Artificial intelligence for detecting temporomandibular joint osteoarthritis using radiographic image data: A systematic review and meta-analysis of diagnostic test accuracy

Xu, Lanfang; Chen, Jiang; Qiu, Kaiyan; Feng, Yu; Wu, Wei-Liang

doi:10.1371/journal.pone.0288631

Cited by 4 publications

(3 citation statements)

References 55 publications

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“…The 2023 study by Almasan [123] showed that the pooled sensitivity and specificity of AI in panoramic radiograph TMJOA detection accounted for 0.76 (95% CI 0.35-0.95) and 0.79 (95% CI 0.75-0.83), respectively. Similar results related to this topic were reported by Xu [126], who reported a pooled sensitivity, specificity, and area under the curve (AUC) of 80%, 90%, and 92%, respectively. A more comprehensive study carried out by Jha et al [125] analyzed 17 articles for the automated diagnosis of masticatory muscle disorders, TMJ osteoarthrosis, internal derangement, and disc perforation.…”

Section: Tmj Evaluationsupporting

confidence: 86%

“…The few reviews and meta-analyses conducted on this topic showed the overall moderate-to-good accuracy of the tested models in TMJOA detection [122][123][124][125][126]. The 2023 study by Almasan [123] showed that the pooled sensitivity and specificity of AI in panoramic radiograph TMJOA detection accounted for 0.76 (95% CI 0.35-0.95) and 0.79 (95% CI 0.75-0.83), respectively.…”

Section: Tmj Evaluationmentioning

confidence: 99%

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AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning—A Comprehensive Review

Kazimierczak,

Serafin

et al. 2024

JCM

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The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.

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Section: Tmj Evaluationsupporting

confidence: 86%

Section: Tmj Evaluationmentioning

confidence: 99%

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning—A Comprehensive Review

Kazimierczak,

Serafin

et al. 2024

JCM

View full text Add to dashboard Cite

show abstract

“…Using panoramic radiography images, the sensitivity and speci city of the deep learning model for diagnosing TMJ osteoarthritis were equivalent to those of experts 30 . When using CBCT images, the area under the curve (AUC) for diagnostic accuracy of TMJ osteoarthritis was 0.86 31 . A ne-tuned model for articular disc displacement using MRI showed excellent predictive performance (AUC = 0.8775) 32 .…”

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

Artificial intelligence for detecting temporomandibular joint osteoarthritis using radiographic image data: A systematic review and meta-analysis of diagnostic test accuracy

Cited by 4 publications

References 55 publications

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning—A Comprehensive Review

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning—A Comprehensive 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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