Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging

Abstract

Objective The purpose of this study is to evaluate the ability of machine learning to discriminate between magnetic resonance images (MRI) of normal and pathological human articular cartilage obtained under standard clinical conditions. Method An approach to MRI classification of cartilage degradation is proposed using pattern recognition and multivariable regression in which image features from MRIs of histologically scored human articular cartilage plugs were computed using weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). The WND-CHRM method was first applied to several clinically available MRI scan types to perform binary classification of normal and osteoarthritic osteochondral plugs based on the OARSI histological system. In addition, the image features computed from WND-CHRM were used to develop a multiple linear least-squares regression model for classification and prediction of an OARSI score for each cartilage plug. Results The binary classification of normal and osteoarthritic plugs yielded results of limited quality with accuracies between 36% and 70%. However, multiple linear least-squares regression successfully predicted OARSI scores and classified plugs with accuracies as high as 86%. The present results improve upon the previously-reported accuracy of classification using average MRI signal intensities and parameter values. Conclusion MRI features detected by WND-CHRM reflect cartilage degradation status as assessed by OARSI histologic grading. WND-CHRM is therefore of potential use in the clinical detection and grading of osteoarthritis.

Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative

“…We note that in our previous study, we used WND‐CHRM features computed on MRIs of osteochondral plugs to construct a multiple linear regression model for classification based on OARSI score . We found that the accuracy of binary classification based on several MR measurements, including the diffusion weighted image with b = 999 s/mm 2 and the T 2 W image with TE = 50 ms, improved through use of regression, which establishes relationships throughout the entire dataset for classification of single samples.…”

“…The features computed from WND‐CHRM were used to develop a multivariable least‐squares regression model to predict OARSI scores and classify OA cartilage with up to 86% accuracy in various MR contrast modalities. Our previous in vitro results indicate the ability to non‐invasively assign individual subjects to a degree of OA pathology . One essential difference between this previous study of explants and analyses performed in the in vivo setting is the requirement for reproducible tissue segmentation in the latter.…”

“…Magnetic resonance imaging (MRI) is a noninvasive and multi‐contrast modality that can detect subtle morphologic changes in articular cartilage and is an effective tool for diagnosing established OA . Computer‐aided diagnostic tools have the potential to assess earlier disease status and are currently the focus of significant MRI research . Specifically, image content descriptors (features) have been shown to be informative in detecting subtle differences between textures and intensities between healthy and pathological cartilage in both the in vitro and in vivo setting …”

“…3,4 Computer-aided diagnostic tools have the potential to assess earlier disease status and are currently the focus of significant MRI research. [5][6][7][8] Specifically, image content descriptors (features) have been shown to be informative in detecting subtle differences between textures and intensities between healthy and pathological cartilage in both the in vitro and in vivo setting. 6,8 Recently, we evaluated the ability for a machine learning algorithm, weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM), 9 to classify MRIs of human osteochondral plugs of normal and pathologic cartilage based on Osteoarthritis Research Society International (OARSI) histological scores.…”

“…[5][6][7][8] Specifically, image content descriptors (features) have been shown to be informative in detecting subtle differences between textures and intensities between healthy and pathological cartilage in both the in vitro and in vivo setting. 6,8 Recently, we evaluated the ability for a machine learning algorithm, weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM), 9 to classify MRIs of human osteochondral plugs of normal and pathologic cartilage based on Osteoarthritis Research Society International (OARSI) histological scores. 8,10 The features computed from WND-CHRM were used to develop a multivari-able least-squares regression model to predict OARSI scores and classify OA cartilage with up to 86% accuracy in various MR contrast modalities.…”