Comparison of T2 Weighted, Fat‐Suppressed T2 Weighted, and Three‐Dimensional (3D) Fast Imaging Employing Steady‐State Acquisition (FIESTA‐C) Sequences in the Temporomandibular Joint (TMJ) Evaluation

Aksoy, Seçil; Orhan, Kaan

doi:10.1155/2021/6032559

Cited by 3 publications

(2 citation statements)

References 45 publications

(56 reference statements)

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“…Early diagnosis is important for managing the temporomandibular disorders [9]. MRI sensitivity, speci city, and diagnostic accuracy are the gold standards for articular disc imaging [10,11].…”

Section: Discussionmentioning

confidence: 99%

The Diagnostic Reliability of Various Magnetic Resonance Imaging sequences for the Early Diagnosis of Temporomandibular Disorders: A Systematic Review

Ganesan,

Chandrasekar.Lakshmi

et al. 2024

Preprint

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Background This systematic study aimed to compare and evaluate the diagnostic accuracy of different MRI sequences for detecting internal TMJ dysfunction. Materials and methods In this search strategy, we employed Google Scholar, PubMed, PubMed Central, Science Direct, and the Cochrane Library. Articles from the last decade were included in the search. The potential for bias in the included studies was evaluated. The search yielded 48 papers. However, only 10 papers met the criteria for inclusion in the systematic review. Results Various sequences such as FIESTA, FLAIR, T2 mapping, MERGE, HASTE, and dGEMRIC, have been discussed in this systematic review. Conclusion Among the various MR sequences, T2 mapping and dGEMRIC sequences help in the quantitative evaluation and assessment of early changes in disc derangement. These sequences provide in-depth diagnostic information complementary to conventional MRI for diagnosing early changes in the TMJ disc

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Section: Discussionmentioning

confidence: 99%

The Diagnostic Reliability of Various Magnetic Resonance Imaging sequences for the Early Diagnosis of Temporomandibular Disorders: A Systematic Review

Ganesan,

Chandrasekar.Lakshmi

et al. 2024

Preprint

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“…Effusion may be related to disc displacement, degenerative joint disease, and arthralgia; however, it has not been mentioned as a major condition for judgment 14 . TMJ effusion typically shows high signal intensity on proton density (PD) and T2-weighted (T2W) magnetic resonance imaging (MRI) 17 . To date, MRI studies investigating TMJ effusion or the association between clinical signs and symptoms, including TMJ effusion and pain, have been limited.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Temporomandibular Joint Effusion with Deep Learning Algorithm

Lee,

Jeon,

Won

et al. 2023

Preprint

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This study investigated the usefulness of deep learning-based automatic detection of temporomandibular joint (TMJ) effusion using magnetic resonance imaging (MRI) in patients with temporomandibular joint disorder (TMD) and whether the diagnostic accuracy of the model improved when patients’ clinical information was provided in addition to MRI images. The sagittal MR images of 2,948 TMJs were collected from 1,017 women and 457 men (mean age 37.19 ± 18.64 years). The TMJ effusion diagnostic performances of three convolutional neural networks (scratch, fine-tuning, and freeze schemes) were compared with those of human experts based on areas under the curve (AUCs) and diagnosis accuracies. The fine-tuning model with proton density (PD) images showed acceptable prediction performance (AUC = 0.7895), and the from-scratch (0.6193) and freeze (0.6149) models showed lower performances (p < 0.05). The fine-tuning model had excellent specificity compared to the human experts (87.25% vs. 58.17%). However, the human experts were superior in sensitivity (80.00% vs. 57.43%) (all p < 0.001). In Grad-CAM visualizations, the fine-tuning scheme focused more on effusion than on other structures of the TMJ, and the sparsity was higher than that of the from-scratch scheme (82.40% vs. 49.83%, p < 0.05). The Grad-CAM visualizations agreed with the model learned through important features in the TMJ area, particularly around the articular disc. Two fine-tuning models on PD and T2-weighted images showed that the diagnostic performance did not improve compared with using PD alone (p < 0.05). Diverse AUCs were observed across each group when the patients were divided according to age (0.7083–0.8375) and sex (male:0.7576, female:0.7083). The prediction accuracy of the ensemble model was higher than that of the human experts when all the data were used (74.21% vs. 67.71%, p < 0.05). A deep neural network (DNN) was developed to process multimodal data, including MRI and patient clinical data. Analysis of four age groups with the DNN model showed that the 41–60 age group had the best performance (AUC = 0.8258). There was no significant difference between the prediction performances of the fine-tuning model and the DNN (p > 0.05). The fine-tuning model and DNN were optimal for judging TMJ effusion and may be used to prevent true negative cases and aid in human diagnostic performance. Assistive automated diagnostic methods have the potential to increase clinicians’ diagnostic accuracy.

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Automatic detection and visualization of temporomandibular joint effusion with deep neural network

Lee,

Jeon,

Won

et al. 2024

Sci Rep

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This study investigated the usefulness of deep learning-based automatic detection of temporomandibular joint (TMJ) effusion using magnetic resonance imaging (MRI) in patients with temporomandibular disorder and whether the diagnostic accuracy of the model improved when patients’ clinical information was provided in addition to MRI images. The sagittal MR images of 2948 TMJs were collected from 1017 women and 457 men (mean age 37.19 ± 18.64 years). The TMJ effusion diagnostic performances of three convolutional neural networks (scratch, fine-tuning, and freeze schemes) were compared with those of human experts based on areas under the curve (AUCs) and diagnosis accuracies. The fine-tuning model with proton density (PD) images showed acceptable prediction performance (AUC = 0.7895), and the from-scratch (0.6193) and freeze (0.6149) models showed lower performances ( p < 0.05). The fine-tuning model had excellent specificity compared to the human experts (87.25% vs. 58.17%). However, the human experts were superior in sensitivity (80.00% vs. 57.43%) (all p < 0.001). In gradient-weighted class activation mapping (Grad-CAM) visualizations, the fine-tuning scheme focused more on effusion than on other structures of the TMJ, and the sparsity was higher than that of the from-scratch scheme (82.40% vs. 49.83%, p < 0.05). The Grad-CAM visualizations agreed with the model learned through important features in the TMJ area, particularly around the articular disc. Two fine-tuning models on PD and T2-weighted images showed that the diagnostic performance did not improve compared with using PD alone ( p < 0.05). Diverse AUCs were observed across each group when the patients were divided according to age (0.7083–0.8375) and sex (male:0.7576, female:0.7083). The prediction accuracy of the ensemble model was higher than that of the human experts when all the data were used (74.21% vs. 67.71%, p < 0.05). A deep neural network (DNN) was developed to process multimodal data, including MRI and patient clinical data. Analysis of four age groups with the DNN model showed that the 41–60 age group had the best performance (AUC = 0.8258). The fine-tuning model and DNN were optimal for judging TMJ effusion and may be used to prevent true negative cases and aid in human diagnostic performance. Assistive automated diagnostic methods have the potential to increase clinicians’ diagnostic accuracy.

show abstract

Comparison of T2 Weighted, Fat‐Suppressed T2 Weighted, and Three‐Dimensional (3D) Fast Imaging Employing Steady‐State Acquisition (FIESTA‐C) Sequences in the Temporomandibular Joint (TMJ) Evaluation

Cited by 3 publications

References 45 publications

The Diagnostic Reliability of Various Magnetic Resonance Imaging sequences for the Early Diagnosis of Temporomandibular Disorders: A Systematic Review

The Diagnostic Reliability of Various Magnetic Resonance Imaging sequences for the Early Diagnosis of Temporomandibular Disorders: A Systematic Review

Automatic Detection of Temporomandibular Joint Effusion with Deep Learning Algorithm

Automatic detection and visualization of temporomandibular joint effusion with deep neural network

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