Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150012
Title: Machine learning in the field of dentistry
Authors: Subburaju, Preethi
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Subburaju, P. (2021). Machine learning in the field of dentistry. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150012
Project: B3185-201
Abstract: The rapid technological advances in machine learning and AI has led to the point where it can be applied to real-life problems across all sectors of society. The diagnostic accuracy of machine/deep learning algorithms in the medical field is approaching levels of human expertise, changing the role of computer-assisted diagnosis from a ‘second-opinion’ tool to a more collaborative one. The development of AI applications in the dental field is also remarkable and the area of exploration in dental will be oral and maxillofacial radiology and orthodontics. Dental braces is a very common diagnosis all around the world. Dental braces are being diagnosed for patients of all ages from young children to even middle aged adults. There are many symptoms or causes for the diagnosis of braces. Some of which are crooked teeth, overcrowding of teeth, missing teeth etc. However, usually the diagnosis process for dental braces may not always be quick. Usually, dental radiograph images of patient’s are taken for further clinical examination before diagnosis. This can be time-consuming. Therefore, to speed up this process this project will discuss the machine learning techniques, specifically Convolutional Neural Networks (CNN), that can be applied in speeding up the clinical examination aspect of the diagnosis.
URI: https://hdl.handle.net/10356/150012
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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The rapid technological advances in machine learning and AI has led to the point where it can be applied to real-life problems across all sectors of society. The diagnostic accuracy of machine/deep learning algorithms in the medical field is approaching levels of human expertise, changing the role of computer-assisted diagnosis from a ‘second-opinion’ tool to a more collaborative one. The development of AI applications in the dental field is also remarkable and the area of exploration in dental will be oral and maxillofacial radiology and orthodontics. Dental braces is a very common diagnosis all around the world. Dental braces are being diagnosed for patients of all ages from young children to even middle aged adults. There are many symptoms or causes for the diagnosis of braces. Some of which are crooked teeth, overcrowding of teeth, missing teeth etc. However, usually the diagnosis process for dental braces may not always be quick. Usually, dental radiograph images of patient’s are taken for further clinical examination before diagnosis. This can be time-consuming. Therefore, to speed up this process this project will discuss the machine learning techniques, specifically Convolutional Neural Networks (CNN), that can be applied in speeding up the clinical examination aspect of the diagnosis.1.91 MBAdobe PDFView/Open

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