N Lazzarini1, J Runhaar2, A C Bay-Jensen3, C S Thudium3, S M A Bierma-Zeinstra4, Y Henrotin5, J Bacardit6. 1. ICOS Research Group, School of Computing, Newcastle University, UK; D-BOARD Consortium, An FP7 Programme By the European Committee. 2. D-BOARD Consortium, An FP7 Programme By the European Committee; Erasmus University Medical Center Rotterdam, the Netherlands, Dept. of General Practice. 3. D-BOARD Consortium, An FP7 Programme By the European Committee; Nordic Bioscience, Copenhagen, Denmark. 4. D-BOARD Consortium, An FP7 Programme By the European Committee; Erasmus University Medical Center Rotterdam, the Netherlands, Dept. of General Practice; Erasmus University Medical Center Rotterdam, the Netherlands, Dept. of Orthopedics. 5. D-BOARD Consortium, An FP7 Programme By the European Committee; University of Liège, Belgium; Artialis SA, Liège, Belgium. 6. ICOS Research Group, School of Computing, Newcastle University, UK; D-BOARD Consortium, An FP7 Programme By the European Committee. Electronic address: jaume.bacardit@newcastle.ac.uk.
Abstract
OBJECTIVE: Knee osteoarthritis (OA) is among the higher contributors to global disability. Despite its high prevalence, currently, there is no cure for this disease. Furthermore, the available diagnostic approaches have large precision errors and low sensitivity. Therefore, there is a need for new biomarkers to correctly identify early knee OA. METHOD: We have created an analytics pipeline based on machine learning to identify small models (having few variables) that predict the 30-months incidence of knee OA (using multiple clinical and structural OA outcome measures) in overweight middle-aged women without knee OA at baseline. The data included clinical variables, food and pain questionnaires, biochemical markers (BM) and imaging-based information. RESULTS: All the models showed high performance (AUC > 0.7) while using only a few variables. We identified both the importance of each variable within the models as well its direction. Finally, we compared the performance of two models with the state-of-the-art approaches available in the literature. CONCLUSIONS: We showed the potential of applying machine learning to generate predictive models for the knee OA incidence. Imaging-based information were found particularly important in the proposed models. Furthermore, our analysis confirmed the relevance of known BM for knee OA. Overall, we propose five highly predictive small models that can be possibly adopted for an early prediction of knee OA.
OBJECTIVE:Knee osteoarthritis (OA) is among the higher contributors to global disability. Despite its high prevalence, currently, there is no cure for this disease. Furthermore, the available diagnostic approaches have large precision errors and low sensitivity. Therefore, there is a need for new biomarkers to correctly identify early knee OA. METHOD: We have created an analytics pipeline based on machine learning to identify small models (having few variables) that predict the 30-months incidence of knee OA (using multiple clinical and structural OA outcome measures) in overweight middle-aged women without knee OA at baseline. The data included clinical variables, food and pain questionnaires, biochemical markers (BM) and imaging-based information. RESULTS: All the models showed high performance (AUC > 0.7) while using only a few variables. We identified both the importance of each variable within the models as well its direction. Finally, we compared the performance of two models with the state-of-the-art approaches available in the literature. CONCLUSIONS: We showed the potential of applying machine learning to generate predictive models for the knee OA incidence. Imaging-based information were found particularly important in the proposed models. Furthermore, our analysis confirmed the relevance of known BM for knee OA. Overall, we propose five highly predictive small models that can be possibly adopted for an early prediction of knee OA.
Authors: B Guan; F Liu; A Haj-Mirzaian; S Demehri; A Samsonov; T Neogi; A Guermazi; R Kijowski Journal: Osteoarthritis Cartilage Date: 2020-02-06 Impact factor: 6.576
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Authors: Paweł Widera; Paco M J Welsing; Christoph Ladel; John Loughlin; Floris P F J Lafeber; Florence Petit Dop; Jonathan Larkin; Harrie Weinans; Ali Mobasheri; Jaume Bacardit Journal: Sci Rep Date: 2020-05-21 Impact factor: 4.379
Authors: Jonas Bianchi; Antônio Carlos de Oliveira Ruellas; João Roberto Gonçalves; Beatriz Paniagua; Juan Carlos Prieto; Martin Styner; Tengfei Li; Hongtu Zhu; James Sugai; William Giannobile; Erika Benavides; Fabiana Soki; Marilia Yatabe; Lawrence Ashman; David Walker; Reza Soroushmehr; Kayvan Najarian; Lucia Helena Soares Cevidanes Journal: Sci Rep Date: 2020-05-15 Impact factor: 4.379