Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Dan Richter Jørgensen, Erik Bjørnager Dam, Martin Lillholm

This study investigates whether measures of knee cartilage thickness can predict future loss of knee cartilage. A slow and a rapid progressor group was determined using longitudinal data, and anatomically aligned cartilage thickness maps were extracted from MRI at baseline. A novel machine learning framework was then trained using these maps. Compared to measures of mean cartilage plate thickness, group separation was increased by focusing on local cartilage differences. This result is central for clinical trials where inclusion of rapid progressors may help reduce the period needed to study effects of new disease-modifying drugs for osteoarthritis. (C) 2013 Elsevier Ltd. All rights reserved.
OriginalsprogEngelsk
TidsskriftComputers in Biology and Medicine
Vol/bind43
Udgave nummer8
Sider (fra-til)1045-1052
Antal sider8
ISSN0010-4825
DOI
StatusUdgivet - 2013

    Forskningsområder

  • Osteoarthritis, Knee MRI, Cartilage thickness, Clinical trials, Machine learning, Classification, Spatial data mining, Dimensionality reduction

ID: 119703441