Disease activation maps for subgingival dental calculus identification based on intelligent dental optical coherence tomography

During the treatment of periodontitis, removal of dental calculus is essential but still tricky despite developments of several imaging modalities. In this research, we propose a novel approach to provide an intuitive guidance, to automatically detect the present of subgingival calculus, and to identify the site of the lesion in optical coherence tomography images based on convolutional neural network model and the class activation maps technique. Our result shows good visualizations of dental calculus both in B-scan and in volumetric view. We believe the improvement can break down the technical barrier and finally facilitate the prevalence of dental optical coherence tomography.