The application of machine learning in early diagnosis of osteoarthritis: a narrative review

Xuan, Anran; Chen, Haowei; Chen, Tianyu; Li, Jia; Lu, Shi‐Long; Fan, Tianxiang; Wen, Zhibo; Ma, Jianhua; Oo, Win Min; Ding, Changhai; Zhu, Zhaohua

doi:10.1177/1759720x231158198

Cited by 5 publications

(3 citation statements)

References 65 publications

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“…This requires the progression of the current binary classifications to multinomial regression functions that recognize the clinical and radiographic findings in addition to the different biomarkers and classify the early, moderate and severe forms of TMJ osteoarthritis. The development of multinomial deep learning classifiers in the field of TMJ osteoarthritis can promote a line of research that investigates the possibility of diagnosis at an early reversible phase 40 . The present study validated an AI model that diagnosed the early and late radiographic signs of TMJ osteoarthritis and can be considered a base for further research to build such multinomial deep learning models.…”

Section: Discussionmentioning

confidence: 99%

An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint

Talaat,

Shetty,

Al Bayatti

et al. 2023

Sci Rep

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The interpretation of the signs of Temporomandibular joint (TMJ) osteoarthritis on cone-beam computed tomography (CBCT) is highly subjective that hinders the diagnostic process. The objectives of this study were to develop and test the performance of an artificial intelligence (AI) model for the diagnosis of TMJ osteoarthritis from CBCT. A total of 2737 CBCT images from 943 patients were used for the training and validation of the AI model. The model was based on a single convolutional network while object detection was achieved using a single regression model. Two experienced evaluators performed a Diagnostic Criteria for Temporomandibular Disorders (DC/TMD)-based assessment to generate a separate model-testing set of 350 images in which the concluded diagnosis was considered the golden reference. The diagnostic performance of the model was then compared to an experienced oral radiologist. The AI diagnosis showed statistically higher agreement with the golden reference compared to the radiologist. Cohen’s kappa showed statistically significant differences in the agreement between the AI and the radiologist with the golden reference for the diagnosis of all signs collectively (P = 0.0079) and for subcortical cysts (P = 0.0214). AI is expected to eliminate the subjectivity associated with the human interpretation and expedite the diagnostic process of TMJ osteoarthritis.

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Section: Discussionmentioning

confidence: 99%

An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint

Talaat,

Shetty,

Al Bayatti

et al. 2023

Sci Rep

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“…ML approaches have been previously applied to investigating and predicting both the AC mechanical properties [47] and OA progression [48]; however, to the best of the authors' knowledge, no study has tailored these methodologies and developed regression models to predict AC functionality starting from knee joint inflammation. Herein, VSURF and XGBoost ML algorithms were used for regression analysis.…”

Section: Discussionmentioning

confidence: 99%

Relations between Structure/Composition and Mechanics in Osteoarthritic Regenerated Articular Tissue: A Machine Learning Approach

Berni,

Veronesi,

Fini

et al. 2023

IJMS

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In the context of a large animal model of early osteoarthritis (OA) treated by orthobiologics, the purpose of this study was to reveal relations between articular tissues structure/composition and cartilage viscoelasticity. Twenty-four sheep, with induced knee OA, were treated by mesenchymal stem cells in various preparations—adipose-derived mesenchymal stem cells (ADSCs), stromal vascular fraction (SVF), and amniotic endothelial cells (AECs)—and euthanized at 3 or 6 months to evaluate the (i) biochemistry of synovial fluid; (ii) histology, immunohistochemistry, and histomorphometry of articular cartilage; and (iii) viscoelasticity of articular cartilage. After performing an initial analysis to evaluate the correlation and multicollinearity between the investigated variables, this study used machine learning (ML) models—Variable Selection Using Random Forests (VSURF) and Extreme Gradient Boosting (XGB)—to classify variables according to their importance and employ them for interpretation and prediction. The experimental setup revealed a potential relation between cartilage elastic modulus and cartilage thickness (CT), synovial fluid interleukin 6 (IL6), and prostaglandin E2 (PGE2), and between cartilage relaxation time and CT and PGE2. SVF treatment was the only limit on the deleterious OA effect on cartilage viscoelastic properties. This work provides indications to future studies aiming to highlight these and other relationships and focusing on advanced regeneration targets.

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“…OA is an incurable disease at present, and cartilage degeneration and subchondral bone remodeling are considered the main pathogenic mechanisms of OA. There are no drugs that can delay the progression of OA[ 6 ]. The goal of clinical treatment is to relieve symptoms such as pain and loss of function[ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Factors affecting osteogenesis and chondrogenic differentiation of mesenchymal stem cells in osteoarthritis

Yi¹,

Jiang²,

Zuo³

2023

World J Stem Cells

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Osteoarthritis (OA) is a common degenerative joint disease that often involves progressive cartilage degeneration and bone destruction of subchondral bone. At present, clinical treatment is mainly for pain relief, and there are no effective methods to delay the progression of the disease. When this disease progresses to the advanced stage, the only treatment option for most patients is total knee replacement surgery, which causes patients great pain and anxiety. As a type of stem cell, mesenchymal stem cells (MSCs) have multidirectional differentiation potential. The osteogenic differentiation and chondrogenic differentiation of MSCs can play vital roles in the treatment of OA, as they can relieve pain in patients and improve joint function. The differentiation direction of MSCs is accurately controlled by a variety of signaling pathways, so there are many factors that can affect the differentiation direction of MSCs by acting on these signaling pathways. When MSCs are applied to OA treatment, the microenvironment of the joints, injected drugs, scaffold materials, source of MSCs and other factors exert specific impacts on the differentiation direction of MSCs. This review aims to summarize the mechanisms by which these factors influence MSC differentiation to produce better curative effects when MSCs are applied clinically in the future.

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The application of machine learning in early diagnosis of osteoarthritis: a narrative review

Cited by 5 publications

References 65 publications

An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint

An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint

Relations between Structure/Composition and Mechanics in Osteoarthritic Regenerated Articular Tissue: A Machine Learning Approach

Factors affecting osteogenesis and chondrogenic differentiation of mesenchymal stem cells in osteoarthritis

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