A Critical Review on the 3D Cephalometric Analysis Using Machine Learning
Machine learning applications have momentously enhanced the quality of human life. The past few decades have seen the progression and application of machine learning in diverse medical fields. With the rapid advancement in technology, machine learning has secured prominence in the prediction and cla...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/11/11/154 |
| _version_ | 1797468645643780096 |
|---|---|
| author | Shtwai Alsubai |
| author_facet | Shtwai Alsubai |
| author_sort | Shtwai Alsubai |
| collection | DOAJ |
| description | Machine learning applications have momentously enhanced the quality of human life. The past few decades have seen the progression and application of machine learning in diverse medical fields. With the rapid advancement in technology, machine learning has secured prominence in the prediction and classification of diseases through medical images. This technological expansion in medical imaging has enabled the automated recognition of anatomical landmarks in radiographs. In this context, it is decisive that machine learning is capable of supporting clinical decision support systems with image processing and whose scope is found in the cephalometric analysis. Though the application of machine learning has been seen in dentistry and medicine, its progression in orthodontics has grown slowly despite promising outcomes. Therefore, the present study has performed a critical review of recent studies that have focused on the application of machine learning in 3D cephalometric analysis consisting of landmark identification, decision making, and diagnosis. The study also focused on the reliability and accuracy of existing methods that have employed machine learning in 3D cephalometry. In addition, the study also contributed by outlining the integration of deep learning approaches in cephalometric analysis. Finally, the applications and challenges faced are briefly explained in the review. The final section of the study comprises a critical analysis from which the most recent scope will be comprehended. |
| first_indexed | 2024-03-09T19:10:20Z |
| format | Article |
| id | doaj.art-6ae1ab0be7a04ee9ba1ef17abf987d1d |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-09T19:10:20Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-6ae1ab0be7a04ee9ba1ef17abf987d1d2023-11-24T04:14:04ZengMDPI AGComputers2073-431X2022-10-01111115410.3390/computers11110154A Critical Review on the 3D Cephalometric Analysis Using Machine LearningShtwai Alsubai0College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaMachine learning applications have momentously enhanced the quality of human life. The past few decades have seen the progression and application of machine learning in diverse medical fields. With the rapid advancement in technology, machine learning has secured prominence in the prediction and classification of diseases through medical images. This technological expansion in medical imaging has enabled the automated recognition of anatomical landmarks in radiographs. In this context, it is decisive that machine learning is capable of supporting clinical decision support systems with image processing and whose scope is found in the cephalometric analysis. Though the application of machine learning has been seen in dentistry and medicine, its progression in orthodontics has grown slowly despite promising outcomes. Therefore, the present study has performed a critical review of recent studies that have focused on the application of machine learning in 3D cephalometric analysis consisting of landmark identification, decision making, and diagnosis. The study also focused on the reliability and accuracy of existing methods that have employed machine learning in 3D cephalometry. In addition, the study also contributed by outlining the integration of deep learning approaches in cephalometric analysis. Finally, the applications and challenges faced are briefly explained in the review. The final section of the study comprises a critical analysis from which the most recent scope will be comprehended.https://www.mdpi.com/2073-431X/11/11/154machine learning3D cephalometric analysislandmark identificationorthodontics |
| spellingShingle | Shtwai Alsubai A Critical Review on the 3D Cephalometric Analysis Using Machine Learning Computers machine learning 3D cephalometric analysis landmark identification orthodontics |
| title | A Critical Review on the 3D Cephalometric Analysis Using Machine Learning |
| title_full | A Critical Review on the 3D Cephalometric Analysis Using Machine Learning |
| title_fullStr | A Critical Review on the 3D Cephalometric Analysis Using Machine Learning |
| title_full_unstemmed | A Critical Review on the 3D Cephalometric Analysis Using Machine Learning |
| title_short | A Critical Review on the 3D Cephalometric Analysis Using Machine Learning |
| title_sort | critical review on the 3d cephalometric analysis using machine learning |
| topic | machine learning 3D cephalometric analysis landmark identification orthodontics |
| url | https://www.mdpi.com/2073-431X/11/11/154 |
| work_keys_str_mv | AT shtwaialsubai acriticalreviewonthe3dcephalometricanalysisusingmachinelearning AT shtwaialsubai criticalreviewonthe3dcephalometricanalysisusingmachinelearning |