StemCell Therapy

Smolen, J. S., Aletaha, D. & McInnes, I. B. Rheumatoid arthritis. Lancet 388, 20232038 (2016).

CAS Article Google Scholar

Goekoop-Ruiterman, Y. P. & Huizinga, T. W. Rheumatoid arthritis: Can we achieve true drug-free remission in patients with RA?Nat. Rev. Rheumatol. 6, 6870 (2010).

Article Google Scholar

Aga, A. B. et al. Time trends in disease activity, response and remission rates in rheumatoid arthritis during the past decade: Results from the NOR-DMARD study 20002010. Ann. Rheum. Dis. 74, 381388 (2015).

CAS Article Google Scholar

van der Helm-van Mil, A. H. Risk estimation in rheumatoid arthritis: From bench to bedside. Nat. Rev. Rheumatol. 10, 171180 (2014).

Article Google Scholar

Ohrndorf, S. & Backhaus, M. Advances in sonographic scoring of rheumatoid arthritis. Ann. Rheum. Dis. 72, ii69ii75 (2013).

Article Google Scholar

Scir, C. A. et al. Ultrasonographic evaluation of joint involvement in early rheumatoid arthritis in clinical remission: Power Doppler signal predicts short-term relapse. Rheumatology (Oxford) 48, 10921097 (2009).

Article Google Scholar

Peluso, G. et al. Clinical and ultrasonographic remission determines different chances of relapse in early and long standing rheumatoid arthritis. Ann. Rheum. Dis. 70, 172175 (2011).

Article Google Scholar

Foltz, V. et al. Power Doppler ultrasound, but not low-field magnetic resonance imaging, predicts relapse and radiographic disease progression in rheumatoid arthritis patients with low levels of disease activity. Arthritis Rheum. 64, 6776 (2012).

Article Google Scholar

Iwamoto, T. et al. Prediction of relapse after discontinuation of biologic agents by ultrasonographic assessment in patients with rheumatoid arthritis in clinical remission: High predictive values of total gray-scale and power Doppler scores that represent residual synovial inflammation before discontinuation. Arthritis Care Res. 66, 15761581 (2014).

Article Google Scholar

Nguyen, H. et al. Prevalence of ultrasound-detected residual synovitis and risk of relapse and structural progression in rheumatoid arthritis patients in clinical remission: A systematic review and meta-analysis. Rheumatology (Oxford) 53, 21102118 (2014).

Article Google Scholar

Kawashiri, S. Y. et al. Ultrasound-detected bone erosion is a relapse risk factor after discontinuation of biologic disease-modifying antirheumatic drugs in patients with rheumatoid arthritis whose ultrasound power Doppler synovitis activity and clinical disease activity are well controlled. Arthritis Res. Ther. 19, 108 (2017).

Article Google Scholar

Matsuo, H. et al. Prediction of recurrence and remission using superb microvascular imaging in rheumatoid arthritis. J. Med. Ultrason. (2001)47, 131138 (2020).

Article Google Scholar

Matsuo, H. et al. Positive rate and prognostic significance of the superb microvascular imaging signal in joints of rheumatoid arthritis patients in remission with normal C-reactive protein levels and erythrocyte sedimentation rates. J. Med. Ultrason. (2001) 48, 353359 (2021).

Article Google Scholar

Ngiam, K. Y. & Khor, I. W. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 20, e262e273 (2019).

Article Google Scholar

Goecks, J., Jalili, V., Heiser, L. M. & Gray, J. W. How machine learning will transform biomedicine. Cell 181, 92101 (2020).

CAS Article Google Scholar

Kingsmore, K. M., Puglisi, C. E., Grammer, A. C. & Lipsky, P. E. An introduction to machine learning and analysis of its use in rheumatic diseases. Nat. Rev. Rheumatol. 17, 710730 (2021).

Article Google Scholar

Stafford, I. S. et al. A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ Digit. Med. 3, 30 (2020).

CAS Article Google Scholar

Luque-Tvar, M. et al. Integrative clinical, molecular, and computational analysis identify novel biomarkers and differential profiles of anti-TNF response in rheumatoid arthritis. Front. Immunol. 12, 631662 (2021).

Article Google Scholar

Kalweit, M. et al. Personalized prediction of disease activity in patients with rheumatoid arthritis using an adaptive deep neural network. PLoSOne 16, e0252289 (2021).

CAS Article Google Scholar

Yoosuf, N. et al. Early prediction of clinical response to anti-TNF treatment using multi-omics and machine learning in rheumatoid arthritis. Rheumatology (Oxford) https://doi.org/10.1093/rheumatology/keab521 (2021).

Article Google Scholar

Vodencarevic, A. et al. Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs. Arthritis Res. Ther. 23, 67 (2021).

CAS Article Google Scholar

Koo, B. S. et al. Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics. Arthritis Res. Ther. 23, 178 (2021).

CAS Article Google Scholar

Johansson, F. D. et al. Predicting response to tocilizumab monotherapy in rheumatoid arthritis: A real-world data analysis using machine learning. J. Rheumatol. 48, 13641370 (2021).

CAS Article Google Scholar

van der Maaten, L. J. P. & Hinton, G. E. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 25792605 (2008).

MATH Google Scholar

Karlsson Sundbaum, J. et al. Methotrexate treatment in rheumatoid arthritis and elevated liver enzymes: A long-term follow-up of predictors, surveillance, and outcome in clinical practice. Int. J. Rheum. Dis. 22, 12261232 (2019).

CAS Article Google Scholar

Chen, Y., Yu, Z., Packham, J. C. & Mattey, D. L. Influence of adult height on rheumatoid arthritis: Association with disease activity, impairment of joint function and overall disability. PLoSOne 8, e64862 (2013).

ADS Article Google Scholar

Zhao, Y. et al. Ensemble learning predicts multiple sclerosis disease course in the SUMMIT study. NPJ Digit. Med. 3, 135 (2020).

Article Google Scholar

Morid, M. A., Lau, M. & Del Fiol, G. Predictive analytics for step-up therapy: Supervised or semi-supervised learning?. J. Biomed. Inform. 119, 103842 (2021).

Article Google Scholar

Fiorentino, M. C. et al. A deep-learning framework for metacarpal-head cartilage-thickness estimation in ultrasound rheumatological images. Comput. Biol. Med. 141, 105117 (2022).

Article Google Scholar

Rohrbach, J., Reinhard, T., Sick, T. & Drr, O. Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks. Comput. Electr. Eng. 78, 472481 (2019).

Article Google Scholar

Naredo, E. et al. Ultrasound joint inflammation in rheumatoid arthritis in clinical remission: How many and which joints should be assessed?. Arthritis Care Res. (Hoboken) 65, 512517 (2013).

Article Google Scholar

Backhaus, M. et al. Guidelines for musculoskeletal ultrasound in rheumatology. Ann. Rheum. Dis. 60, 641649 (2001).

CAS Article Google Scholar

Szkudlarek, M. et al. Interobserver agreement in ultrasonography of the finger and toe joints in rheumatoid arthritis. Arthritis Rheum. 48, 955962 (2003).

Article Google Scholar

Breiman, L. Random forests. Mach. Learn. 45, 532 (2001).

Article Google Scholar

Chen, T. & Carlos, G. XGBoost: A Scalable Tree Boosting System. KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785794. https://doi.org/10.1145/2939672.2939785 (2016).

See the article here:
Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test | Scientific Reports...

This entry was posted on May 8, 2022 at 1:56 am and is filed under Arthritis. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed. |