Machine learning in the field of dentistry
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 o...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/150012 |
| _version_ | 1826111884986679296 |
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| author | Subburaju, Preethi |
| author2 | Muhammad Faeyz Karim |
| author_facet | Muhammad Faeyz Karim Subburaju, Preethi |
| author_sort | Subburaju, Preethi |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T02:58:10Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/150012 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:58:10Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1500122023-07-07T18:30:39Z Machine learning in the field of dentistry Subburaju, Preethi Muhammad Faeyz Karim School of Electrical and Electronic Engineering faeyz@ntu.edu.sg Engineering::Electrical and electronic engineering 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. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-06-10T06:56:27Z 2021-06-10T06:56:27Z 2021 Final Year Project (FYP) Subburaju, P. (2021). Machine learning in the field of dentistry. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150012 https://hdl.handle.net/10356/150012 en B3185-201 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Subburaju, Preethi Machine learning in the field of dentistry |
| title | Machine learning in the field of dentistry |
| title_full | Machine learning in the field of dentistry |
| title_fullStr | Machine learning in the field of dentistry |
| title_full_unstemmed | Machine learning in the field of dentistry |
| title_short | Machine learning in the field of dentistry |
| title_sort | machine learning in the field of dentistry |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/150012 |
| work_keys_str_mv | AT subburajupreethi machinelearninginthefieldofdentistry |