Reconstructed magnetic resonance image‐based effusion volume assessment for temporomandibular joint arthralgia

Xu, Jiali; Wang, Dongmei; Yang, Chao; Wang, Fangfang; Wang, Meiqing

doi:10.1111/joor.13551

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…The sample size was calculated through an appropriate calculator ( https://www.surveymonkey.com/mp/sample-size-calculator/ ) based on similar previous studies. 8 , 15 , 17 Assuming a confidence level of 95%, a margin of error of 5%, and a population proportion of 50%, the minimum sample size required was determined to be 92 participants.…”

Section: Methodsmentioning

confidence: 99%

“…The rising application of 3D reconstruction provided new means to quantitatively assess joint effusion. 15 3D methods facilitate a more detailed and visual understanding of the pathological changes in TMJ and may potentially enhance the current analysis of MRI slice images. Nonetheless, there are still insufficient studies focused on the effectiveness of 3D quantification in TMJ effusion diagnosis, as well as its necessity over 2D qualitative assessment since it often requires extra temporal and financial cost.…”

Section: Introductionmentioning

confidence: 99%

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Relationship Between Temporomandibular Joint Effusion, Pain, and Jaw Function Limitation: A 2D and 3D Comparative Study

Lau Rui Han,

Xiang,

Zeng

et al. 2024

JPR

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Relationship Between Temporomandibular Joint Effusion, Pain, and Jaw Function Limitation: A 2D and 3D Comparative Study

Lau Rui Han,

Xiang,

Zeng

et al. 2024

JPR

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Can temporomandibular joint osteoarthritis be diagnosed on MRI proton density-weighted images with diagnostic support from the latest deep learning classification models?

Nozawa,

Fukuda,

Kotaki

et al. 2024

Dentomaxillofacial Radiology

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Objectives This study aimed to clarify the performance of magnetic resonance imaging (MRI)-based deep learning classification models in diagnosing temporomandibular joint osteoarthritis (TMJ-OA) and to compare the developed diagnostic assistance with human observers. Methods The subjects were 118 patients who underwent MRI for examination of TMJ disorders. One hundred condyles with TMJ-OA and 100 condyles without TMJ-OA were enrolled. Deep learning was performed with four networks (ResNet18, EfficientNet b4, Inception v3, and GoogLeNet) using five-fold cross validation. Receiver operating characteristics (ROC) curves were drawn for each model and diagnostic metrics were determined. The performances of the four network models were compared using Kruskal-Wallis tests and post-hoc Scheffe tests, and ROCs between the best model and human were compared using chi-square tests, with p < 0.05 considered significant. Results ResNet18 had areas under the curves (AUCs) of 0.91–0.93 and accuracy of 0.85–0.88, which were the highest among the four networks. There were significant differences in AUC and accuracy between ResNet and GoogLeNet (p = 0.0264 and p = 0.0418, respectively). The kappa values of the models were large, 0.95 for ResNet and 0.93 for EfficientNet. The experts achieved similar AUC and accuracy values to the ResNet metrics, 0.94 and 0.85, and 0.84 and 0.84, respectively, but with a lower kappa of 0.67. Those of the dental residents showed lower values. There were significant differences in AUCs between ResNet and residents (p < 0.0001) and between experts and residents (p < 0.0001). Conclusions Using a deep learning model, high performance was confirmed for MRI diagnosis of TMJ-OA.

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Comparative study of clinical and MRI features of TMD patients with or without joint effusion: a retrospective study

Li,

Chen,

Zhang

et al. 2024

BMC Oral Health

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Background The relationship between joint effusion and temporomandibular disorders (TMD) remains unclear. The purpose of this study was to investigate the correlation among joint effusion, clinical features and MRI imaging features of TMD. Methods A total of 1532 temporomandibular joints (TMJs) from 766 patients (605 females and 161 males) with the mean age of 31.68 ± 13.71 years from January 2022 to June 2023 were included in the study. Clinical and MRI features were collected and analyzed. Chi-Square test, Spearman correlation coefficient and binary logistic regression analysis were performed. Results Patients with joint effusion were significantly older and had smaller value of MIO (p < 0.001). There were significant differences in the distribution of joint sounds (with or without), joint pain (with or without), disc morphology (biconcave, contracture, irregular and lengthened) and disc position between joint effusion group (JE) and non-joint effusion group (NA) (P < 0.05).The odds of having joint effusion were 1.726 higher in patients with joint sounds when compared to those without joint sounds. The odds of having joint effusion were 8.463 higher in patients with joint pain when compared to those without joint pain. The odds of having joint effusion were 2.277 higher in patients with contracture when compared to those with biconcave. The odds of having joint effusion were 1.740 higher in patients with anterior disc displacement with reduction (ADDWR) when compared to those with normal disc position. The prediction accuracy of this model is 74.9%, and the area under curve (AUC) is 79.5%, indicating that it can be used for the prediction and the judgment effect is average. Conclusions The results demonstrated that joint sounds, joint pain, contracture, and ADDWR are high risk factors for joint effusion, especially joint pain. Trial registration This study was retrospectively registered on 28/03/2022 and endorsed by the Ethics Committee of Affiliated Stomatology Hospital of Guangzhou Medical University (LCYJ2022014).

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Reconstructed magnetic resonance image‐based effusion volume assessment for temporomandibular joint arthralgia

Cited by 3 publications

References 39 publications

Relationship Between Temporomandibular Joint Effusion, Pain, and Jaw Function Limitation: A 2D and 3D Comparative Study

Relationship Between Temporomandibular Joint Effusion, Pain, and Jaw Function Limitation: A 2D and 3D Comparative Study

Can temporomandibular joint osteoarthritis be diagnosed on MRI proton density-weighted images with diagnostic support from the latest deep learning classification models?

Comparative study of clinical and MRI features of TMD patients with or without joint effusion: a retrospective study

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