Development of a measuring app for systemic sclerosis-related digital ulceration (SALVE: Scleroderma App for Lesion...

To test the hypothesis that photographs (in addition to self-reported data) can be collected daily by patients with SSc using a smartphone app designed specifically for digital lesions, and could provide an objective outcome measure for use in clinical trials.

An app was developed to collect images and patient-reported outcome measures including Pain score and the Hand Disability in Systemic Sclerosis-Digital Ulcers (HDISS-DU) questionnaire. Participants photographed their lesion(s) each day for 30 days and uploaded images to a secure repository. Lesions were analysed both manually and automatically, using a machine learning approach.

Twenty-five patients with SSc-related digital lesions consented, of whom 19 completed the 30-day study, with evaluable data from 27 lesions. Mean (s.d.) baseline Pain score was 5.7 (2.4) and HDISS-DU 2.2 (0.9), indicating high lesion- and disease-related morbidity. A total of 506 images were used in the analysis [mean number of used images per lesion 18.7 (s.d. 8.3)]. Mean (s.d.) manual and automated lesion areas at day 1 were 11.6 (16.0) and 13.9 (16.7) mm2, respectively. Manual area decreased by 0.08 mm2 per day (2.4 mm2 over 30 days) and automated area by 0.1 mm2 (3.0 mm2 over 30 days). Average gradients of manual and automated measurements over 30 days correlated strongly (r = 0.81). Manual measurements were on average 40% lower than automated ones, with wide limits of agreement.

Even patients with significant hand disability were able to use the app. Automated measurement of finger lesions could be valuable as an outcome measure in clinical trials.