Temporomandibular Joint Osteoarthritis Diagnosis Using Privileged Learning of Protein Markers

Zhang, Winston; Bianchi, Jonas; Turkestani, Najla Al; Le, Celia; Deleat‐Besson, Romain; Ruellas, Antônio Carlos de Oliveira; Cevidanes, Lucia; Yatabe, Marília; Gonçalves, João Roberto; Benavides, Erika; Soki, Fabiana N.; Prieto, Juan Carlos; Paniagua, Beatriz; Najarian, Kayvan; Gryak, Jonathan; Soroushmehr, S. M. Reza

doi:10.1109/embc46164.2021.9629990

Cited by 6 publications

(16 citation statements)

References 20 publications

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“…Serious sources of bias included inadequate identification of the participant characteristics or clinical operators 18,21,28,35,36 . Very serious sources of bias included studies utilising the same dataset and CBCT data acquisition procedures that led to some overlapping of data and may have reproduced procedural errors during the combined data acquisition procedure 10,22,24–26 …”

Section: Resultsmentioning

confidence: 99%

“…2,39 All but two papers 19,23 deviated from all the clinical classification methods, resorting to binary classification. 18,20,21,25,26 A binary classification is when only two groups are present in training and diagnostic evaluation, i.e., 'healthy' and 'TMJOA'. Disc displacement on its own is categorised into a comprehensive multi-stage progressive classification based on the ability of the joint to reduce to its original position.…”

Section: Too Many Clinical Classifications For Tmj Disordersmentioning

confidence: 99%

“…The current review revealed several areas for improvement in the administrative handling of machine learning projects. It was seen that the majority of the funds and ethical approvals to use clinical TMJ data for deep learning were provided in South Korea (25%), 18,20,23,29,33 USA (25%) 10,22,[24][25][26] and Japan (15%). 30,31,36 and held by key research teams.…”

Section: The Future Of Automated Tmd Diagnosticsmentioning

confidence: 99%

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Automation and deep (machine) learning in temporomandibular joint disorder radiomics: A systematic review

Farook

Dudley

2023

J of Oral Rehabilitation

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Objective This review aimed to systematically analyse the influence of clinical variables, diagnostic parameters and the overall image acquisition process on automation and deep learning in TMJ disorders. Methods Articles were screened in late 2022 according to a predefined eligibility criteria adhering to the PRISMA protocol. Eligible studies were extracted from databases hosted by MEDLINE, EBSCOHost, Scopus, PubMed and Web of Science. Critical appraisals were performed on individual studies following Nature Medicine's MI‐CLAIM checklist while a combined appraisal of the image acquisition procedures was conducted using Cochrane's GRADE approach. Results Twenty articles were included for full review following eligibility screening. The average experience possessed by the clinical operators within the eligible studies was 13.7 years. Bone volume, trabecular number and separation, and bone surface‐to‐volume ratio were clinical radiographic parameters while disc shape, signal intensity, fluid collection, joint space narrowing and arthritic changes were successful parameters used in MRI‐based deep machine learning. Entropy was correlated to sclerosis in CBCT and was the most stable radiomic parameter in MRI while contrast was the least stable across thermography and MRI. Adjunct serum and salivary biomarkers, or clinical questionnaires only marginally improved diagnostic outcomes through deep learning. Substantial data was classified as unusable and subsequently discarded owing to a combination of suboptimal image acquisition and data augmentation procedures. Inadequate identification of the participant characteristics and multiple studies utilising the same dataset and data acquisition procedures accounted for serious risks of bias. Conclusion Deep‐learned models diagnosed osteoarthritis as accurately as clinicians from 2D and 3D radiographs but, in comparison, performed poorly when detecting disc disorders from MRI datasets. Complexities in clinical classification criteria; non‐standardised diagnostic parameters; errors in image acquisition; cognitive, contextual or implicit biases were influential variables that generally affected analyses of inflammatory joint changes and disc disorders.

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Section: Resultsmentioning

confidence: 99%

Section: Too Many Clinical Classifications For Tmj Disordersmentioning

confidence: 99%

Section: The Future Of Automated Tmd Diagnosticsmentioning

confidence: 99%

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Automation and deep (machine) learning in temporomandibular joint disorder radiomics: A systematic review

Farook

Dudley

2023

J of Oral Rehabilitation

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“…The data from this study was incorporated into two artificial intelligence-based tools -TMJOAI (TMJ Osteoarthritis Artificial Intelligence) tool (31) that integrates biological, clinical and imaging data; and the TMJPI (TMJ Privileged Information) tool (32). The Learning Using Privileged Information (LUPI) implemented in the TMJPI tool uses biological data to train the machine learning model but classifies new patients based on clinical and imaging data only, which is the current standard of care.…”

Section: Diagnostic Performance Of the Markers In Machine Learning Al...mentioning

confidence: 99%

“…The TMJPI tool approach tested the performance of RVFL and KRVFL+ models using biological data as privileged information (32). Considering that biological data is not routinely acquired for TMJ OA patients, we performed fivefold cross-validation and hyper-parameter tuning using a grid-search approach, utilized feature selection approaches such as normalized mutual information feature selection (NMIFS), MRMR (maximum relevancy minimum redundancy) and calculated Shapley Additive explanations values to rank features by their importance (37).…”

Section: Diagnostic Performance Of the Markers In Machine Learning Al...mentioning

confidence: 99%

Quantitative bone imaging biomarkers and joint space analysis of the articular Fossa in temporomandibular joint osteoarthritis using artificial intelligence models

Tamara

Turkestani²,

Bianchi³

et al. 2022

Front. Dent. Med

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Temporomandibular joint osteoarthritis (TMJ OA) is a disease with a multifactorial etiology, involving many pathophysiological processes, and requiring comprehensive assessments to characterize progressive cartilage degradation, subchondral bone remodeling, and chronic pain. This study aimed to integrate quantitative biomarkers of bone texture and morphometry of the articular fossa and joint space to advance the role of imaging phenotypes for diagnosis of Temporomandibular Joint Osteoarthritis (TMJ OA) in early to moderate stages by improving the performance of machine-learning algorithms to detect TMJ OA status. Ninety-two patients were prospectively enrolled (184 h-CBCT scans of the right and left mandibular condyles), divided into two groups: 46 control and 46 TMJ OA subjects. No significant difference in the articular fossa radiomic biomarkers was found between TMJ OA and control patients. The superior condyle-to-fossa distance (p < 0.05) was significantly smaller in diseased patients. The interaction effects of the articular fossa radiomic biomarkers enhanced the performance of machine-learning algorithms to detect TMJ OA status. The LightGBM model achieved an AUC 0.842 to diagnose the TMJ OA status with Headaches and Range of Mouth Opening Without Pain ranked as top features, and top interactions of VE-cadherin in Serum and Angiogenin in Saliva, TGF-β1 in Saliva and Headaches, Gender and Muscle Soreness, PA1 in Saliva and Range of Mouth Opening Without Pain, Lateral Condyle Grey Level Non-Uniformity and Lateral Fossa Short Run Emphasis, TGF-β1 in Serum and Lateral Fossa Trabeculae number, MMP3 in Serum and VEGF in Serum, Headaches and Lateral Fossa Trabecular spacing, Headaches and PA1 in Saliva, and Headaches and BDNF in Saliva. Our preliminary results indicate that condyle imaging features may be more important in regards to main effects, but the fossa imaging features may have a larger contribution in terms of interaction effects. More studies are needed to optimize and further enhance machine-learning algorithms to detect early markers of disease, improve prediction of disease progression and severity to ultimately better serve clinical decision support systems in the treatment of patients with TMJ OA.

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Predicting Osteoarthritis of the Temporomandibular Joint Using Random Forest with Privileged Information

Warner

Turkestani

Bianchi

et al. 2022

Lecture Notes in Computer Science

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Temporomandibular Joint Osteoarthritis Diagnosis Using Privileged Learning of Protein Markers

Cited by 6 publications

References 20 publications

Automation and deep (machine) learning in temporomandibular joint disorder radiomics: A systematic review

Automation and deep (machine) learning in temporomandibular joint disorder radiomics: A systematic review

Quantitative bone imaging biomarkers and joint space analysis of the articular Fossa in temporomandibular joint osteoarthritis using artificial intelligence models

Predicting Osteoarthritis of the Temporomandibular Joint Using Random Forest with Privileged Information

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