Deep Learning Algorithms for Early Recognition of Occlusal Trauma-Induced Periodontal Alterations

Abstract

Occlusal trauma is a contributing factor to periodontal tissue alterations, often presenting with subtle structural and functional changes that are difficult to detect at early stages using conventional diagnostic approaches. Recent advances in deep learning have demonstrated significant potential in enhancing diagnostic accuracy through automated analysis of dental and periodontal data. This study explores the application of deep learning algorithms for the early recognition of periodontal alterations induced by occlusal trauma. By leveraging convolutional neural networks and related architectures, patterns associated with early periodontal stress, bone remodeling, and tissue response can be identified from clinical images and radiographic data. The integration of deep learning–based decision support systems into periodontal diagnostics offers the potential for earlier intervention, improved treatment planning, and reduced progression of periodontal damage. Despite challenges related to data quality, model interpretability, and clinical integration, deep learning represents a promising tool for advancing precision diagnostics in periodontology.