Machine Learning and AI-Driven Biomechanics in Orthopedic Surgery

Rapid advances in artificial intelligence, machine learning, and data-driven computational modeling have opened unprecedented opportunities to transform diagnostic accuracy, clinical decision-making, and outcome prediction across the field of orthopedic surgery. The musculoskeletal system exhibits highly complex biomechanical behavior, nonlinear tissue responses, and substantial inter-patient variability, all of which challenge the effectiveness of conventional clinical assessment methods. In recent years, the integration of multimodal datasets—including advanced medical imaging, 3D gait analysis, kinematic and kinetic signals, wearable sensor outputs, finite-element and musculoskeletal simulations, and electronic health records—with modern machine learning techniques has enabled the extraction of hidden patterns, identification of objective biomarkers, and development of precise, patient-specific predictive models.This Research Topic aims to gather cutting-edge and innovative contributions showcasing the application of artificial intelligence and biomechanical modeling across diverse domains of orthopedic care. Submissions may span a wide variety of themes, such as deep learning for injury detection, predictive algorithms for surgical outcomes and rehabilitation trajectories, automated movement and gait analysis, early risk stratification using data-mining techniques, multimodal data fusion for improved diagnostic accuracy, and explainable AI frameworks designed to enhance clinical interpretability and trust. Research that contributes to the development of clinical decision-support tools, optimizes treatment planning, or advances personalized approaches to orthopedic surgery is particularly encouraged.Relevant applications may include, but are not limited to:- upper and lower limb disorders, - spine trauma, - reconstructive surgery, - movement impairments, - sports-related injuries, or rehabilitation,- studies that integrate 3D biomechanical modeling, finite-element analysis, imaging-based diagnostics, physiological signal processing, or kinematic–kinetic datasets with AI-based analytical approaches will be of special interest.The overarching goal of this collection is to establish a multidisciplinary platform that brings together orthopedic surgeons, biomechanists, data scientists, and AI researchers to accelerate the translation of computational innovations into clinical practice and to support the development of more accurate diagnostic tools, improved surgical planning strategies, and enhanced patient-specific evaluation methods in orthopedic surgery.We welcome a broad range of article types, including original research, systematic reviews, technical reports, clinical studies, computational modeling papers, publicly available datasets, and methodological contributions.Submissions to Frontiers in Bioengineering and Biotechnology that use machine learning without demonstrating a genuine step-change in biomechanical understanding, or that present modelling without experimental validation, will not be considered; any manuscript submitted to this journal must have a substantial biomechanical component at its core. Topic editors: Elaheh Ziaei, Mohammad Razi Source: https://www.frontiersin.org/research-topics/76925/machine-learning-and-ai-driven-biomechanics-in-orthopedic-surgery