Liubov Arbeeva, Mary C Minnig, Katherine A Yates, Amanda E Nelson
{"title":"Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes.","authors":"Liubov Arbeeva, Mary C Minnig, Katherine A Yates, Amanda E Nelson","doi":"10.1007/s11926-023-01114-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose of review: </strong>Osteoarthritis (OA) is a complex heterogeneous disease with no effective treatments. Artificial intelligence (AI) and its subfield machine learning (ML) can be applied to data from different sources to (1) assist clinicians and patients in decision making, based on machine-learned evidence, and (2) improve our understanding of pathophysiology and mechanisms underlying OA, providing new insights into disease management and prevention. The purpose of this review is to improve the ability of clinicians and OA researchers to understand the strengths and limitations of AI/ML methods in applications to OA research.</p><p><strong>Recent findings: </strong>AI/ML can assist clinicians by prediction of OA incidence and progression and by providing tailored personalized treatment. These methods allow using multidimensional multi-source data to understand the nature of OA, to identify different OA phenotypes, and for biomarker discovery. We described the recent implementations of AI/ML in OA research and highlighted potential future directions and associated challenges.</p>","PeriodicalId":10761,"journal":{"name":"Current Rheumatology Reports","volume":null,"pages":null},"PeriodicalIF":5.7000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592147/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Rheumatology Reports","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11926-023-01114-9","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/10 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"RHEUMATOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose of review: Osteoarthritis (OA) is a complex heterogeneous disease with no effective treatments. Artificial intelligence (AI) and its subfield machine learning (ML) can be applied to data from different sources to (1) assist clinicians and patients in decision making, based on machine-learned evidence, and (2) improve our understanding of pathophysiology and mechanisms underlying OA, providing new insights into disease management and prevention. The purpose of this review is to improve the ability of clinicians and OA researchers to understand the strengths and limitations of AI/ML methods in applications to OA research.
Recent findings: AI/ML can assist clinicians by prediction of OA incidence and progression and by providing tailored personalized treatment. These methods allow using multidimensional multi-source data to understand the nature of OA, to identify different OA phenotypes, and for biomarker discovery. We described the recent implementations of AI/ML in OA research and highlighted potential future directions and associated challenges.
期刊介绍:
This journal aims to review the most important, recently published research in the field of rheumatology. By providing clear, insightful, balanced contributions by international experts, the journal intends to serve all those involved in the care and prevention of rheumatologic conditions.
We accomplish this aim by appointing international authorities to serve as Section Editors in key subject areas such as the many forms of arthritis, osteoporosis and metabolic bone disease, and systemic lupus erythematosus. Section Editors, in turn, select topics for which leading experts contribute comprehensive review articles that emphasize new developments and recently published papers of major importance, highlighted by annotated reference lists. An international Editorial Board reviews the annual table of contents, suggests articles of special interest to their country/region, and ensures that topics are current and include emerging research. Commentaries from well-known figures in the field are also occasionally provided.