Artificial Intelligence–Augmented Optimization of Cannulated Screw Configurations for Pauwels Type III Femoral Neck Fractures: A Critical Review of Biomechanical and Computational Approaches

Abstract

Around 50% of the world's hip fractures are femoral neck fractures, which are projected to increase by 30% by 2025. Among these fractures, the Pauwels type III fractures are most unstable due to their steep fracture angles, resulting in high shear and rotational stresses. Despite the fact that cannulated screw fixation remains the most cost-effective and least invasive method of treatment, high rates of fixation failure in vertically oriented fractures are cause for concern. This review synthesized 2017-2025 experimental, finite element, and computational studies on cannulated screw configurations and their biomechanical performance, and the preliminary orthopaedic utilization of Artificial Intelligence (AI) and Machine Learning (ML). Among the evidence supporting advanced configurations, the oblique triangular configuration arguably provides the best resistance to shear and most favorable stress cvergence over the others. Robotic fixation systems, TiRobot and TianJi, as well as screw design and spacing, are not fully optimized to enhance the fixation relative to patient anatomy. The combination of AI and ML with finite element modeling will lead to the first real-time fixation design that personalize data on screw axis and dimensions. This will position orthopaedic surgery away from empirical methods and clinically adopt biomechanical fixation methods, resulting in smart fixation and advanced orthopedic surgery.