Machine Learning Approaches for Integrating Clinical and Radiographic Data in the Early Detection of Osteonecrosis of the Jaw

Machine Learning Approaches for Integrating Clinical Data in Osteonecrosis of the Jaw

Abstract

Osteonecrosis of the jaw (ONJ) is a severe bone condition characterized by the progressive destruction of the jawbone, often associated with the long-term use of antiresorptive medications such as bisphosphonates. Early detection of ONJ remains a significant clinical challenge due to the subtle onset of symptoms and limitations in current diagnostic methods, which rely on clinical assessment and radiographic imaging. Conventional techniques, such as panoramic X-rays, computed tomography (CT), and magnetic resonance imaging (MRI), often fail to detect early-stage ONJ, delaying diagnosis until more advanced stages. Machine learning (ML) has emerged as a powerful tool for improving the early detection and diagnosis of ONJ by integrating clinical and radiographic data. ML algorithms, including supervised learning methods like random forests, support vector machines, and deep learning models such as convolutional neural networks, are particularly suited for analyzing complex datasets and identifying patterns that are undetectable by traditional methods. These models can enhance the sensitivity and specificity of ONJ detection, potentially leading to earlier interventions and improved patient outcomes. This paper reviews the current state of ML applications in ONJ detection, emphasizing the integration of clinical and radiographic data. It discusses various ML approaches, their potential to improve diagnostic accuracy, and the challenges involved in data integration. Also, this review highlights future directions of ML as a diagnostic tool that has the potential to revolutionize ONJ detection, offering a path toward earlier, more accurate diagnosis and better patient care.