Quantitative bone imaging biomarkers to diagnose temporomandibular joint osteoarthritis

Bianchi, Jonas; Gonçalves, João Roberto; Ruellas, Antônio Carlos de Oliveira; Ashman, Lawrence M; Vimort, Jean Baptiste; Yatabe, Marília; Paniagua, Beatriz; Hernández, P.R.; Benavides, Erika; Soki, Fabiana N.; Ioshida, Marcos; Cevidanes, Lucia

doi:10.1016/j.ijom.2020.04.018

Cited by 21 publications

(16 citation statements)

References 35 publications

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“…Fig 2 summarizes the study biases as high, low, or unclear. The patient selection bias potential was low in 11 out of 17 studies [ 17 , 19 , 21 , 25 – 32 ] and high in 6 out of 17 studies [ 18 , 20 , 33 – 36 ]. A high risk of bias in patient selection was present due to the inclusion of case-control studies.…”

Section: Resultsmentioning

confidence: 99%

“…Sex distribution indicated higher number of female subjects than male subjects for most of the studies [ 17 , 21 , 27 , 30 , 31 , 35 , 36 ]. Image and nonimage data were used, and medical diagnostic imaging modalities, such as CBCT [ 17 , 18 , 20 , 26 , 33 ], high-resolution CBCT (HR-CBCT) [ 34 – 36 ], MRI [ 21 , 33 ], and panoramic radiography [ 28 ] were used. Other types of image data included infrared cameras with a motion-tracking system [ 27 ] and high-resolution video cameras [ 19 ].…”

Section: Resultsmentioning

confidence: 99%

“…All studies on TMJOA used image data to analyze mandibular condyle shapes [ 17 – 20 , 28 – 31 , 33 – 36 ], and CBCT was the most commonly used imaging modality. Accurate assessment of bony changes is possible using CBCT; thus, it is considered the gold standard for TMJOA [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

“…Feature extraction techniques such as gray-level co-occurrence matrix, gray-level run-length matrix [ 36 ], local binary patterns [ 26 ], and histograms of oriented gradients [ 26 ] are used as image-processing techniques to automatically analyze texture, shape, and color changes within images. Feature selection is an important step in classification [ 52 ].…”

Section: Discussionmentioning

confidence: 99%

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Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis

2022

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Background Artificial intelligence (AI) algorithms have been applied to diagnose temporomandibular disorders (TMDs). However, studies have used different patient selection criteria, disease subtypes, input data, and outcome measures. Resultantly, the performance of the AI models varies. Objective This study aimed to systematically summarize the current literature on the application of AI technologies for diagnosis of different TMD subtypes, evaluate the quality of these studies, and assess the diagnostic accuracy of existing AI models. Materials and methods The study protocol was carried out based on the preferred reporting items for systematic review and meta-analysis protocols (PRISMA). The PubMed, Embase, and Web of Science databases were searched to find relevant articles from database inception to June 2022. Studies that used AI algorithms to diagnose at least one subtype of TMD and those that assessed the performance of AI algorithms were included. We excluded studies on orofacial pain that were not directly related to the TMD, such as studies on atypical facial pain and neuropathic pain, editorials, book chapters, and excerpts without detailed empirical data. The risk of bias was assessed using the QUADAS-2 tool. We used Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) to provide certainty of evidence. Results A total of 17 articles for automated diagnosis of masticatory muscle disorders, TMJ osteoarthrosis, internal derangement, and disc perforation were included; they were retrospective studies, case-control studies, cohort studies, and a pilot study. Seven studies were subjected to a meta-analysis for diagnostic accuracy. According to the GRADE, the certainty of evidence was very low. The performance of the AI models had accuracy and specificity ranging from 84% to 99.9% and 73% to 100%, respectively. The pooled accuracy was 0.91 (95% CI 0.76–0.99), I2 = 97% (95% CI 0.96–0.98), p < 0.001. Conclusions Various AI algorithms developed for diagnosing TMDs may provide additional clinical expertise to increase diagnostic accuracy. However, it should be noted that a high risk of bias was present in the included studies. Also, certainty of evidence was very low. Future research of higher quality is strongly recommended.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis

2022

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“…J o u r n a l P r e -p r o o f 12 Changes in bone are part of the OA disease process. Recent studies have investigated the role of bone shape [19][20][21][22][23][24] , subchondral bone structure and texture [25][26][27][28] , and bone morphology in OA [29][30][31] as well as the interplay between subchondral bone changes and articular cartilage damage 32,33 .…”

Section: Imaging Of Bone Cartilage and Bone-cartilage Interactions In Oamentioning

confidence: 99%