Modeling Interphalangeal Joints for Swelling Assessment in Psoriatic Arthritis via Smartphone Photographs

Digital assessment of swollen joints from smartphone photographs can support remote monitoring of patients with inflammatory arthritis, enabling more efficient care delivery. In this study, we propose a method to classify each interphalangeal joint in hand photographs as swollen or not. The classifier is a logistic regression model that uses joint effective width as the main predictor, with covariates including age, sex, body mass index, and the joint type. Effective width measures the thickness of a joint and is derived using computer vision algorithms that segment individual fingers, detect joint landmarks, and calculate distances between points of interest. We validated the model using a dataset collected from 85 individuals with psoriatic arthritis recruited between December 2024 and April 2025 across three countries. For each participant, the dataset includes paired photographs of both hands, captured by the participants themselves, along with demographic and clinical data reported by healthcare professionals. Model validation was performed using k-fold cross-validation, ensuring each validation fold contained exactly one patient with at least one swollen joint. The method achieved an average area under the ROC curve (AUROC) of 0.68 (SD 0.29). These findings demonstrate the potential of smartphone-based joint modeling as a scalable and patient-friendly approach to enhance remote assessment and personalized management of psoriatic arthritis.