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Related Experiment Video

Updated: Jul 12, 2026

Distinctive Capillary Action by Micro-channels in Bone-like Templates can Enhance Recruitment of Cells for Restoration of Large Bony Defect
09:35

Distinctive Capillary Action by Micro-channels in Bone-like Templates can Enhance Recruitment of Cells for Restoration of Large Bony Defect

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Reproducibility and Validation Challenges in AI-Driven Scaffold Design for Bone Regeneration.

Mohammadamin Damsaz1,2, Farnaz Heidari Laybidi3, Zohreh Moradipour4

  • 1Research Director, Facial Plastic Surgery Research Program, iFACE Academy, Toronto, Canada.

Tissue Engineering. Part B, Reviews
|July 10, 2026
PubMed
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Artificial intelligence (AI) aids tissue engineering (TE) for bone regeneration but faces challenges. A new framework improves AI-TE validation and reporting for better clinical translation.

Area of Science:

  • Biomaterials Science
  • Tissue Engineering
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is increasingly used in bioink formulation and bioprinting for tissue engineering (TE).
  • Translational progress in AI-driven TE is hindered by small datasets, limited validation, and poor links between computational predictions and biological results.
  • Existing reporting standards lack TE-specific requirements for validation and translational readiness.

Purpose of the Study:

  • To critically evaluate experimentally validated AI applications in scaffold-based bone regeneration.
  • To identify methodological gaps and propose solutions for improving AI-TE reliability and translational relevance.
  • To introduce the AI-TE reporting framework for enhanced validation and documentation.

Main Methods:

Keywords:
artificial intelligencebiomaterialsbioprintingreproducibilityscaffold architecturetissue engineering

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Repair of a Critical-sized Calvarial Defect Model Using Adipose-derived Stromal Cells Harvested from Lipoaspirate
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Repair of a Critical-sized Calvarial Defect Model Using Adipose-derived Stromal Cells Harvested from Lipoaspirate

Published on: October 31, 2012

Related Experiment Videos

Last Updated: Jul 12, 2026

Distinctive Capillary Action by Micro-channels in Bone-like Templates can Enhance Recruitment of Cells for Restoration of Large Bony Defect
09:35

Distinctive Capillary Action by Micro-channels in Bone-like Templates can Enhance Recruitment of Cells for Restoration of Large Bony Defect

Published on: September 11, 2015

Multimodal Approach to Assess Bone Regeneration and Scaffold Performance
06:54

Multimodal Approach to Assess Bone Regeneration and Scaffold Performance

Published on: February 13, 2026

Repair of a Critical-sized Calvarial Defect Model Using Adipose-derived Stromal Cells Harvested from Lipoaspirate
11:31

Repair of a Critical-sized Calvarial Defect Model Using Adipose-derived Stromal Cells Harvested from Lipoaspirate

Published on: October 31, 2012

  • Review of experimentally validated AI applications in bone regeneration TE.
  • Analysis of AI approaches including physics-informed models and transfer learning.
  • Development of a reporting framework addressing TE-specific needs.

Main Results:

  • Physics-informed AI models show greater robustness and generalizability than purely data-driven methods.
  • Transfer learning in AI-TE is challenging due to variability in cellular responses and fabrication.
  • The proposed AI-TE framework incorporates essential elements for biological validation, fabrication documentation, and translational readiness.

Conclusions:

  • AI holds significant promise for bone regeneration TE, but methodological rigor and validation are crucial.
  • Addressing current gaps in reporting and validation is essential for advancing AI-TE applications.
  • The AI-TE reporting framework aims to enhance reproducibility, reliability, and clinical translation of AI in bone regeneration.