Machine learning in dental, oral and craniofacial imaging: a review of recent progress
Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning mo...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2021-05-01
|
| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/11451.pdf |
| _version_ | 1827605637474811904 |
|---|---|
| author | Ruiyang Ren Haozhe Luo Chongying Su Yang Yao Wen Liao |
| author_facet | Ruiyang Ren Haozhe Luo Chongying Su Yang Yao Wen Liao |
| author_sort | Ruiyang Ren |
| collection | DOAJ |
| description | Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study. |
| first_indexed | 2024-03-09T06:23:30Z |
| format | Article |
| id | doaj.art-219fa9cd92d24638a15ef933ab830b40 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:23:30Z |
| publishDate | 2021-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-219fa9cd92d24638a15ef933ab830b402023-12-03T11:28:33ZengPeerJ Inc.PeerJ2167-83592021-05-019e1145110.7717/peerj.11451Machine learning in dental, oral and craniofacial imaging: a review of recent progressRuiyang Ren0Haozhe Luo1Chongying Su2Yang Yao3Wen Liao4State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu, Sichuan, ChinaSchool of Computer Science, Sichuan University, Chengdu, Sichuan, ChinaState Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu, Sichuan, ChinaState Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, ChinaState Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, ChinaArtificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.https://peerj.com/articles/11451.pdfOrthodonticsOral cancerMachine learningDental, oral and craniofacial imaging |
| spellingShingle | Ruiyang Ren Haozhe Luo Chongying Su Yang Yao Wen Liao Machine learning in dental, oral and craniofacial imaging: a review of recent progress PeerJ Orthodontics Oral cancer Machine learning Dental, oral and craniofacial imaging |
| title | Machine learning in dental, oral and craniofacial imaging: a review of recent progress |
| title_full | Machine learning in dental, oral and craniofacial imaging: a review of recent progress |
| title_fullStr | Machine learning in dental, oral and craniofacial imaging: a review of recent progress |
| title_full_unstemmed | Machine learning in dental, oral and craniofacial imaging: a review of recent progress |
| title_short | Machine learning in dental, oral and craniofacial imaging: a review of recent progress |
| title_sort | machine learning in dental oral and craniofacial imaging a review of recent progress |
| topic | Orthodontics Oral cancer Machine learning Dental, oral and craniofacial imaging |
| url | https://peerj.com/articles/11451.pdf |
| work_keys_str_mv | AT ruiyangren machinelearningindentaloralandcraniofacialimagingareviewofrecentprogress AT haozheluo machinelearningindentaloralandcraniofacialimagingareviewofrecentprogress AT chongyingsu machinelearningindentaloralandcraniofacialimagingareviewofrecentprogress AT yangyao machinelearningindentaloralandcraniofacialimagingareviewofrecentprogress AT wenliao machinelearningindentaloralandcraniofacialimagingareviewofrecentprogress |