title: Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps.
abstract: 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.
authors.affiliation: Department of Computer Science, University of Copenhagen, Denmark; Biomediq A/S, Copenhagen, Denmark. drj@biomediq.com
authors.forename: Dan R
authors.initials: DR
authors.lastname: Jørgensen
authors.forename: Erik B
authors.initials: EB
authors.lastname: Dam
authors.forename: Martin
authors.initials: M
authors.lastname: Lillholm
mesh_terms: D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D002358:Cartilage, Articular / Q000473:pathology*; D003198:Computer Simulation; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007719:Knee Joint / Q000473:pathology*; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests