Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis.

ABSTRACT:

Background

To develop and assess the performance of machine learning (ML) models based on magnetic resonance imaging (MRI) radiomics analysis for knee osteoarthritis (KOA) diagnosis.

Methods

This retrospective study analysed 148 consecutive patients (72 with KOA and 76 without) with available MRI image data, where radiomics features in cartilage portions were extracted and then filtered. Intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features, and a threshold of 0.8 was set. The training and validation cohorts consisted of 117 and 31 cases, respectively. Least absolute shrinkage and selection operator (LASSO) regression method was employed for feature selection. The ML classifiers were logistic regression (LR), K-nearest neighbour

SUBMITTER: Cui T 

PROVIDER: S-EPMC10199595 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature