Pilot study of a machine-learning tool to assist in the diagnosis of hand arthritis.

BACKGROUND: Arthritis is a common condition, which frequently involves the hands. Patients with inflammatory arthritis have been shown to experience significant delays in diagnosis. AIM: 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. METHODS: A total of 280 consecutive new patients presenting to a rheumatology practice with hand arthritis were enrolled. Each patient completed a nine-part questionnaire, had photographs taken of each hand and had a single examination result recorded. The rheumatologist diagnosis was recorded following a 45-min consultation. The photograph algorithm was developed from 1000 previous hand images and machine learning techniques were applied to the questionnaire results, training several models against the diagnosis from the rheumatologist. RESULTS: The combined algorithms in the present 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%. CONCLUSION: The present study demonstrates a novel application combining 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.

A multicentre validation study of a smartphone application to screen hand arthritis.

BACKGROUND: Arthritis is a common condition, and the prompt and accurate assessment of hand arthritis in primary care is an area of unmet clinical need. We have previously developed and tested a screening tool combining machine-learning algorithms, to help primary care physicians assess patients presenting with arthritis affecting the hands. The aim of this study was to assess the validity of the screening tool among a number of different Rheumatologists. METHODS: Two hundred and forty-eight consecutive new patients presenting to 7 private Rheumatology practices across Australia were enrolled. Using a smartphone application, each patient had photographs taken of their hands, completed a brief 9-part questionnaire, and had a single examination result (wrist irritability) recorded. The Rheumatologist diagnosis was entered following a 45-minute consultation. Multiple machine learning models were applied to both the photographic and survey/examination results, to generate a screening outcome for the primary diagnoses of osteoarthritis, rheumatoid and psoriatic arthritis. RESULTS: The combined algorithms in the application performed well in identifying and discriminating between different forms of hand arthritis. The algorithms were able to predict rheumatoid arthritis with accuracy, precision, recall and specificity of 85.1, 80.0, 88.1 and 82.7% respectively. The corresponding results for psoriatic arthritis were 95.2, 76.9, 90.9 and 95.8%, and for osteoarthritis were 77.4, 78.3, 80.6 and 73.7%. The results were maintained when each contributor was excluded from the analysis. The median time to capture all data across the group was 2 minutes and 59 seconds. CONCLUSIONS: This multicentre study confirms the results of the pilot study, and indicates that the performance of the screening tool is maintained across a group of different Rheumatologists. The smartphone application can provide a screening result from a combination of machine-learning algorithms applied to hand images and patient symptom responses. This could be used to assist primary care physicians in the assessment of patients presenting with hand arthritis, and has the potential to improve the clinical assessment and management of such patients.