ArticleProceedings of SPIE--the International Society for Optical Engineering2018
Automatic quantification framework to detect cracks in teeth.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Comparison of Conventional Root Tip Resection with Digitally Guided Resection-An In Vitro Study.Dentistry journal · 2025Article
- Artificial Intelligence in Dentistry: A Descriptive Review.Bioengineering (Basel, Switzerland) · 2024Review
- Artificial intelligence application in endodontics: A narrative review.Imaging science in dentistry · 2024Review
- Uncertainty-based Active Learning by Bayesian U-Net for Multi-label Cone-beam CT Segmentation.Journal of endodontics · 2024Article
- Impact of Artificial Intelligence in Endodontics: Precision, Predictions, and Prospects.Journal of medical signals and sensors · 2024Review
- Review
- Three Visual-Diagnostic Methods for the Detection of Enamel Cracks: An In Vitro Study.Journal of clinical medicine · 2023Article
- Machine Learning in Dentistry: A Scoping Review.Journal of clinical medicine · 2023Article
- Analysis of Advances in Research Trends in Robotic and Digital Dentistry: An Original Research.Journal of pharmacy & bioallied sciences · 2022Article
- Review
- Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls.Diagnostics (Basel, Switzerland) · 2022Review
- Dental microfracture detection using wavelet features and machine learning.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Studies show that cracked teeth are the third most common cause for tooth loss in industrialized countries. If detected early and accurately, patients can retain their teeth for a longer time. Most cracks are not detected early because of the discontinuous symptoms and lack of good diagnostic tools. Currently used imaging modalities like Cone Beam Computed Tomography (CBCT) and intraoral radiography often have low sensitivity and do not show cracks clearly. This paper introduces a novel method that can detect, quantify, and localize cracks automatically in high resolution CBCT (hr-CBCT) scans of teeth using steerable wavelets and learning methods. These initial results were created using hr-CBCT scans of a set of healthy teeth and of teeth with simulated longitudinal cracks. The cracks were simulated using multiple orientations. The crack detection was trained on the most significant wavelet coefficients at each scale using a bagged classifier of Support Vector Machines. Our results show high discriminative specificity and sensitivity of this method. The framework aims to be automatic, reproducible, and open-source. Future work will focus on the clinical validation of the proposed techniques on different types of cracks ex-vivo. We believe that this work will ultimately lead to improved tracking and detection of cracks allowing for longer lasting healthy teeth.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.