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

Gurdeep Kaur Chauhan, Ravi Ranjan Sinha, Rohit Mehta, Upasana Chhabra, Shailesh D Gawande, Pooja Arora

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Article in Bioinformation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Gurdeep Kaur ChauhanDepartment of Prosthodontics and Crown & Bridge, the Oxford Dental College, Bangalore, India.

Ravi Ranjan SinhaDepartment of Prosthodontics and Crown & Bridge, the Oxford Dental College, Bangalore, India.

Rohit MehtaDepartment of Prosthodontics Crown and Bridge, Geetanjali Dental & Research institute, Udaipur, Rajasthan, India.

Upasana ChhabraDepartment of Prosthodontics Crown and Bridge, Geetanjali Dental & Research institute, Udaipur, Rajasthan, India.

Shailesh D GawandeDepartment of Dentistry, Government Medical College and Hospital, Ratnagiri, India.

Pooja AroraDepartment of Prosthodontics, Taif University, Saudi Arabia.

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6 · The paper itself

Abstract

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.

Indexed as

convolutional neural network (CNN)Deep learning (DL)micro-crack detectionmicro CTmonolithic zirconia

Identifiers

PMID42282366

DOI10.6026/973206300222694

PMCPMC13252228

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