Development of a Checklist Tool to Assess the Quality of Skin Lesion Images Acquired by Consumers Using Sequential Mobile Teledermoscopy.

BACKGROUND

Mobile teledermoscopy is an emerging technology that involves imaging and digitally sending dermoscopic images of skin lesions to a clinician for assessment. High-quality, consistent images are required for accurate telediagnoses when monitoring lesions over time. To date there are no tools to assess the quality of sequential images taken by consumers using mobile teledermoscopy. The purpose of this study was to develop a tool to assess the quality of images acquired by consumers.

METHODS

Participants imaged skin lesions that they felt were concerning at baseline, 1-, and 2-months. A checklist to assess the quality of consumer sequential imaging of skin lesions was developed based on the International Skin Imaging Collaboration guidelines. A scale was implemented to grade the quality of the images: 0 (low) to 18 (very high). Intra- and inter-reliability of the checklist was assessed using Bland-Altman analysis. Using this checklist, the consistency with which 85 sets of images were scored by 2 evaluators were compared using Kappa statistics. Items with a low Kappa value <0.4 were removed.

RESULTS

After reliability testing, 5 of the items were removed due to low Kappa values (<0.4) and the final checklist included 13 items surveying: lesion selection; image orientation; lighting; field of view; focus and depth of view. Participants had a mean age of 41 years (range 19-73), and 67% were female. Most participants (84%, n = 71/85) were able to select and image the correct lesion over time for both the dermoscopic and overview images. Younger participants (<40 years old) scored significantly higher (8.1 ± 2.1) on the imaging checklist compared to older participants (7.1 ± 2.4; p = 0.037). Participants had most difficulty with consistent image orientation.

CONCLUSIONS

This checklist could be used as a triage tool to filter images acquired by consumers prior to telediagnosis evaluation, which would improve the efficiency and accuracy of teledermatology and teledermoscopy processes. It may also be used to provide feedback to the consumers to improve image acquisition over time.