BACKGROUND
Rheumatoid arthritis (RA) is distinguished by the presence of modified bone microarchitecture, also known as 'texture,' in the periarticular regions. The radiographic detection of such alterations in RA can be challenging.
OBJECTIVE
To train and to validate a deep learning model to quantitatively produce periarticular texture features di-rectly from radiography and predict the diagnosis of early RA without human reading. Two kinds of deep learning models were compared for diagnostic performance.
METHODS
Anterior-posterior bilateral hands radiographs of 891 early RA (within one year of initial diagno-sis) and 1237 non-RA patients were split into a training set (64%), a validation set (16%), and a test set (20%). The second, third, and fourth distal metacarpal areas were segmented for the Deep Texture Encod-ing Network (Deep-TEN; texture-based) and residual network-50 (ResNet-50; texture and structure-based) models to predict the probability of RA.
RESULTS
The area under the curve of the receiver operating characteristics curve for RA was 0.69 for the Deep-TEN model 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 tex-ture scores were associated with age- and sex-adjusted odds ratios (ORs) with 95% confidence interval (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.
CONCLUSIONS
Fully automated quantitative assessment for periarticular texture by deep learning models can help in the classification of early RA.