Artificial Intelligence in Tissue and Organ Biomechanics: From Tissues to Fixation Techniques

Artificial intelligence is reshaping biomechanics through machine learning, deep learning, predictive analytics, and data-driven computational modeling. These approaches are increasingly integrated with finite element analysis, medical imaging, mechanobiology, and digital-twin technologies to investigate tissue and organ mechanics under physiological and pathological conditions. Current applications include predicting disease progression, characterizing tissue properties, optimizing implant and fixation design, supporting treatment planning, and advancing tissue regeneration. However, important challenges remain, including limited and heterogeneous datasets, insufficient integration of biomechanical principles into AI models, difficulties in model interpretability, and the need for rigorous experimental and clinical validation. Further investigation is needed to establish reliable, explainable, and clinically transferable AI approaches for biomechanics.This Research Topic aims to highlight recent advances in the application of artificial intelligence to tissue, organ, musculoskeletal, and implant biomechanics. It will explore how AI can improve the prediction of biomechanical behavior, optimize medical device performance, support patient-specific diagnosis and treatment, and accelerate the translation of computational and experimental findings into clinical practice. Particular emphasis will be placed on interdisciplinary studies that combine AI with finite element modeling, mechanobiology, digital twins, medical imaging, additive manufacturing, and computational simulation. The Research Topic also seeks to address how biomechanical knowledge can be incorporated into AI systems to improve their robustness, interpretability, generalizability, and clinical relevance.To gather further insights into AI-enabled computational, experimental, and clinical biomechanics, this Research Topic welcomes original research articles, reviews, perspectives, and methodological papers addressing, but not limited to, the following themes:AI-driven computational biomechanics and finite element modelingMachine learning for tissue, organ, and musculoskeletal mechanicsPatient-specific biomechanical modeling and digital twinsAI-assisted implant design, optimization, and selectionIntelligent fracture fixation and orthopedic biomechanicsMechanobiology, tissue regeneration, and regenerative medicineCardiovascular, musculoskeletal, dental, craniofacial, and soft-tissue biomechanicsAdditive manufacturing and AI-enabled biomaterial designMedical image-based biomechanical modelingExplainable AI and data-driven decision support in biomedical applicationsAI for rehabilitation biomechanics and surgical planningValidation of AI models using experimental, preclinical, and clinical data Topic editors: Ali Mehboob, Alessandro Borghi, Muhammad Owais Source: https://www.frontiersin.org/research-topics/86929/artificial-intelligence-in-tissue-and-organ-biomechanics-from-tissues-to-fixation-techniques