How feasible is the stratification of osteoarthritis phenotypes by means of artificial intelligence?

Abstract

Osteoarthritis (OA) is a common and serious disease that involves all of the tissues of an affected joint (e.g., cartilage, bone, meniscus, tendon/ligament, synovium) and can affect one or multiple joints in an individual person, most often the finger joints, knees, hips, and spine [1]. OA is a major and growing contributor to disability worldwide and is associated with increased comorbidity and excess mortality [1]. Management of OA is focused on modestly effective lifestyle/behavioral interventions such as increased physical activity and weight loss, with pharmacologic therapies directed toward temporary symptomatic relief [2]. Although many clinical trials have been conducted, there are still no effective disease-modifying therapies, no proven way to prevent progression, and no cure. This is at least in part due to the lack of appreciation of, and accounting for, the heterogeneity of this complex disease in trials to date [3]. In general, most trials have enrolled all individuals with knee OA defined as the presence of symptoms (e.g., pain, aching, and stiffness) and moderate to severe radiographic change (e.g., osteophytes or joint space narrowing) in at least one knee. This does not account for the diverse mechanisms of disease development, which can be due to mechanical dysfunction, prior injury, metabolic factors, inflammation, or combinations of these. Nor does it address the diversity of presentations, burden of disease (i.e., number/severity of involved joints), chronicity, or numerous other aspects of the disease process in a given individual that may subsequently affect their response to the proposed therapy. This brief editorial review seeks to summarize recent work in the area of machine learning and osteoarthritis phenotyping.