Literature DB >> 35284273

Quantitative analysis of the mouth opening movement of temporomandibular joint disorder patients according to disc position using computer vision: a pilot study.

Kug Jin Jeon1, Young Hyun Kim1, Eun-Gyu Ha1, Han Seung Choi1, Hyung-Joon Ahn2, Jeong Ryong Lee3, Dosik Hwang1,3,4, Sang-Sun Han1,4,5.   

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 (χ2=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. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Entities:  

Keywords:  Artificial intelligence (AI); magnetic resonance imaging (MRI); temporomandibular joint disorder (TMD)

Year:  2022        PMID: 35284273      PMCID: PMC8899952          DOI: 10.21037/qims-21-629

Source DB:  PubMed          Journal:  Quant Imaging Med Surg        ISSN: 2223-4306


  25 in total

1.  Artificial Intelligent Model With Neural Network Machine Learning for the Diagnosis of Orthognathic Surgery.

Authors:  Hyuk-Il Choi; Seok-Ki Jung; Seung-Hak Baek; Won Hee Lim; Sug-Joon Ahn; Il-Hyung Yang; Tae-Woo Kim
Journal:  J Craniofac Surg       Date:  2019-10       Impact factor: 1.046

Review 2.  Role of magnetic resonance imaging in the clinical diagnosis of the temporomandibular joint.

Authors:  Tore A Larheim
Journal:  Cells Tissues Organs       Date:  2005       Impact factor: 2.481

3.  Evaluation of bone changes in the temporomandibular joint using cone beam CT.

Authors:  M L dos Anjos Pontual; J S L Freire; J M N Barbosa; M A G Frazão; A dos Anjos Pontual
Journal:  Dentomaxillofac Radiol       Date:  2012-01       Impact factor: 2.419

4.  Mandibular Range of Movement and Pain Intensity in Patients with Anterior Disc Displacement without Reduction.

Authors:  Iva Z Alajbeg; Marijana Gikić; Melita Valentić-Peruzović
Journal:  Acta Stomatol Croat       Date:  2015-06

5.  An automated technique to stage lower third molar development on panoramic radiographs for age estimation: a pilot study.

Authors:  J De Tobel; P Radesh; D Vandermeulen; P W Thevissen
Journal:  J Forensic Odontostomatol       Date:  2017-12-01

6.  Relationship between anterior disc displacement with/without reduction and effusion in temporomandibular disorder patients using magnetic resonance imaging.

Authors:  Kwang-Joon Koh; Ha-Na Park; Kyoung-A Kim
Journal:  Imaging Sci Dent       Date:  2013-12-12

7.  Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning.

Authors:  Marc Aubreville; Christian Knipfer; Nicolai Oetter; Christian Jaremenko; Erik Rodner; Joachim Denzler; Christopher Bohr; Helmut Neumann; Florian Stelzle; Andreas Maier
Journal:  Sci Rep       Date:  2017-09-20       Impact factor: 4.379

8.  Morphology of the Temporomandibular Joints Regarding the Presence of Osteoarthritic Changes.

Authors:  Marcin Derwich; Maria Mitus-Kenig; Elzbieta Pawlowska
Journal:  Int J Environ Res Public Health       Date:  2020-04-23       Impact factor: 3.390

9.  Temporomandibular Joints' Morphology and Osteoarthritic Changes in Cone-Beam Computed Tomography Images in Patients with and without Reciprocal Clicking-A Case Control Study.

Authors:  Marcin Derwich; Maria Mitus-Kenig; Elzbieta Pawlowska
Journal:  Int J Environ Res Public Health       Date:  2020-05-14       Impact factor: 3.390

Review 10.  Developments, application, and performance of artificial intelligence in dentistry - A systematic review.

Authors:  Sanjeev B Khanagar; Ali Al-Ehaideb; Prabhadevi C Maganur; Satish Vishwanathaiah; Shankargouda Patil; Hosam A Baeshen; Sachin C Sarode; Shilpa Bhandi
Journal:  J Dent Sci       Date:  2020-06-30       Impact factor: 2.080

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