Using Machine Learning Techniques for Diagnosing and Analysing Medication-Related Osteonecrosis of the Jaw (MRONJ)

Abstract This research examines the use of streamlined multimodal machine learning (ML) models to predict and assess key factors contributing to medication-related osteonecrosis of the jaw (MRONJ), particularly in patients undergoing treatment for osteoporosis or cancer. Our approach aims at harnessing these ML models to enable early diagnosis and precise identification of significant risk factors, creating a foundation for real-time, proactive strategies that could mitigate the onset and progression of MRONJ. Through detailed analysis and validation, we propose an innovative framework that integrates ML insights with clinical practices, ultimately enhancing patient care and quality of life by steering clear of potential complications associated with MRONJ.