Deep learning-based automated detection of Micro-cracks in monolithic zirconia crowns using Micro-CT imaging: An in...

Early detection of Micro-cracks in monolithic zirconia crowns remains a challenge because conventional inspection methods cannot identify subsurface defects that may lead to clinical failure. Therefore, it is of interest to develop and evaluate a deep learning convolutional neural network model for detecting and classifying Micro-cracks in zirconia crowns using high-resolution micro-computed tomography imaging. Hence, sixty zirconia crowns were fabricated and divided into control and experimentally stressed groups, generating 1,440 labeled micro-CT cross-sectional images that were used to train (80%) and test (20%) a ResNet-50 model. The model achieved an overall accuracy of 94.7%, sensitivity of 93.2%, specificity of 96.1% and an AUC of 0.97 for micro-crack detection and classification. Deep learning combined with micro-CT imaging provides a highly accurate and automated approach for identifying Micro-cracks in zirconia crowns, with potential to enhance quality assurance in dental manufacturing workflows.