A pilot study of a machine-learning tool to assist in the diagnosis of hand arthritis (Preprint)

Abstract

Arthritis is a common condition, which frequently involves the hands. Patients with inflammatory arthritis have been shown to experience significant delays in diagnosis.

We sought to develop and test a screening tool combining an image of a patient’s hands, a short series of questions, and a single examination technique, to determine the most likely diagnosis in a patient presenting with hand arthritis. Machine learning techniques were used to develop separate algorithms for each component, which were combined to produce a diagnosis.

280 consecutive new patients presenting to a Rheumatology practice with hand arthritis were enrolled. Each patient completed a 9-part questionnaire, had photographs taken of each hand, and had a single examination result recorded. The Rheumatologist diagnosis was recorded following a 45-minute consultation. The photograph algorithm was developed from a library of 1000 images, and machine learning techniques were applied to the questionnaire results, training several models against the diagnosis from the Rheumatologist.

The combined algorithms in this study were able to predict inflammatory arthritis with an accuracy, precision, recall and specificity of 96·8%, 97·2%, 98·6% and 90·5% respectively. Similar results were found when inflammatory arthritis was subclassified into rheumatoid arthritis and psoriatic arthritis. The corresponding figures for osteoarthritis were 79·6%, 85·9%, 61·9% and 92·6%.

This study demonstrates a novel application of a combined image-processing and a patient questionnaire with applied machine-learning methods, to facilitate the diagnosis of patients presenting with hand arthritis. Preliminary results are encouraging for the application of such techniques in clinical practice.

Not applicable.