» Articles » PMID: 35284273

Quantitative Analysis of the Mouth Opening Movement of Temporomandibular Joint Disorder Patients According to Disc Position Using Computer Vision: a Pilot Study

Overview
Specialty Radiology
Date 2022 Mar 14
PMID 35284273
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Temporomandibular joint disorder (TMD), which is a broad category encompassing disc displacement, is a common condition with an increasing prevalence. This study aimed to develop an automated movement tracing algorithm for mouth opening and closing videos, and to quantitatively analyze the relationship between the results obtained using this developed system and disc position on magnetic resonance imaging (MRI).

Methods: Mouth opening and closing videos were obtained with a digital camera from 91 subjects, who underwent MRI. Before video acquisition, an 8.0-mm-diameter circular sticker was attached to the center of the subject's upper and lower lips. The automated mouth opening tracing system based on computer vision was developed in two parts: (I) automated landmark detection of the upper and lower lips in acquired videos, and (II) graphical presentation of the tracing results for detected landmarks and an automatically calculated graph height (mouth opening length) and width (sideways values). The graph paths were divided into three types: straight, sideways-skewed, and limited-straight line graphs. All traced results were evaluated according to disc position groups determined using MRI. Graph height and width were compared between groups using analysis of variance (SPSS version 25.0; IBM Corp., Armonk, NY, USA).

Results: Subjects with a normal disc position predominantly (85.72%) showed straight line graphs. The other two types (sideways-skewed or limited-straight line graphs) were found in 85.0% and 89.47% in the anterior disc displacement with reduction group and in the anterior disc displacement without reduction group, respectively, reflecting a statistically significant correlation (χ=38.113, P<0.001). A statistically significant difference in graph height was found between the normal group and the anterior disc displacement without reduction group, 44.90±9.61 and 35.78±10.24 mm, respectively (P<0.05).

Conclusions: The developed mouth opening tracing system was reliable. It presented objective and quantitative information about different trajectories from those associated with a normal disc position in mouth opening and closing movements. This system will be helpful to clinicians when it is difficult to obtain information through MRI.

Citing Articles

Comparative analysis of myoelectric activity and mandibular movement in healthy and nonpainful articular temporomandibular disorder subjects.

Xiaojie X, Yiling C, Honglei L, Jiamei P, Xiaoyong W, Hao Y Clin Oral Investig. 2024; 28(11):605.

PMID: 39428401 DOI: 10.1007/s00784-024-05957-z.


Correlations of temporomandibular joint morphology and position using cone-beam computed tomography and dynamic functional analysis in orthodontic patients: A cross-sectional study.

Xu B, Park J, Kim S Korean J Orthod. 2024; 54(5):325-341.

PMID: 39317705 PMC: 11422681. DOI: 10.4041/kjod24.089.


Remote, automatic, digital preanesthetic evaluation - are we there yet?.

Pasternak M, Szczeklik W, Bialka S, Andruszkiewicz P, Szczukocka M, Pawlak A Anaesthesiol Intensive Ther. 2024; 56(2):91-97.

PMID: 39166500 PMC: 11284583. DOI: 10.5114/ait.2024.138959.


Analysis of Cervical Range of Motion in Subjects Affected by Temporomandibular Disorders: A Controlled Study.

Nota A, Pittari L, Lannes A, Vaghi C, Calugi Benvenuti C, Tecco S Medicina (Kaunas). 2024; 60(1).

PMID: 38256297 PMC: 10819167. DOI: 10.3390/medicina60010037.


A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases.

Ozsari S, Guzel M, Yilmaz D, Kamburoglu K Diagnostics (Basel). 2023; 13(16).

PMID: 37627959 PMC: 10453523. DOI: 10.3390/diagnostics13162700.


References
1.
dos Anjos Pontual M, Freire J, Barbosa J, Frazao M, dos Anjos Pontual A . Evaluation of bone changes in the temporomandibular joint using cone beam CT. Dentomaxillofac Radiol. 2011; 41(1):24-9. PMC: 3520276. DOI: 10.1259/dmfr/17815139. View

2.
Choi H, Jung S, Baek S, Lim W, Ahn S, Yang I . Artificial Intelligent Model With Neural Network Machine Learning for the Diagnosis of Orthognathic Surgery. J Craniofac Surg. 2019; 30(7):1986-1989. DOI: 10.1097/SCS.0000000000005650. View

3.
Khanagar S, Al-Ehaideb A, Maganur P, Vishwanathaiah S, Patil S, Baeshen H . Developments, application, and performance of artificial intelligence in dentistry - A systematic review. J Dent Sci. 2021; 16(1):508-522. PMC: 7770297. DOI: 10.1016/j.jds.2020.06.019. View

4.
Aubreville M, Knipfer C, Oetter N, Jaremenko C, Rodner E, Denzler J . Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning. Sci Rep. 2017; 7(1):11979. PMC: 5607286. DOI: 10.1038/s41598-017-12320-8. View

5.
Derwich M, Mitus-Kenig M, Pawlowska E . Morphology of the Temporomandibular Joints Regarding the Presence of Osteoarthritic Changes. Int J Environ Res Public Health. 2020; 17(8). PMC: 7215313. DOI: 10.3390/ijerph17082923. View