[Home ] [Archive]    
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Contact us::
Site Facilities::
Publishing Ethics Issues::
Executive Board Identity::
Indexing Info::
Journal / statistics::
Social Networks::
RSS: Volume 12 - Issue 1
Search in website

Advanced Search
Receive site information
Enter your Email in the following box to receive the site news and information.
Journal DOI


:: Volume 12, Issue 2 ( September- 2023) ::
Caspian J Dent Res 2023, 12(2): 70-81 Back to browse issues page
Fully automated orthodontic photograph analysis by machine learning
Mahdi Soleiman Mezerji , Sedigheh Sheikhzadeh * , Maysam Mirzaie , Hemmat Gholinia
,Oral Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran. , elfsh@yahoo.com
Abstract:   (128 Views)
Introduction: The craniofacial anthropometric ratios are very useful in sciences such as dentistry, maxillofacial surgery, developmental studies and plastic surgery. The manual method of analyzing facial photographs requires a lot of time and precision. The aim of this study was to introduce an application tool that fully automates the analysis of facial photographs and compare it with the manual method.
Materials & Methods: In this cross-sectional study, the database consisted of 395 profile photographs, 271 frontal photographs in smile and 346 frontal photographs at rest. A two-stage fully convolutional network architecture was used for landmark detection. Two methods of manual and automatic analysis were compared in the measurement of 8 variables, including buccal corridor space, ratio of the height of the middle to the lower third of the face, total facial convexity angle, facial convexity angle, nasofacial angle, mentolabial angle, and nasofrontal angle. The agreement between the two methods was evaluated using the paired T-test and intraclass correlation coefficient (ICC). A value of p<0.05 was considered significant.
Results: For total facial convexity (P=0.005), nasofacial (P=0.001), and nasolabial (p=0.02) angles, the difference between the two methods was significant. However, no significant difference was found between the two methods for facial convexity, mentolabial, nasofrontal, buccal corridor space, and the ratio of the height of the middle to the lower third of the face no significant difference was observed between the two methods. The ICC for all variables was found to be greater than 0.69 except for the nasolabial angle. For most of the measured variables, the accuracy of the automatic method was similar to that of the manual method.
Conclusion: Machine learning has the potential to be used in clinical soft tissue analysis. It offers the ability to perform reliable and repeatable analyses on large image datasets.

Keywords: Orthodontics, Face, Photography, Machine Learning
Full-Text [PDF 1028 kb]   (43 Downloads)    
Type of Study: Research Paper | Subject: Orthodontics
* Corresponding Author Address: Department of Orthodontics, Faculty of Dentistry, Babol University of Medical Sciences, Babol, Iran.
Send email to the article author

Add your comments about this article
Your username or Email:


XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Soleiman Mezerji M, Sheikhzadeh S, Mirzaie M, Gholinia H. Fully automated orthodontic photograph analysis by machine learning. Caspian J Dent Res 2023; 12 (2) :70-81
URL: http://cjdr.ir/article-1-399-en.html

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 12, Issue 2 ( September- 2023) Back to browse issues page
مجله تحقیقات دندانپزشکی کاسپین Caspian Journal of Dental Research
Persian site map - English site map - Created in 0.06 seconds with 42 queries by YEKTAWEB 4612