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

International audienceObjective The aim of this systematic literature review was to provide a comprehensive and exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis (OA). Methods A systematic literature review was performed in July 2021 using MEDLINE PubMed with key words and MeSH terms. For each selected article, the number of patients, ML algorithms used, type of data analysed, validation methods and data availability were collected. Results From 1148 screened articles, 46 were selected and analysed; most were published after 2017. Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. The number of patients included ranged from 18 to 5749. Overall, 35% of the articles described the use of deep learning And 74% imaging analyses. A total of 85% of the articles involved knee OA and 15% hip OA. No study investigated hand OA. Most of the studies involved the same cohort, with data from the OA initiative described in 46% of the articles and the MOST and Cohort Hip and Cohort Knee cohorts in 11% and 7%. Data and source codes were described as publicly available respectively in 54% and 22% of the articles. External validation was provided in only 7% of the articles. Conclusion This review proposes an up-to-date overview of ML approaches used in clinical OA research and will help to enhance its application in this field

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

ObjectiveThe aim of this systematic literature review was to provide a comprehensive and exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis (OA).MethodsA systematic literature review was performed in July 2021 using MEDLINE PubMed with key words and MeSH terms. For each selected article, the number of patients, ML algorithms used, type of data analysed, validation methods and data availability were collected.ResultsFrom 1148 screened articles, 46 were selected and analysed; most were published after 2017. Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. The number of patients included ranged from 18 to 5749. Overall, 35% of the articles described the use of deep learning And 74% imaging analyses. A total of 85% of the articles involved knee OA and 15% hip OA. No study investigated hand OA. Most of the studies involved the same cohort, with data from the OA initiative described in 46% of the articles and the MOST and Cohort Hip and Cohort Knee cohorts in 11% and 7%. Data and source codes were described as publicly available respectively in 54% and 22% of the articles. External validation was provided in only 7% of the articles.ConclusionThis review proposes an up-to-date overview of ML approaches used in clinical OA research and will help to enhance its application in this field

Identification of Symptoms Phenotypes of Hand Osteoarthritis using Hierarchical Clustering: Results from the <scp>DIGICOD</scp> Cohort

International audienceObjectives: We aimed to delineate phenotypes in hand osteoarthritis (HOA) based on cardinal symptoms (pain, functional limitation, stiffness, aesthetic discomfort).Methods: With data from the DIGItal COhort Design (DIGICOD), we performed hierarchical agglomerative clustering analysis based on Australian/Canadian HOA index sub-scores (AUSCAN) for pain, physical function, stiffness, and visual analogue scale for aesthetic discomfort. Kruskal-Wallis and Post-Hoc analyses were used to assess differences between clusters.Results: Among 389 patients, we identified five clusters: cluster 1 (N=88) and cluster 2 (N=91) featured low and mild symptoms; cluster 3 (N=80) isolated aesthetic discomfort; cluster 4 (N=42) a high level of pain, stiffness, and functional limitation; and cluster 5 (N=88) the same features as cluster 4 but with high aesthetic discomfort. For clusters 4 and 5, AUSCAN pain was > 41/100 representing only one-third of our patients. Aesthetic discomfort (clusters 3 and 5) was significantly associated with erosive HOA and a higher number of nodes. The highly symptomatic cluster 5 was associated but not significantly with metabolic syndrome, and body mass index and C-reactive protein level did not differ among clusters. Symptom intensity was significantly associated with joint destruction as well as with physical and psychological burden. Patients’ main expectations differed among clusters, and function improvement was the most frequent expectation overall.Conclusions: The identification of distinct clinical clusters based on HOA cardinal symptoms suggests previously undescribed subtypes of this condition warranting further study of biological characteristics of such clusters and opening a path toward phenotype-based personalized medicine in HOA