Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data

Aleksei Tiulpin, Stefan Klein, Sita M.A. Bierma-Zeinstra, Jérôme Thevenot, Esa Rahtu, Joyce van Meurs, Edwin H.G. Oei, Simo Saarakkala

Research output: Contribution to journalArticleScientificpeer-review

19 Citations (Scopus)
22 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number20038
JournalScientific Reports
Volume9
Issue number1
DOIs
Publication statusPublished - 1 Dec 2019
Publication typeA1 Journal article-refereed

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • General

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