Application of 3D neural networks and explainable AI to classify ICDAS detection system on mandibular molars

STATEMENT OF PROBLEM Considerable variations exist in cavity preparation methods and approaches. Whether the extent and depth of cavity preparation because of the extent of caries affects the overall accuracy of training deep learning models remains unexplored. PURPOSE The purpose of this study was to investigate the difference in 3-dimensionsal (3D) model cavity preparations after International Caries Detection and Assessment System (ICDAS) classification performed by different practitioners and the subsequent influence on the ability of a deep learning model to predict cavity classification. MATERIAL AND METHODS Two operators prepared 56 restorative cavities on simulated mandibular first molars according to 4 ICDAS classifications, followed by 3D scanning and computer-aided design processing. The surface area, virtual volume, Hausdorff distance (HD), and Dice…