-sections of stretched and unstretched filaments were prepared, stained (hematoxylin and eosin), and imaged for AI training and prediction via Teachable Machine.

Main Results:

  • Over 600 histological cross-section images were collected and cataloged.
  • AI models successfully differentiated between stretched and unstretched hydrogel filament images.
  • While effective for state differentiation, AI exhibited some limitations in distinguishing subtle variations among different hydrogel formulations.

Conclusions:

  • The AI-powered image prediction tool, implemented via Teachable Machine, proved efficient for classifying hydrogel filaments as stretched or unstretched.
  • This approach demonstrates a viable strategy for the AI-driven analysis of biopolymeric hydrogel histology.