Tiulpin, A., Klein, S., Bierma-Zeinstra, S.M.A. et al. Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data. Sci Rep 9, 20038 (2019). https://doi.org/10.1038/s41598-019-56527-3
Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data
|Author:||Tiulpin, Aleksei1,2; Klein, Stefan3; Bierma-Zeinstra, Sita M. A.4,5;|
1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
2Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
3Biomedical Imaging Group Rotterdam, Depts. of Medical Informatics & Radiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
4Department of General Practice, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
5Department of Orthopedics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
6Department of Signal Processing, Tampere University of Technology, Tampere, Finland
7Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
8Department of Radiology & Nuclear Medicine, University Medical Center Rotterdam, Rotterdam, The Netherlands
|Online Access:||PDF Full Text (PDF, 2.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202003057323
|Publish Date:|| 2020-03-05
Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilises raw radiographic data, clinical examination results and previous medical history of the patient. We validated this approach on an independent test set of 3,918 knee images from 2,129 subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78–0.81) and Average Precision (AP) of 0.68 (0.66–0.70). In contrast, a reference approach, based on logistic regression, yielded AUC of 0.75 (0.74–0.77) and AP of 0.62 (0.60–0.64). The proposed method could significantly improve the subject selection process for OA drug-development trials and help the development of personalised therapeutic plans.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
The OAI is a public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. MOST is comprised of four cooperative grants (Felson - AG18820; Torner - AG18832; Lewis - AG18947; and Nevitt - AG19069) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by MOST study investigators. This manuscript was prepared using MOST data and does not necessarily reflect the opinions or views of MOST investigators. We would like to acknowledge the strategic funding of the University of Oulu, Infotech Oulu, KAUTE foundation and Sigrid Juselius Foundation for supporting this work. Dr. Claudia Lindner is acknowledged for providing BoneFinder and Egor Panfilov is acknowledged for proof-reading of the manuscript.
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