Journal ArticleDOI
An Artificial Neural Network for Detection of Simulated Dental Caries
Suwadee Kositbowornchai,Sanphet Siriteptawee,Supattra Plermkamon,Sujin Bureerat,Danaipong Chetchotsak +4 more
- Vol. 1, Iss: 2, pp 91-96
TLDR
The study suggests an artificial neural network can be trained to make the correct interpretations of dental caries, and could be a prototype for caries detection but should be improved for classifying caries depth.Abstract:
Objects A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard.read more
Citations
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Journal ArticleDOI
Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study
TL;DR: The designed neural network was able to diagnose and classify teeth with and without VRFs and can be used as a proper model for the diagnosis of VRFs on CBCT images of endodontically treated and intact teeth; in this context,CBCT images are more effective than similar periapical radiographs.
Journal ArticleDOI
Dental caries diagnosis in digital radiographs using back-propagation neural network
TL;DR: This study suggests that dental caries can be predicted more accurately with back-propagation neural network and there is a need for improving the system for classification of caries depth.
Journal ArticleDOI
Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review.
Naseer Ahmed,Maria Shakoor Abbasi,Filza Zuberi,Warisha Qamar,Mohamad Syahrizal Halim,Afsheen Maqsood,Mohammad Khursheed Alam +6 more
TL;DR: In this article, a systematic review was conducted to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry, which revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making.
Journal ArticleDOI
A computer-aided automated methodology for the detection and classification of occlusal caries from photographic color images
Elias D. Berdouses,Georgia Koutsouri,Evanthia E. Tripoliti,George K. Matsopoulos,Constantine J. Oulis,Dimitrios I. Fotiadis +5 more
TL;DR: The proposed methodology provides an objective and fully automated caries diagnostic system for occlusal carious lesions with similar or better performance of a trained dentist taking into consideration the available medical knowledge.
Journal ArticleDOI
Performance of an artificial neural network for vertical root fracture detection: an ex vivo study
TL;DR: The neural network designed in this study has sufficient sensitivity, specificity and accuracy to be a model for vertical root fracture detection.
References
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Book
Neural network design
TL;DR: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules, as well as methods for training them and their applications to practical problems.
Journal ArticleDOI
Radiographic detection of approximal caries: a comparison of dental films and digital imaging systems.
TL;DR: The diagnostic accuracy of digital systems is comparable with that of dental films and the ability of dentists to recognise caries correctly is the main factor contributing to variation in radiographic diagnosis and not the imaging modality.
Journal ArticleDOI
Clinical efficacy of dental radiography in the detection of dental caries and periodontal diseases.
Chester W. Douglass,Richard W. Valachovic,Anila Wijesinha,Howard H. Chauncey,Krishan K. Kapur,Barbara J. McNeil +5 more
TL;DR: The ability of dental radiographs to correctly detect evidence of dental caries and periodontal disease (sensitivity) and to correctly establish the absence of these diseases (specificity) is reported and requires further investigation.
Journal ArticleDOI
Clinical decision support systems: perspectives in dentistry.
TL;DR: The characteristics of clinical decision-support systems, the challenges in developing them, potential barriers for their use in clinical practice, and perspectives for the future are addressed.
Journal ArticleDOI
The use of artificial intelligence to identify people at risk of oral cancer and precancer
TL;DR: A neural network was evaluated to predict the likelihood of an individual having a malignant or potentially malignant oral lesion based on knowledge of their risk habits and may be of value for the identification of individuals with a high risk of oral cancer or precancer for further clinical examination or health education.