Use of machine learning in osteoarthritis research: a systematic literature review

Binvignat, Marie; Pedoia, Valentina; Butte, Atul J.; Louati, Karine; Klatzmann, David; Bérenbaum, Francis; Mariotti‐Ferrandiz, Encarnita; Sellam, Jérémie

doi:10.1136/rmdopen-2021-001998

Cited by 36 publications

(37 citation statements)

References 85 publications

(83 reference statements)

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“…This situation reinforces the need for purpose-built cohorts for ML analyses and future studies should aim to test ML models on broad data to demonstrate the robustness and generalizability. [63][64][65] Although ML could be an important tool to aid the work of researchers, and eventually clinicians, in the early identification of OA, acceptable quality standards for real-world applications have not been fully met. It is hoped that the heightened pace of external validation added to the current pace of model development and internal validation will accelerate the translation to the accurate, rapid, and cost-effective detection of OA in the near future.…”

Section: Perspectives Of ML In Oamentioning

confidence: 99%

“…Moreover, developing interpretable ML models that can determine which features contribute to a specific model decision is also a challenge. 65 Despite these limitations, we believe that investigational methodologies are emerging in clinical trials, and could provide better quantitative information about the joints of patients who already have OA in a systematic way. 21 In conclusion, we have demonstrated current predictive models based on ML for the early diagnosis of OA.…”

Section: Perspectives Of ML In Oamentioning

confidence: 99%

Section: Perspectives Of ML In Oamentioning

confidence: 99%

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

Xuan

Chen

et al. 2023

Therapeutic Advances in Musculoskeletal

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Osteoarthritis (OA) is the commonest musculoskeletal disease worldwide, with an increasing prevalence due to aging. It causes joint pain and disability, decreased quality of life, and a huge burden on healthcare services for society. However, the current main diagnostic methods are not suitable for early diagnosing patients of OA. The use of machine learning (ML) in OA diagnosis has increased dramatically in the past few years. Hence, in this review article, we describe the research progress in the application of ML in the early diagnosis of OA, discuss the current trends and limitations of ML approaches, and propose future research priorities to apply the tools in the field of OA. Accurate ML-based predictive models with imaging techniques that are sensitive to early changes in OA ahead of the emergence of clinical features are expected to address the current dilemma. The diagnostic ability of the fusion model that combines multidimensional information makes patient-specific early diagnosis and prognosis estimation of OA possible in the future.

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Section: Perspectives Of ML In Oamentioning

confidence: 99%

Section: Perspectives Of ML In Oamentioning

confidence: 99%

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

Xuan

Chen

et al. 2023

Therapeutic Advances in Musculoskeletal

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“…In 2022, some authors provided an outline of ML applications in musculoskeletal histopathology [37], and reviewed the ML methods applied to OA research [38], with a special emphasis on Magnetic Resonance Imaging (MRI) [39]. Other authors addressed the use of three specific ML algorithms including logistic regression, in the diagnosis of rheumatic illnesses [40].…”

Section: Past Reviewsmentioning

confidence: 99%

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature

Madrid

Merino‐Barbancho

Rodríguez-González

et al. 2022

Preprint

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The outstanding and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five-year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease classification, disease prediction, predictors identification, patient stratification and disease subtype identification, disease progression and activity, and treatment response. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed are highlighted in a discussion. Therefore, the present review arises with the aim of characterising how researchers are employing data science techniques in the rheumatology medical field.

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“…Recent advancements in machine learning in healthcare have led us to accelerate and improve the diagnostic process of various life-threatening diseases, including rheumatology and OA [10,11]. In this paper, we suggest an advanced automated OA diagnostic system based on neural networks and transfer learning.…”

Section: Introductionmentioning

confidence: 99%

Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach

Alshamrani

Rashid²,

Alshamrani

et al. 2023

Healthcare

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Knee osteoarthritis is a challenging problem affecting many adults around the world. There are currently no medications that cure knee osteoarthritis. The only way to control the progression of knee osteoarthritis is early detection. Currently, X-ray imaging is a central technique used for the prediction of osteoarthritis. However, the manual X-ray technique is prone to errors due to the lack of expertise of radiologists. Recent studies have described the use of automated systems based on machine learning for the effective prediction of osteoarthritis from X-ray images. However, most of these techniques still need to achieve higher predictive accuracy to detect osteoarthritis at an early stage. This paper suggests a method with higher predictive accuracy that can be employed in the real world for the early detection of knee osteoarthritis. In this paper, we suggest the use of transfer learning models based on sequential convolutional neural networks (CNNs), Visual Geometry Group 16 (VGG-16), and Residual Neural Network 50 (ResNet-50) for the early detection of osteoarthritis from knee X-ray images. In our analysis, we found that all the suggested models achieved a higher level of predictive accuracy, greater than 90%, in detecting osteoarthritis. However, the best-performing model was the pretrained VGG-16 model, which achieved a training accuracy of 99% and a testing accuracy of 92%.

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Use of machine learning in osteoarthritis research: a systematic literature review

Cited by 36 publications

References 85 publications

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

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

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature

Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach

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