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Large-Scale, Automated Production of Adipose-Derived Stem Cell Spheroids for 3D Bioprinting

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Published on: March 31, 2022

AI-Driven Innovations for Quality Control and Standardization: Future Strategies in Adipose-Derived Stem Cell

Riccardo Foti¹, Gabriele Storti¹, Marco Palmesano^1,2

¹Plastic and Reconstructive Surgery, Department of Surgical Sciences, Tor Vergata University, 00133 Rome, Italy.

International Journal of Molecular Sciences

|March 14, 2026

Summary

Artificial intelligence (AI) enhances adipose-derived stem/stromal cell (ADSC) manufacturing by addressing variability and improving quality control. AI tools optimize cell culture and potency assessment, accelerating regenerative medicine development.

Keywords:

adipose-derived stem/stromal cells (ADSCs)artificial intelligence deep learning machine learning

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Area of Science:

Regenerative Medicine
Stem Cell Biology
Computational Biology

Background:

Adipose-derived stem/stromal cells (ADSCs) show therapeutic promise but face manufacturing challenges.
Donor variability, cell heterogeneity, and lack of standardization hinder ADSC clinical translation.
Robust quality control (QC) and potency assays are critical for Good Manufacturing Practice (GMP) compliance.

Purpose of the Study:

To review the application of artificial intelligence (AI) in the ADSC manufacturing pipeline.
To highlight AI-driven methods for improving ADSC production, quality, and clinical readiness.
To discuss challenges and future directions for AI in ADSC-based regenerative medicine.

Main Methods:

Review of AI applications including machine learning (ML) and deep learning (DL).
Analysis of computer vision and label-free imaging for cell monitoring (morphology, proliferation, senescence, contamination).
Integration of multi-omics data (transcriptomics, proteomics, metabolomics, secretomics) with ML for potency prediction and biomarker discovery.

Main Results:

AI optimizes ADSC pre-screening, isolation, expansion, differentiation, and batch release.
AI-powered imaging enhances monitoring of cell characteristics and culture conditions.
ML models integrating multi-omics data predict functional potency and identify therapeutic efficacy biomarkers.

Conclusions:

AI provides a powerful toolkit to enhance ADSC manufacturing reproducibility, safety, and scalability.
AI facilitates the development of standardized, data-driven regenerative medicine products.
Future AI integration includes closed-loop bioprocess control, foundation models, and federated learning.