Graph-Augmented Multi-Modal Deep Learning Framework for Automated Bone Fracture Detection and Reporting

Abstract

Accurate and efficient bone fracture diagnosis is essential for timely medical intervention, yet conventional manual interpretation of medical images is time-consuming, prone to variability, and dependent on radiologist expertise. To address these challenges, this paper proposes a Graph-Augmented Multi-Modal Deep Learning Framework for Fracture Detection, leveraging the strengths of convolutional and graph-based learning techniques to enhance fracture identification and classification. The proposed model integrates multi-modal medical imaging data (X-rays, CT scans), improving its adaptability across different imaging techniques. Convolutional Neural Networks (CNNs) are employed for feature extraction, while Graph Neural Networks (GNNs) model spatial and structural relationships within bone fractures, enabling precise localization and classification, particularly in cases of overlapping, comminuted, and subtle fractures. Additionally, explainable AI (XAI) techniques, such as Grad-CAM and saliency maps, are incorporated to enhance interpretability, providing radiologists with a transparent understanding of AI-driven diagnoses. To streamline clinical workflows, the system generates structured diagnostic reports, detailing fracture type, severity, and localization, ensuring consistency and reducing reporting time. The proposed framework is rigorously evaluated on multi-modal and real-world datasets, demonstrating its effectiveness in improving diagnostic accuracy, reducing human error, and enhancing clinical decision-making. By bridging the gap between AI-driven automation and radiological expertise, this research contributes to the advancement of intelligent medical imaging systems, making fracture diagnosis more efficient, accurate, and accessible in diverse healthcare settings.