Use of machine learning in osteoarthritis research: a systematic literature review.
Artificial Intelligence
Machine Learning
Osteoarthritis
Systemic Literature Review
Journal
RMD open
ISSN: 2056-5933
Titre abrégé: RMD Open
Pays: England
ID NLM: 101662038
Informations de publication
Date de publication:
03 2022
03 2022
Historique:
received:
04
10
2021
accepted:
16
02
2022
entrez:
17
3
2022
pubmed:
18
3
2022
medline:
6
4
2022
Statut:
ppublish
Résumé
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). 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. 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. 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.
Identifiants
pubmed: 35296530
pii: rmdopen-2021-001998
doi: 10.1136/rmdopen-2021-001998
pmc: PMC8928401
pii:
doi:
Types de publication
Journal Article
Systematic Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: AJB is a cofounder and consultant to Personalis and NuMedii; consultant to Samsung, Mango Tree Corporation, and in the recent past, 10 x Genomics, Helix, Pathway Genomics, and Verinata (Illumina); has served on paid advisory panels or boards for Geisinger Health, Regenstrief Institute, Gerson Lehman Group, AlphaSights, Covance, Novartis, Genentech, and Merck, and Roche; is a shareholder in Personalis and NuMedii; is a minor shareholder in Apple, Facebook, Alphabet (Google), Microsoft, Amazon, Snap, 10 x Genomics, Illumina, CVS, Nuna Health, Assay Depot, Vet24seven, Regeneron, Sanofi, Royalty Pharma, AstraZeneca, Moderna, Biogen, Paraxel, and Sutro, and several other non-health related companies and mutual funds; and has received honoraria and travel reimbursement for invited talks from Johnson and Johnson, Roche, Genentech, Pfizer, Merck, Lilly, Takeda, Varian, Mars, Siemens, Optum, Abbott, Celgene, AstraZeneca, AbbVie, Westat and many academic institutions, medical or disease specific foundations and associations, and health systems. AJB receives royalty payments through Stanford University, for several patents and other disclosures licensed to NuMedii and Personalis. AJB’s research has been funded by NIH, Northrup Grumman (as the prime on an NIH contract), Genentech, Johnson and Johnson, FDA, Robert Wood Johnson Foundation, Leon Lowenstein Foundation, Intervalien Foundation, Priscilla Chan and Mark Zuckerberg, the Barbara and Gerson Bakar Foundation, and in the recent past, the March of Dimes, Juvenile Diabetes Research Foundation, California Governor’s Office of Planning and Research, California Institute for Regenerative Medicine, L’Oreal, and Progenity. JS reports personal fees from MSD, Pfizer, Abbvie, Fresenius Kabi, BMS, Roche Chugai, Sandoz, Lilly, Gilead, Novartis, Janssen and grant research from Pfizer, MSD, Schwa Medico, BMS.
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