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

Abstract

The purpose of this study is to evaluate the ability of a machine learning algorithm to classify in vivo magnetic resonance images (MRI) of human articular cartilage for development of osteoarthritis (OA). Sixty-eight subjects were selected from the Osteoarthritis Initiative (OAI) control and incidence cohorts. Progression to clinical OA was defined by the development of symptoms as quantified by the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire three years after baseline evaluation. Multi-slice T2–weighted knee images, obtained through the OAI, of these subjects were registered using a nonlinear image registration algorithm. T2 maps of cartilage from the central weight bearing slices of the medial femoral condyle were derived from the registered images using the multiple available echo times and were classified for “progression to symptomatic OA” using the machine learning tool, weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). WND-CHRM classified the isolated T2 maps for the progression to symptomatic OA with 75% accuracy. Statement of clinical significance Machine learning algorithms applied to T2 maps have the potential to provide important prognostic information for the development of OA.

Longitudinal validity of using digital hand photographs for assessing hand osteoarthritis progression over 7 years in community-dwelling older adults with hand pain

“…Therefore other imaging methods, although more expensive and less feasible in large populationbased studies, may have been more sensitive in determining structural change against which photographic change could have been compared. Additionally, other methods such as machine learning, which have been successfully applied to MRI images, may prove to be more sensitive in assessing hand photographs for the subtle differences that determines the development but also the progression of clinical features of OA (30).…”

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

“…One essential difference between our in vitro study of explants and analyses performed in the in vivo setting is the requirement for reproducible tissue segmentation in the latter. While challenging, great progress has been made in the registration and segmentation of joint cartilage in clinical images, with several automated methods having been developed 46–48 . These advances would permit WND-CHRM or related analyses to be performed on joint structures in addition to cartilage.…”

Automating Three-dimensional Osteoarthritis Histopathological Grading of Human Osteochondral Tissue using Machine Learning on Contrast-Enhanced Micro-Computed Tomography

“…Multiple 3D histopathological grading methods for different tissues have been proposed in the literature, based on magnetic resonance imaging (MRI) [12][13][14][15] , optical imaging 16 , ultrasound 17 , and atomic force microscopy 18 . 3D grading methods could possibly serve as a reference for clinical 3D modalities, as well as higher resolution 3D techniques.…”

“…The development of machine learning techniques has enabled a data-driven approach in pattern recognition and decision making without the need for explicit programming. Machine learning has been applied in clinical OA research in several domains, such as the prediction of OA severity [28][29][30][31] and progression 15,32,33 using X-ray radiographs 28,29,31,32 or MRI analysis 15,30,33 . However, little attention has been paid to machine learning in pre-clinical OA research 26,34,35 .…”