Evidence map›Paper›PMID 35284273›Full record

ArticleQuantitative imaging in medicine and surgery2022

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

Kug Jin Jeon, Young Hyun Kim, Eun-Gyu Ha, Han Seung Choi, Hyung-Joon Ahn, Jeong Ryong Lee, Dosik Hwang, Sang-Sun Han

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Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

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10citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Kug Jin Jeon *Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea.
Young Hyun Kim *Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea.
Eun-Gyu HaDepartment of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea.
Han Seung ChoiDepartment of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea.
Hyung-Joon AhnDepartment of Orofacial Pain and Oral Medicine, Dental Hospital, Yonsei University College of Dentistry, Seoul, Korea.
Jeong Ryong LeeDepartment of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea.
Dosik HwangDepartment of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea.
Sang-Sun HanDepartment of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 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.

Indexed as

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

Identifiers

PMID35284273
PMCPMC8899952

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