Prediction of total knee replacement using deep learning analysis of knee MRI

[1]  Yuanzhi Li,et al.  Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning , 2020, ICLR.

[2]  Jin Hyung Lee,et al.  Diagnostic Accuracy of Quantitative Multi-Contrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement. , 2020, AJR. American journal of roentgenology.

[3]  Krzysztof J. Geras,et al.  Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative. , 2020, Radiology.

[4]  Su Ruan,et al.  A Multi-Modality Fusion Network Based on Attention Mechanism for Brain Tumor Segmentation , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[5]  Sharmila Majumdar,et al.  Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images , 2020, Scientific Reports.

[6]  D. Felson,et al.  Assessment of knee pain from MR imaging using a convolutional Siamese network , 2020, European Radiology.

[7]  M. Nevitt,et al.  Tool for osteoarthritis risk prediction (TOARP) over 8 years using baseline clinical data, X‐ray, and MRI: Data from the osteoarthritis initiative , 2018, Journal of magnetic resonance imaging : JMRI.

[8]  N. Egund,et al.  Risk factors for joint replacement in knee osteoarthritis; a 15-year follow-up study , 2017, BMC Musculoskeletal Disorders.

[9]  L. Sharma,et al.  Knee tissue lesions and prediction of incident knee osteoarthritis over 7 years in a cohort of persons at higher risk. , 2017, Osteoarthritis and cartilage.

[10]  M. Karsdal,et al.  Disease-modifying treatments for osteoarthritis (DMOADs) of the knee and hip: lessons learned from failures and opportunities for the future. , 2016, Osteoarthritis and cartilage.

[11]  C. Kwoh,et al.  Determinants of patient preferences for total knee replacement: African-Americans and whites , 2015, Arthritis Research & Therapy.

[12]  N. Hafezi-Nejad,et al.  Predictive value of semi-quantitative MRI-based scoring systems for future knee replacement: data from the osteoarthritis initiative , 2015, Skeletal Radiology.

[13]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[14]  A David Paltiel,et al.  Lifetime Medical Costs of Knee Osteoarthritis Management in the United States: Impact of Extending Indications for Total Knee Arthroplasty , 2015, Arthritis care & research.

[15]  D. Felson,et al.  Identifying and treating preclinical and early osteoarthritis. , 2014, Rheumatic diseases clinics of North America.

[16]  J. Sorkin,et al.  Quality of life and radiographic severity of knee osteoarthritis predict total knee arthroplasty: data from the osteoarthritis initiative , 2013 .

[17]  R. Boudreau,et al.  Evolution of semi-quantitative whole joint assessment of knee OA: MOAKS (MRI Osteoarthritis Knee Score). , 2011, Osteoarthritis and cartilage.

[18]  Johanne Martel-Pelletier,et al.  Risk factors predictive of joint replacement in a 2-year multicentre clinical trial in knee osteoarthritis using MRI: results from over 6 years of observation , 2011, Annals of the rheumatic diseases.

[19]  D. J. Hunter,et al.  Systematic review of the concurrent and predictive validity of MRI biomarkers in OA. , 2011, Osteoarthritis and cartilage.

[20]  A. Guermazi,et al.  Anatomical distribution of synovitis in knee osteoarthritis and its association with joint effusion assessed on non-enhanced and contrast-enhanced MRI. , 2010, Osteoarthritis and cartilage.

[21]  D J Hunter,et al.  Risk stratification for knee osteoarthritis progression: a narrative review. , 2009, Osteoarthritis and cartilage.

[22]  Erika Schneider,et al.  The osteoarthritis initiative: report on the design rationale for the magnetic resonance imaging protocol for the knee. , 2008, Osteoarthritis and cartilage.

[23]  G. Lester,et al.  Clinical research in OA--the NIH Osteoarthritis Initiative. , 2008, Journal of musculoskeletal & neuronal interactions.

[24]  J. Craig,et al.  Bone marrow edema in the knee in osteoarthrosis and association with total knee arthroplasty within a three-year follow-up , 2008, Skeletal Radiology.

[25]  A. Astrup,et al.  Effect of weight reduction in obese patients diagnosed with knee osteoarthritis: a systematic review and meta-analysis , 2006, Annals of the rheumatic diseases.

[26]  Roy D. Altman,et al.  Workshop for Consensus on Osteoarthritis Imaging: MRI of the knee , 2006 .

[27]  M. Doherty,et al.  Aerobic walking or strengthening exercise for osteoarthritis of the knee? A systematic review , 2005, Annals of the rheumatic diseases.

[28]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[29]  B. Beynnon,et al.  Knee Injury and Osteoarthritis Outcome Score (KOOS)--development of a self-administered outcome measure. , 1998, The Journal of orthopaedic and sports physical therapy.

[30]  D. Felson,et al.  An update on the epidemiology of knee and hip osteoarthritis with a view to prevention. , 1998, Arthritis and rheumatism.

[31]  Anders Krogh,et al.  Learning with ensembles: How overfitting can be useful , 1995, NIPS.

[32]  C. Goldsmith,et al.  Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. , 1988, The Journal of rheumatology.

[33]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[34]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[35]  W. Youden,et al.  Index for rating diagnostic tests , 1950, Cancer.