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.
Summary
We described the recent implementations of AI/ML in OA research and highlighted potential future directions and associated challenges.
Similar content being viewed by others
Abbreviations
- ACR :
-
American College of Rheumatology
- AI :
-
artificial intelligence
- AUC :
-
area under curve
- BMI :
-
body mass index
- CDW :
-
Clinical Data Warehouses
- CNN :
-
convolutional neural network
- DEC :
-
deep embedded clustering
- DL :
-
deep learning
- EHR :
-
electronic health record
- GWAS :
-
genome-wide association studies
- KLG :
-
Kellgren-Lawrence Grade
- LR :
-
logistic regression
- MFAC :
-
clustering with multiple factor analysis
- ML :
-
machine learning
- MRI :
-
magnetic resonance imaging
- NSAID :
-
non-steroidal anti-inflammatory drugs
- OA :
-
osteoarthritis
- OAI :
-
Osteoarthritis Initiative
- RCT :
-
randomized clinical trial
- RF :
-
random forest
- TJR :
-
total joint replacement
- US :
-
ultrasound data
- WOMAC :
-
Western Ontario and McMaster Universities Arthritis Index
- XGB :
-
eXtreme Gradient Boosting
References
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
Murphy LB, Cisternas MG, Pasta DJ, Helmick CG, Yelin EH. Medical expenditures and earnings losses among us adults with arthritis in 2013. Arthritis Care Res (Hoboken). 2018;70(6):869–76.
Kolasinski SL, Neogi T, Hochberg MC, Oatis C, Guyatt G, Block J, et al. 2019 American College of Rheumatology/Arthritis Foundation Guideline for the Management of Osteoarthritis of the Hand, Hip, and Knee. Arthritis Rheumatol. 2020;72(2):220–33.
Grässel S, Muschter D. Recent advances in the treatment of osteoarthritis. F1000Res. 2020;9:F1000 Faculty Rev–325. https://doi.org/10.12688/f1000research.22115.1.
Loos NL, Hoogendam L, Souer JS, Slijper HP, Andrinopoulou ER, Coppieters MW, et al. Machine learning can be used to predict function but not pain after surgery for thumb carpometacarpal osteoarthritis. Clin Orthop Relat Res. 2022;480(7):1271–84.
Bowes MA, Kacena K, Alabas OA, Brett AD, Dube B, Bodick N, et al. Machine-learning, MRI bone shape and important clinical outcomes in osteoarthritis: data from the Osteoarthritis Initiative. Ann Rheum Dis. 2021;80(4):502–8.
Chaudhari AS, Stevens KJ, Wood JP, Chakraborty AK, Gibbons EK, Fang Z, et al. Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers. J Magn Reson Imaging. 2020;51(3):768–79.
Lester G. The Osteoarthritis Initiative: A NIH Public-Private Partnership. HSS J. 2012;8(1):62–3.
Chen G, Sullivan PF, Kosorok MR. Biclustering with heterogeneous variance. Proc Natl Acad Sci U S A. 2013;110(30):12253–8.
Cheng Y, Church GM. Biclustering of expression data. Proc Int Conf Intell Syst Mol Biol. 2000;8:93–103.
Nelson AE, Keefe TH, Schwartz TA, Callahan LF, Loeser RF, Golightly YM, et al. Biclustering reveals potential knee OA phenotypes in exploratory analyses: Data from the Osteoarthritis Initiative. PLoS One. 2022;17(5):e0266964.
Demanse D, Saxer F, Lustenberger P, Tanko LB, Nikolaus P, Rasin I, et al. Unsupervised machine-learning algorithms for the identification of clinical phenotypes in the osteoarthritis initiative database. Semin Arthritis Rheum. 2023;58:152140.
Trajerova M, Kriegova E, Mikulkova Z, Savara J, Kudelka M, Gallo J. Knee osteoarthritis phenotypes based on synovial fluid immune cells correlate with clinical outcome trajectories. Osteoarthritis Cartilage. 2022;30(12):1583–92.
Deveza LA, Nelson AE, Loeser RF. Phenotypes of osteoarthritis: Current state and future implications. Clin Exp Rheumatol. 2019;37 Suppl;120(5):64–72.
Mobasheri A, van Spil WE, Budd E, Uzieliene I, Bernotiene E, Bay-Jensen AC, et al. Molecular taxonomy of osteoarthritis for patient stratification, disease management and drug development: Biochemical markers associated with emerging clinical phenotypes and molecular endotypes. Curr Opin Rheumatol. 2019;31(1):80–9.
Steinberg J, Southam L, Fontalis A, Clark MJ, Jayasuriya RL, Swift D, et al. Linking chondrocyte and synovial transcriptional profile to clinical phenotype in osteoarthritis. Ann Rheum Dis. 2021;80(8):1070–4. Used machine learning to assess gene expression profiles with results supporting the theory that osteoarthritis is a continuum with less variation at later stages of disease; greater heterogeneity early in disease suggests an opportunity for tailored treatment.
Widera P, PMJ W, Ladel C, Loughlin J, Lafeber F, Petit Dop F, et al. Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data. Sci Rep. 2020;10(1):8427. Rigorous statistical framework using advanced statistical techniques to account for classes imbalance and incomplete data. Used categorical rather than binary definition of the outcome, KOA progression.
van Helvoort EM, van Spil WE, Jansen MP, Welsing PMJ, Kloppenburg M, Loef M, et al. Cohort profile: The Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) study: a 2-year, European, cohort study to describe, validate and predict phenotypes of osteoarthritis using clinical, imaging and biochemical markers. BMJ Open. 2020;10(7):e035101.
Kraus VB, Collins JE, Hargrove D, Losina E, Nevitt M, Katz JN, et al. Predictive validity of biochemical biomarkers in knee osteoarthritis: Data from the FNIH OA biomarkers consortium. Ann Rheum Dis. 2017;76(1):186–95.
Bonakdari H, Pelletier JP, Abram F, Martel-Pelletier J. A machine learning model to predict knee osteoarthritis cartilage volume changes over time using baseline bone curvature. Biomedicines. 2022;10(6)
Raynauld JP, Pelletier JP, Delorme P, Dodin P, Abram F, Martel-Pelletier J. Bone curvature changes can predict the impact of treatment on cartilage volume loss in knee osteoarthritis: data from a 2-year clinical trial. Rheumatology (Oxford). 2017;56(6):989–98.
Raynauld JP, Martel-Pelletier J, Bias P, Laufer S, Haraoui B, Choquette D, et al. Protective effects of licofelone, a 5-lipoxygenase and cyclo-oxygenase inhibitor, versus naproxen on cartilage loss in knee osteoarthritis: a first multicentre clinical trial using quantitative MRI. Ann Rheum Dis. 2009;68(6):938–47.
Ilse M, Tomczak J, Welling M. Attention-based deep multiple instance learning. In: International conference on machine learning. PMLR; 2018. p. 2127–36.
Schiratti JB, Dubois R, Herent P, Cahane D, Dachary J, Clozel T, et al. A deep learning method for predicting knee osteoarthritis radiographic progression from MRI. Arthritis Res Ther. 2021;23(1):262. Developed a weakly supervised deep learning algorithm to predict OA progression over a short time frame; encouraging results suggest that such algorithms can feasibility be integrated into the screening phase of clinical trials and improve how inclusion criteria are determined.
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual explanations from deep networks via gradient-based localization. Int J Comput Vis. 2020;128(2):336–59.
Guan B, Liu F, Haj-Mirzaian A, Demehri S, Samsonov A, Neogi T, et al. Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period. Osteoarthritis Cartilage. 2020;28(4):428–37.
Guan B, Liu F, Mizaian AH, Demehri S, Samsonov A, Guermazi A, et al. Deep learning approach to predict pain progression in knee osteoarthritis. Skeletal Radiol. 2022;51(2):363–73.
Nelson AE, Arbeeva L. Narrative review of machine learning in rheumatic and musculoskeletal diseases for clinicians and researchers: biases, goals, and future directions. J Rheumatol. 2022;49(11):1191–200. Review of machine learning in rheumatic and musculoskeletal diseases beyond osteoarthritis, providing extensive discussion around potential biases and limitations.
Yoo HJ, Jeong HW, Kim SW, Kim M, Lee JI, Lee YS. Prediction of progression rate and fate of osteoarthritis: Comparison of machine learning algorithms. J Orthop Res. 2023;41(3):583–90.
Dunn CM, Sturdy C, Velasco C, Schlupp L, Prinz E, Izda V, et al. Peripheral blood DNA methylation-based machine learning models for prediction of knee osteoarthritis progression: Biologic specimens and data from the osteoarthritis initiative and johnston county osteoarthritis project. Arthritis Rheumatol. 2023;75(1):28–40. Use of fully independent data for external validation and investigation of potentially novel epigenetic biomarkers for useful clinical progression definitions are strengths of this work.
Bonakdari H, Pelletier JP, Blanco FJ, Rego-Pérez I, Durán-Sotuela A, Aitken D, et al. Single nucleotide polymorphism genes and mitochondrial DNA haplogroups as biomarkers for early prediction of knee osteoarthritis structural progressors: use of supervised machine learning classifiers. BMC Med. 2022;20(1):316.
Dore D, Martens A, Quinn S, Ding C, Winzenberg T, Zhai G, et al. Bone marrow lesions predict site-specific cartilage defect development and volume loss: a prospective study in older adults. Arthritis Res Ther. 2010;12(6):R222.
Leung K, Zhang B, Tan J, Shen Y, Geras KJ, Babb JS, et al. Prediction of total knee replacement and diagnosis of osteoarthritis by using deep learning on knee radiographs: data from the osteoarthritis initiative. Radiology. 2020;296(3):584–93.
Jamshidi A, Pelletier JP, Labbe A, Abram F, Martel-Pelletier J, Droit A. Machine learning-based individualized survival prediction model for total knee replacement in osteoarthritis: data from the osteoarthritis initiative. Arthritis Care Res (Hoboken). 2021;73(10):1518–27.
Tiulpin A, Saarakkala S, Mathiessen A, Hammer HB, Furnes O, Nordsletten L, et al. Predicting total knee arthroplasty from ultrasonography using machine learning. Osteoarthr Cartil Open. 2022;4(4):100319.
Hirvasniemi J, Runhaar J, van der Heijden RA, Zokaeinikoo M, Yang M, Li X, et al. The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images. Osteoarthritis Cartilage. 2023;31(1):115–25. The first biomedical challenge on the prediction of incident symptomatic radiographic knee OA, a step towards unbiased comparison between different models, robust validation and clinical translation of AI/ML algorithms.
Runhaar J, van Middelkoop M, Reijman M, Willemsen S, Oei EH, Vroegindeweij D, et al. Prevention of knee osteoarthritis in overweight females: the first preventive randomized controlled trial in osteoarthritis. Am J Med. 2015;128(8):888–95. e4
Allen KD, Helmick CG, Schwartz TA, DeVellis RF, Renner JB, Jordan JM. Racial differences in self-reported pain and function among individuals with radiographic hip and knee osteoarthritis: the Johnston County Osteoarthritis Project. Osteoarthritis Cartilage. 2009;17(9):1132–6.
Vaughn IA, Terry EL, Bartley EJ, Schaefer N, Fillingim RB. Racial-ethnic differences in osteoarthritis pain and disability: A meta-analysis. J Pain. 2019;20(6):629–44.
Pierson E, Cutler DM, Leskovec J, Mullainathan S, Obermeyer Z. An algorithmic approach to reducing unexplained pain disparities in underserved populations. Nat Med. 2021;27(1):136–40. An example of implementation of AI algorithm for predicting the severity of OA symptoms based on objective image data rather than subjective self-report and/or radiologist assessment. If externally validated, can be used as a decision aid for TJR referral as it can potentially mitigate bias in pain assessment in disadvantaged social groups and reduce health disparities in pain management and medical decisions.
Blum MA, Ibrahim SA. Race/ethnicity and use of elective joint replacement in the management of end-stage knee/hip osteoarthritis: a review of the literature. Clin Geriatr Med. 2012;28(3):521–32.
Singh JA, Lu X, Rosenthal GE, Ibrahim S, Cram P. Racial disparities in knee and hip total joint arthroplasty: An 18-year analysis of national Medicare data. Ann Rheum Dis. 2014;73(12):2107–15.
Joseph GB, McCulloch CE, Nevitt MC, Link TM, Sohn JH. Machine learning to predict incident radiographic knee osteoarthritis over 8 Years using combined MR imaging features, demographics, and clinical factors: data from the Osteoarthritis Initiative. Osteoarthritis Cartilage. 2022;30(2):270–9.
Messier SP, Mihalko SL, Legault C, Miller GD, Nicklas BJ, DeVita P, et al. Effects of intensive diet and exercise on knee joint loads, inflammation, and clinical outcomes among overweight and obese adults with knee osteoarthritis: the IDEA randomized clinical trial. JAMA. 2013;310(12):1263–73.
Jiang X, Nelson AE, Cleveland RJ, Beavers DP, Schwartz TA, Arbeeva L, et al. Precision medicine approach to develop and internally validate optimal exercise and weight-loss treatments for overweight and obese adults with knee osteoarthritis: Data from a single-center randomized trial. Arthritis Care Res (Hoboken). 2021;73(5):693–701. This is among the first studies to apply precision medicine methodology to interventions in OA, and uses data from an existing, high quality RCT, finding potential subgroups where benefit could be increased by optimal assignment based on baseline features.
Chen B, Butte AJ. Leveraging big data to transform target selection and drug discovery. Clin Pharmacol Ther. 2016;99(3):285–97.
Hodos RA, Kidd BA, Shameer K, Readhead BP, Dudley JT. In silico methods for drug repurposing and pharmacology. Wiley Interdiscip Rev Syst Biol Med. 2016;8(3):186–210.
Jang IJ. Artificial intelligence in drug development: clinical pharmacologist perspective. Transl Clin Pharmacol. 2019;27(3):87–8.
Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 2018;178(11):1544–7.
Reinke A, Tizabi MD, Eisenmann M, Maier-Hein L. Common pitfalls and recommendations for grand challenges in medical artificial intelligence. Eur Urol Focus. 2021;7(4):710–2.
Funding
Funding for this work was provided in part by NIH/NIAMS K24AR081368 and P30AR07250. The funders had no role in the writing or submission of the manuscript. Dr. Nelson also reports funding outside this work from NIH/NIAMS and the Rheumatology Research Foundation; she has received honoraria from Osteoarthritis and Cartilage and Nestle Health. The other authors report no competing interests.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare no competing interests.
Human and Animal Rights and Informed Consent
All reported studies/experiments with human or animal subjects performed by the authors have been previously published and complied with all applicable ethical standards (including the Helsinki declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines).
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Arbeeva, L., Minnig, M.C., Yates, K.A. et al. Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes. Curr Rheumatol Rep 25, 213–225 (2023). https://doi.org/10.1007/s11926-023-01114-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11926-023-01114-9