Radiographic Bone Texture Analysis Using Deep Learning Models for Early Rheumatoid Arthritis Diagnosis

Abstract BackgroundRheumatoid arthritis (RA) is characterized by altered bone microarchitecture (radiographically referred to as ‘texture’) of periarticular regions. We hypothesize that deep learning models can quantify periarticular texture changes to aid in the classification of early RA.MethodsThe second, third, and fourth distal metacarpal areas from hand radiographs of 892 early RA and 1236 non-RA patients were segmented for the Deep Texture Encoding Network (Deep-TEN; texture-based) and residual network-50 (ResNet-50; texture and structure-based) models to predict the probability of RA. The performances were measured using the area under the curve of the receiver operating characteristics curve (AUROC). Multivariate logistic regression was used to estimate the odds ratio (OR) with 95% confidence intervals (CIs) for RA.ResultsThe AUROC for RA was 0.69 for the Deep-TEN and 0.73 for the ResNet-50 model. The positive predictive values of a high texture score to classify RA using the Deep-TEN and ResNet-50 models were 0.64 and 0.67, respectively. High mean texture scores were associated with age- and sex-adjusted ORs (95% CI) for RA of 3.42 (2.59–4.50) and 4.30 (3.26–5.69) using the Deep-TEN and ResNet-50 models, respectively. The moderate and high RA risk groups determined by the Deep-TEN model were associated with adjusted ORs (95% CIs) of 2.48 (1.78–3.47) and 4.39 (3.11–6.20) for RA, respectively, and those using the ResNet-50 model were 2.17 (1.55–3.04) and 6.91 (4.83–9.90), respectively.ConclusionFully automated quantitative assessment for periarticular texture by deep learning models can help the classification of early RA.