Title
Deep learning-based automated detection of microcracks in monolithic zirconia crowns using Micro-CT imaging: An in vitro study
Authors
Gurdeep Kaur Chauhan1, Ravi Ranjan Sinha1,*, Rohit Mehta2, Upasana Chhabra2, Shailesh D Gawande3 & Pooja Arora4
Affiliation
1Department of Prosthodontics and Crown & Bridge, the Oxford Dental College, Bangalore, India; 2Department of Prosthodontics Crown and Bridge, Geetanjali Dental & Research institute, Udaipur, Rajasthan, India; 3Department of Dentistry, Government Medical College and Hospital, Ratnagiri, India; 4Department of Prosthodontics, Taif University, Saudi Arabia; *Corresponding author
Ravi Ranjan Sinha - E-mail: r.ravi.sinha@gmail.com
Gurdeep Kaur Chauhan - E-mail: dr.gurdeep.k.chauhan@gmail.com
Rohit Mehta - E-mail: docrohit07@gmail.com
Upasana Chhabra - E-mail: chhabraupasana@gmail.com
Shailesh D Gawande - E-mail: drsdg999@gmail.com
Pooja Arora - E-mail: drpoojaprosthodontist@gmail.com
Article Type
Research Article
Date
Received April 1, 2026; Revised April 30, 2026; Accepted April 30, 2026, Published April 30, 2026
Abstract
Early detection of microcracks 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 microcracks 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 microcrack detection and classification. Deep learning combined with micro-CT imaging provides a highly accurate and automated approach for identifying microcracks in zirconia crowns, with potential to enhance quality assurance in dental manufacturing workflows.
Keywords
Deep learning (DL), microcrack detection, monolithic zirconia, micro CT, convolutional neural network (CNN)
Citation
Chauhan et al. Bioinformation 22(4): 2694-2700 (2026)
Edited by
Hiroj Bagde
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
