Digital skin imaging applications, part I: Assessment of image acquisition technique features

Abstract

Background:The rapid adoption of digital skin imaging applications has increased the utilization of smartphone-acquired images in dermatology. While this has enormous potential for scaling the assessment of concerning skin lesions, the insufficient quality of many consumer/patient-taken images can undermine clinical accuracy and potentially harm patients due to lack of diagnostic interpretability. We aim to characterize the current state of digital skin imaging applications and comprehensively assess how image acquisition features address image quality. Materials and methods:Publicly discoverable mobile, web, and desktop-based skin imaging applications, identified through keyword searches in mobile app stores, Google Search queries, previous teledermatology studies, and expert recommendations were independently assessed by three reviewers. Applications were categorized by primary audience (consumer-facing, nonhospital-based practice, or enterprise/health system), function (education, store-and-forward teledermatology, live-interactive teledermatology, electronic medical record adjunct/clinical imaging storage, or clinical triage), inapp connection to a healthcare provider (yes or no), and user type (patient, provider, or both).Results: Just over half (57%) of 191 included skin imaging applications had at least one of 14 image acquisition technique features. Those that were consumer-facing, intended for educational use, and designed for both patient and physician users had significantly greater feature richness (p < 0.05). The most common feature was the inclusion of text-based imaging tips, followed by the requirement to submit multiple images and body area matching.

Advancing Dermatological Care: A Comprehensive Narrative Review of Tele-Dermatology and mHealth for Bridging Gaps and Expanding Opportunities beyond the COVID-19 Pandemic

“…Nine reviews [22][23][24][25][28][29][30]36,38] and one systematic review [32]; • Twenty-five papers [21,26,27,31,[33][34][35][40][41][42][43][44][45]48,[50][51][52][53][54][55][56][57][58][59][60].…”

“…The search provided 40 components [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ] in the three years since the COVID-19 pandemic outbreak (at the date of this study).…”

“…In detail, the components were: Two randomized controlled (clinical) trials (RCTs) [ 37 , 49 ]; Three observational studies (OS)s [ 39 , 46 , 47 ]; Nine reviews [ 22 , 23 , 24 , 25 , 28 , 29 , 30 , 36 , 38 ] and one systematic review [ 32 ]; Twenty-five papers [ 21 , 26 , 27 , 31 , 33 , 34 , 35 , 40 , 41 , 42 , 43 , 44 , 45 , 48 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. …”

“…In general, more domains of intervention are emerging in this area, ranging from technologies to ethical aspects. Sun et al [33,40], for example, proposed a study divided into two parts which addressed the critical domain of standardization from a global point of view. The take-home messages were that the image post-processing capabilities vary greatly based on the us-er and the intended function; technological standards were not always implemented or reported; and critical issues were also detected in the consent procedure and in other aspects of privacy and data confidentiality which were either not present or not standardized.…”

Artificial intelligence and skin cancer

“…Moreover, a study done in United Kingdom primary care facilities showed enhanced photo quality when patients were educated with the “4 Key Instructions” (Framing—requesting at least one near and one distant image; Flash—educating about the use of flash to enhance image sharpness, emphasizing not to use it too closely; Focus—educating patients to give the camera time to auto-focus; Scale—asking for a comparison like a ruler or a coin) ( 73 ). Among 191 digital applications for skin imaging, 57% included one or more strategies to enhance quality, but it was rare for applications to have more than one ( 80 ). An immediate feedback feature for image quality shows promise, although it is still in the early stages of development ( 81 ).…”