Predictive Analytics and Neural Networks in Assessing Endodontic Treatment Prognosis: A New Frontier in Digital Dentistry

Abstract

The advent of digital dentistry has transformed endodontic practice, offering innovative tools to enhance treatment planning and prognosis assessment. Predictive analytics and neural networks have emerged as powerful technologies capable of analyzing complex patient and clinical data to forecast the outcomes of endodontic treatments. By leveraging machine learning algorithms, neural networks can interpret radiographic images, detect subtle pathological changes, and provide data-driven predictions of treatment success. Integrating predictive analytics with neural network models enables clinicians to make more informed decisions, reduce treatment failures, and personalize patient care. Despite the promising potential, challenges such as data quality, algorithm transparency, and clinical adoption remain. Continued research and development in this domain are essential to fully realize the transformative impact of AI-driven predictive tools in endodontics, paving the way for more precise, efficient, and patient-centered care.