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Related Concept Videos

Stem Cell Culture01:17

Stem Cell Culture

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Stem cell research aims to find ways to use stem cells to regenerate and repair cellular damage. Over time, most adult cells undergo the wear and tear of aging and lose their ability to divide and repair themselves. Stem cells do not display a particular morphology or function. Adult stem cells, which exist as a small subset of cells in most tissues, keep dividing and can differentiate into a number of specialized cells generally formed by that tissue. These cells enable the body to renew and...
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AI-Driven Quality Monitoring and Control in Stem Cell Cultures: A Comprehensive Review.

Rohan Singh1, Hamid Ebrahimi Orimi2, Praveen Kumar Raju Pedabaliyarasimhuni2

  • 1Department of Chemical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada.

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|August 11, 2025
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Artificial intelligence (AI) offers real-time quality control for stem cell cultures, moving beyond traditional methods. This approach enhances the automated, scalable production of stem cells for regenerative medicine.

Keywords:
artificial intelligencepredictive modelingquality monitoringreal‐time feedbackstem cell culture

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Area of Science:

  • Regenerative Medicine
  • Biotechnology
  • Artificial Intelligence

Background:

  • Stem cell research offers promising cell therapy applications.
  • Current quality monitoring methods are labor-intensive and lack real-time process insights.
  • Scalable stem cell production requires advanced quality control.

Purpose of the Study:

  • To explore artificial intelligence (AI)-driven approaches for real-time quality control in stem cell cultures.
  • To integrate machine vision, predictive modeling, and sensor-based monitoring for dynamic quality tracking.
  • To highlight AI's potential in enhancing scalability, reproducibility, and automation in stem cell biomanufacturing.

Main Methods:

  • AI models analyze high-resolution imaging and multi-sensor data.
  • Tracking of critical quality attributes (CQAs) including cell morphology, proliferation, differentiation, and environmental stability.
  • Integration of real-time feedback systems and multi-omics data.

Main Results:

  • AI enables automated anomaly detection and differentiation tracking.
  • Dynamic monitoring of cell morphology, proliferation, and differentiation potential.
  • Real-time tracking of environmental parameters (pH, oxygen, nutrients) and genetic integrity.

Conclusions:

  • AI-driven quality monitoring significantly improves scalability and reproducibility in stem cell biomanufacturing.
  • Automated systems enhance process control, leading to better clinical translation and regulatory compliance.
  • Future directions involve further integration of AI for fully automated stem cell production.