Tendon Regeneration Deserves Better: Focused Review on In vivo Models, Artificial Intelligence and 3D Bioprinting Approaches

DAMLA AYKORA^1*Burak TAŞCI²Muhammed Zahid Şahin³İbrahim Tekeoğlu⁴Metehan Uzun¹Victoria Sarafian^5,6Denitsa Docheva⁷

• ¹Faculty of Medicine, Çanakkale Onsekiz Mart University, Çanakkale, Türkiye
• ²Firat University, Elazig, Elazig, Türkiye
• ³Faculty of Medicine, Sakarya University, Sakarya, Sakarya, Türkiye
• ⁴Kutahya Health Sciences University, Kutahya, Kütahya, Türkiye
• ⁵Department of Medical Biology, Faculty of Medicine, Plovdiv Medical University, Plovdiv, Plovdiv, Bulgaria
• ⁶Research Institute, Plovdiv Medical University, Plovdiv, Plovdiv, Bulgaria
• ⁷Musculoskeletal Center, Faculty of Medicine, University of Würzburg, Wuerzburg, Bavaria, Germany

Tendon regeneration has been one of the most challenging issues in orthopedics. Despite various surgical techniques and rehabilitation methods, tendon tears or ruptures cannot wholly regenerate and gain the load-bearing capacity the tendon tissue had before the injury. The enhancement of tendon regeneration mostly requires grafting or an artificial tendon-like tissue to replace the damaged tendon. Tendon tissue engineering offers promising regenerative effects with numerous techniques in the additive manufacturing context. 3D bioprinting is a widely used additive manufacturing method to produce tendon-like artificial tissues based on biocompatible substitutes. There are multiple techniques and bio-inks for fabricating innovative scaffolds for tendon applications. Nevertheless, there are still many drawbacks to overcome for the successful regeneration of injured tendon tissue. The most important target is to catch the highest similarity to the tissue requirements such as anisotropy, porosity, viscoelasticity, mechanical strength, and cell-compatible constructs. To achieve the best-designed artificial tendon-like structure, novel AI-based systems in the field of 3D bioprinting may unveil excellent final products to re-establish tendon integrity and functionality. AI-driven optimization can enhance bio-ink selection, scaffold architecture, and printing parameters, ensuring better alignment with the biomechanical properties of native tendons. Furthermore, AI algorithms facilitate real-time process monitoring and adaptive adjustments, improving reproducibility and precision in scaffold fabrication. Thus, in vitro biocompatibility and in vivo application-based experimental processes will make it possible to accelerate tendon healing and reach the required mechanical strength. Integrating AI-based predictive modeling can further refine these experimental processes to evaluate scaffold performance, cell viability, and mechanical durability, ultimately improving translation into clinical applications. Here in this review, 3D bioprinting approaches and AI-based technology incorporation were given in addition to in vivo models.