Machine learning to predict incident radiographic knee osteoarthritis over 8 Years using combined MR imaging features, demographics, and clinical factors: data from the Osteoarthritis Initiative.

ABSTRACT:

Objective

To develop a machine learning-based prediction model for incident radiographic osteoarthritis (OA) of the knee over 8 years using MRI-based cartilage biochemical composition and knee joint structure, demographics, and clinical predictors including muscle strength and symptoms.

Design

Individuals (n = 1,044) with baseline Kellgren Lawrence (KL) grade 0-1 in the right knee from the Osteoarthritis Initiative database were analyzed. 3T MRI at baseline was used to quantify knee cartilage T₂, and Whole-Organ Magnetic Resonance Imaging Scores (WORMS) were obtained for cartilage, meniscus, and bone marrow. The outcome was set as true if a subject developed KL grade 2-4 OA in the right knee over 8 years (n = 183) and false if the subject remained at KL 0-1 over 8

SUBMITTER: Joseph GB 

PROVIDER: S-EPMC8792367 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature