The following is a summary of “Development of a measuring app for systemic sclerosis-related digital ulceration (SALVE: Scleroderma App for Lesion VErification),” published in the July 2024 issue of Rheumatology by Davison et al.
Researchers conducted a retrospective study evaluating whether daily photographs, alongside self-reported data, collected with a smartphone app by patients with systemic sclerosis (SSc) can serve as an objective outcome measure in clinical trials.
They created an app gathering images and patient-reported outcome measures (PROMs), including pain scores and the Hand Disability in Systemic Sclerosis-Digital Ulcers (HDISS-DU) questionnaire. Participants used the app to photograph the lesions daily for 30 days, uploading the images to a secure repository. A machine learning approach was used to analyze the lesions manually and automatically.
The result showed 25 patients with SSc-related digital lesions who consented; 19 completed the 30-day study, providing data from 27 lesions. Baseline Pain scores averaged 5.7 (SD 2.4), and the HDISS-DU score was 2.2 (SD 0.9), reflecting significant morbidity. A total of 506 images were analyzed, with each lesion contributing an average of 18.7 (SD 8.3). At day 1, mean lesion areas were 11.6 mm2 (SD 16.0) for manual and 13.9 mm2 (SD 16.7) for automated measurements. The manual lesion area decreased by 0.08 mm2 per day (2.4 mm2 over 30 days), while the automated area decreased by 0.1 mm2 per day (3.0 mm2 over 30 days). A strong correlation was observed (r = 0.81) between manual and automated measurements, averaging 40% lower manual values.
Investigators concluded that even patients with severe hand disability could effectively use the app, suggesting automated lesion measurement as a viable outcome measure in clinical trials.
