Abstract
Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways. As a result, no regulatory agency approved disease modifying OA drugs are available to date. Stratifying knees into MRI-based morphological phenotypes may provide insight into predicting future OA incidence, leading to improved inclusion criteria and efficacy of therapeutics. We trained convolutional neural networks to classify bone, meniscus/cartilage, inflammatory, and hypertrophy phenotypes in knee MRIs from participants in the Osteoarthritis Initiative (n = 4791). We investigated cross-sectional association between baseline morphological phenotypes and baseline structural OA (Kellgren Lawrence grade > 1) and symptomatic OA. Among participants without baseline OA, we evaluated association of baseline phenotypes with 48-month incidence of structural OA and symptomatic OA. The area under the curve of bone, meniscus/cartilage, inflammatory, and hypertrophy phenotype neural network classifiers was 0.89 ± 0.01, 0.93 ± 0.03, 0.96 ± 0.02, and 0.93 ± 0.02, respectively (mean ± standard deviation). Among those with no baseline OA, bone phenotype (OR: 2.99 (95%CI: 1.59–5.62)) and hypertrophy phenotype (OR: 5.80 (95%CI: 1.82–18.5)) each respectively increased odds of developing incident structural OA and symptomatic OA at 48 months. All phenotypes except meniscus/cartilage increased odds of undergoing total knee replacement within 96 months. Artificial intelligence can rapidly stratify knees into structural phenotypes associated with incident OA and total knee replacement, which may aid in stratifying patients for clinical trials of targeted therapeutics.
Highlights
Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways, a primary reason there are not yet regulatory agency approved disease modifying OA drugs (DMOADs) to date[1,2,3]
Rapid OsteoArthritis magnetic resonance imaging (MRI) Eligibility Score (ROAMES) was introduced to stratify knees into structural phenotypes representative of underlying pathophysiologic changes and simplify OA grading with MRI for largescale screening[10]
Larger cohort studies with MRI assessment may further demonstrate the prognostic value of morphological phenotypes in predicting incident OA
Summary
Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways, a primary reason there are not yet regulatory agency approved disease modifying OA drugs (DMOADs) to date[1,2,3]. Several studies have recruited large numbers of participants and collected magnetic resonance imaging (MRI) to investigate mechanisms of OA development and classify structural p henotypes[4,5,6]. Rapid OsteoArthritis MRI Eligibility Score (ROAMES) was introduced to stratify knees into structural phenotypes representative of underlying pathophysiologic changes and simplify OA grading with MRI for largescale screening[10]. Larger cohort studies with MRI assessment may further demonstrate the prognostic value of morphological phenotypes in predicting incident OA. ROAMES phenotypes are commonly seen in knees with OA; a large cohort study may corroborate the association between ROAMES phenotypes and incident OA in knees with pre-OA. Artificial intelligence may be applied to currently available large datasets of MRIs to associate morphological phenotypes with OA and future total knee replacement (TKR) surgery. We examined associations between phenotypes and undergoing TKR by 96 months from baseline
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