An AI-Driven Framework for Detecting Bone Fractures in Orthopedic Therapy

Abstract

This study presents an advanced artificial intelligence-driven framework designed to enhance the speed and accuracy of bone fracture detection, addressing key limitations in traditional diagnostic approaches that rely on manual image analysis. The proposed framework integrates the YOLOv8 object detection model with a ResNet backbone to combine robust feature extraction and precise fracture classification. This combination effectively identifies and categorizes bone fractures within X-ray images, supporting reliable diagnostic outcomes. Evaluated on an extensive data set, the model demonstrated a mean average precision of 0.9 and overall classification accuracy of 90.5%, indicating substantial improvements over conventional methods. These results underscore a potential framework to provide healthcare professionals with a powerful, automated tool for orthopedic diagnostics, enhancing diagnostic efficiency and accuracy in routine and emergency care settings. The study contributes to the field by offering an effective solution for automated fracture detection that aims to improve patient outcomes through timely and accurate intervention.

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