Unusual suspects in hereditary melanoma: POT1, POLE, BAP1.

Systematic literature searches on POT1/POLE/BAP1 found that limited skin phenotypic characteristics have been documented in mutation carriers; 248 variants were annotated, and high-cluster variant regions associated with cutaneous melanoma were found in all three genes. Genotype-phenotype correlations can be used to identify patient disease predisposition based on mutation position and cluster regions.

POT1 and multiple primary melanomas: the dermatological phenotype.

POT1 is the second most frequently reported gene (after CDKN2A) in familial melanoma. Pathogenic variants are associated with earlier onset and/or multiple primary melanomas (MPMs). To date, POT1 phenotypical reports have been largely restricted to associated malignancies, and description of the dermatological landscape has been limited. We identified 10 variants in n=18 of 384 (4.7%) unrelated individuals (n=13 MPMs; n=5 single primary melanomas) of European ancestry. Five variants were rare (minor allele frequency <0.001) or novel (two loss-of-function (LOF), one splice acceptor and two missense) and were predicted to be functionally significant, in five unrelated probands with MPMs (≥3 melanomas). We performed three-dimensional total body photography on both individuals with confirmed pathogenic LOF variants to characterise the dermatological phenotype. Total body naevus counts (≥2 mm diameter) were significantly higher (p=7.72×10-12) in carriers compared with a control population. Majority of naevi were on the probands' back and lower limb regions, where only mild to moderate ultraviolet (UV) damage was observed. Conversely, the head/neck region, where both probands exhibited severe UV damage, had comparably fewer naevi. We hypothesise that carriage of functionally significant POT1 variants is associated with increased naevus counts generally, and naevi >5 mm in diameter specifically and the location of these are independent of UV damage.

Assessing the genetic risk of nodular melanoma using a candidate gene approach.

BACKGROUND: Nodular melanoma (NM) is a challenge to diagnose early due to its rapid growth and more atypical clinical presentation, making it the largest contributor to melanoma mortality. OBJECTIVES: Our study aim was to perform a rare-variant allele (RVA) analysis of whole-exome sequencing of patients with NM and non-NM (minor allele frequency ≤ 1% non-Finnish European) for a set of 500 candidate genes potentially implicated in melanoma. METHODS: This study recruited 131 participants with NM and 194 with non-NM from South-east Queensland and patients with NM from Victoria to perform a comparative analysis of possible genetic differences or similarities between the two melanoma cohorts. RESULTS: Phenotypic analysis revealed that a majority of patients diagnosed with NM were older males with a higher frequency of fair skin and red hair than is seen in the general population. The distribution of common melanoma polygenic risk scores was similar in patients with NM and non-NM, with over 28% in the highest quantile of scores. There was also a similar frequency of carriage of familial/high-penetrant melanoma gene and loss-of-function variants. We identified 39 genes by filtering 500 candidate genes based on the greatest frequency in NM compared with non-NM cases. The genes with RVAs of greatest frequency in NM included PTCH1, ARID2 and GHR. Rare variants in the SMO gene, which interacts with PTCH1 as ligand and receptor, were also identified, providing evidence that the Hedgehog pathway may contribute to NM risk. There was a cumulative effect in carrying multiple rare variants in the NM-associated genes. A 14.8-fold increased ratio for NM compared with non-NM was seen when two RVAs of the 39 genes were carried by a patient. CONCLUSIONS: This study highlights the importance of considering frequency of RVA to identify those at risk of NM in addition to known high penetrance genes.

Position statement of the EADV Artificial Intelligence (AI) Task Force on AI-assisted smartphone apps and web-based services for skin disease.

BACKGROUND: As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer. OBJECTIVE: This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI-assisted smartphone applications (apps) and web-based services for skin diseases with emphasis on skin cancer detection. METHODS: An initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance. RESULTS: Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non-medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web-based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users. CONCLUSIONS: The utilisation of AI-assisted smartphone apps and web-based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice.

Examining labelling guidelines for AI-based software as a medical device: A review and analysis of dermatology mobile applications in Australia.

In recent years, there has been a surge in the development of AI-based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately. However, guidelines for labelling AI-based SaMD in dermatology are lacking, which may result in products failing to provide essential information about algorithm development and performance metrics. This review examines existing labelling guidelines for AI-based SaMD across visual medical specialties, with a specific focus on dermatology. Common recommendations for labelling are identified and applied to currently available dermatology AI-based SaMD mobile applications to determine usage of these labels. Of the 21 AI-based SaMD mobile applications identified, none fully comply with common labelling recommendations. Results highlight the need for standardized labelling guidelines. Ensuring transparency and accessibility of information is essential for the safe integration of AI into health care and preventing potential risks associated with inaccurate clinical decisions.

Minimum labelling requirements for dermatology artificial intelligence-based Software as Medical Device (SaMD): A consensus statement.

BACKGROUND/OBJECTIVES: Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs. METHODS: Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the specific characteristics to include for some items. Consensus was achieved when more than 75% of the experts agreed that inclusion of information was necessary. RESULTS: There was robust consensus supporting inclusion of all proposed items as minimum labelling requirements; indication for use, intended user, training and test data sets, algorithm design, image processing techniques, clinical validation, performance metrics, limitations, updates and adverse events. Nearly all suggested characteristics of the labelling items received endorsement, except for some characteristics related to performance metrics. Moreover, there was consensus that uniform labelling criteria should apply across all AI categories and risk classes set out by the Therapeutic Goods Administration. CONCLUSIONS: This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested.

Models of communication for polygenic scores and associated psychosocial and behavioral effects on recipients: A systematic review.

PURPOSE: This study aimed to systematically review current models for communicating polygenic scores (PGS) and psycho-behavioral outcomes of receiving PGSs. METHODS: Original research on communicating PGSs and reporting on psycho-behavioral outcomes was included. Search terms were applied to 5 databases and were limited by date (2009-2021). RESULTS: In total, 28 articles, representing 17 studies in several disease settings were identified. There was limited consistency in PGS communication and evaluation/reporting of outcomes. Most studies (n = 14) presented risk in multiple ways (ie, numerically, verbally, and/or visually). Three studies provided personalized lifestyle advice and additional resources. Only 1 of 17 studies reported using behavior change theory to inform their PGS intervention. A total of 8 studies found no evidence of long-term negative psychosocial effects up to 12 months post result. Of 14 studies reporting on behavior, 9 found at least 1 favorable change after PGS receipt. When stratified by risk, 7 out of 9 studies found high PGS was associated with favorable changes including lifestyle, medication, and screening. Low-risk PGS was not associated with maladaptive behaviors (n = 4). CONCLUSION: PGS has the potential to benefit health behavior. High variability among studies emphasizes the need for developing standardized guidelines for communicating PGSs and evaluating psycho-behavioral outcomes. Our findings call for development of best communication practices and evidence-based interventions informed by behavior change theories.

The MC1R r allele does not increase melanoma risk in MITF E318K carriers.

BACKGROUND: Population-wide screening for melanoma is not cost-effective, but genetic characterization could facilitate risk stratification and targeted screening. Common Melanocortin-1 receptor (MC1R) red hair colour (RHC) variants and Microphthalmia-associated transcription factor (MITF) E318K separately confer moderate melanoma susceptibility, but their interactive effects are relatively unexplored. OBJECTIVES: To evaluate whether MC1R genotypes differentially affect melanoma risk in MITF E318K+ vs. E318K- individuals. MATERIALS AND METHODS: Melanoma status (affected or unaffected) and genotype data (MC1R and MITF E318K) were collated from research cohorts (five Australian and two European). In addition, RHC genotypes from E318K+ individuals with and without melanoma were extracted from databases (The Cancer Genome Atlas and Medical Genome Research Bank, respectively). χ2 and logistic regression were used to evaluate RHC allele and genotype frequencies within E318K+/- cohorts depending on melanoma status. Replication analysis was conducted on 200 000 general-population exomes (UK Biobank). RESULTS: The cohort comprised 1165 MITF E318K- and 322 E318K+ individuals. In E318K- cases MC1R R and r alleles increased melanoma risk relative to wild type (wt), P < 0.001 for both. Similarly, each MC1R RHC genotype (R/R, R/r, R/wt, r/r and r/wt) increased melanoma risk relative to wt/wt (P < 0.001 for all). In E318K+ cases, R alleles increased melanoma risk relative to the wt allele [odds ratio (OR) 2.04 (95% confidence interval 1.67-2.49); P = 0.01], while the r allele risk was comparable with the wt allele [OR 0.78 (0.54-1.14) vs. 1.00, respectively]. E318K+ cases with the r/r genotype had a lower but not significant melanoma risk relative to wt/wt [OR 0.52 (0.20-1.38)]. Within the E318K+ cohort, R genotypes (R/R, R/r and R/wt) conferred a significantly higher risk compared with non-R genotypes (r/r, r/wt and wt/wt) (P < 0.001). UK Biobank data supported our findings that r did not increase melanoma risk in E318K+ individuals. CONCLUSIONS: RHC alleles/genotypes modify melanoma risk differently in MITF E318K- and E318K+ individuals. Specifically, although all RHC alleles increase risk relative to wt in E318K- individuals, only MC1R R increases melanoma risk in E318K+ individuals. Importantly, in the E318K+ cohort the MC1R r allele risk is comparable with wt. These findings could inform counselling and management for MITF E318K+ individuals.

Artificial Intelligence for the Classification of Pigmented Skin Lesions in Populations with Skin of Color: A Systematic Review.

BACKGROUND: While skin cancers are less prevalent in people with skin of color, they are more often diagnosed at later stages and have a poorer prognosis. The use of artificial intelligence (AI) models can potentially improve early detection of skin cancers; however, the lack of skin color diversity in training datasets may only widen the pre-existing racial discrepancies in dermatology. OBJECTIVE: The aim of this study was to systematically review the technique, quality, accuracy, and implications of studies using AI models trained or tested in populations with skin of color for classification of pigmented skin lesions. METHODS: PubMed was used to identify any studies describing AI models for classification of pigmented skin lesions. Only studies that used training datasets with at least 10% of images from people with skin of color were eligible. Outcomes on study population, design of AI model, accuracy, and quality of the studies were reviewed. RESULTS: Twenty-two eligible articles were identified. The majority of studies were trained on datasets obtained from Chinese (7/22), Korean (5/22), and Japanese populations (3/22). Seven studies used diverse datasets containing Fitzpatrick skin type I-III in combination with at least 10% from black Americans, Native Americans, Pacific Islanders, or Fitzpatrick IV-VI. AI models producing binary outcomes (e.g., benign vs. malignant) reported an accuracy ranging from 70% to 99.7%. Accuracy of AI models reporting multiclass outcomes (e.g., specific lesion diagnosis) was lower, ranging from 43% to 93%. Reader studies, where dermatologists' classification is compared with AI model outcomes, reported similar accuracy in one study, higher AI accuracy in three studies, and higher clinician accuracy in two studies. A quality review revealed that dataset description and variety, benchmarking, public evaluation, and healthcare application were frequently not addressed. CONCLUSIONS: While this review provides promising evidence of accurate AI models in populations with skin of color, the majority of the studies reviewed were obtained from East Asian populations and therefore provide insufficient evidence to comment on the overall accuracy of AI models for darker skin types. Large discrepancies remain in the number of AI models developed in populations with skin of color (particularly Fitzpatrick type IV-VI) compared with those of largely European ancestry. A lack of publicly available datasets from diverse populations is likely a contributing factor, as is the inadequate reporting of patient-level metadata relating to skin color in training datasets.

Consumer perceptions on privacy and confidentiality in dermatology for 3D total-body imaging.

As 3D total-body photography for the early detection of melanoma is not yet widely used in clinical practice, we do not have a full understanding of patient's concerns about use, privacy and confidentiality, and if their concerns differ depending on the use-case. We conducted a virtual consumer forum to assess patients concerns about privacy and confidentiality in dermatology imaging for research, artificial intelligence development and for their own clinical care.

Informing a position statement on the use of artificial intelligence in dermatology in Australia.

Artificial Intelligence (AI) is the ability for computers to simulate human intelligence. In dermatology, there is substantial interest in using AI to identify skin lesions from images. Due to increasing research and interest in the use of AI, the Australasian College of Dermatologists has developed a position statement to inform its members of appropriate use of AI. This article presents the ACD Position Statement on the use of AI in dermatology, and provides explanatory information that was used to inform the development of this statement.

Patterns and cost of care according to keratinocyte cancer risk stratification in a volunteer population screening clinic: Real-world data from the TRoPICS study.

BACKGROUND: Risk prediction tools have been developed for keratinocyte cancers (KCs) to effectively categorize individuals with different levels of skin cancer burden. Few have been clinically validated nor routinely used in clinical settings. OBJECTIVES: To assess whether risk prediction tool categories associate with interventions including chemoprophylaxis for skin cancer, and health-care costs in a dermatologist-run screening clinic. METHODS: Adult participants who presented to a walk-in screening facility were invited to participate. A self-completed KC risk prediction tool was used to classify participants into one of the five risk categories. Participants subsequently underwent full skin examination by a dermatologist. Dermatological interventions and skin cancer-related medical prescriptions were documented. Total health-care costs, both to the health-care system and patients were evaluated. RESULTS: Of the 507 participants recruited, 5-fluorouracil cream and nicotinamide were more frequently prescribed in the higher risk groups as chemoprophylaxis (p < 0.005). A significant association with high predicted risk was also observed in the use of cryotherapy and curettage and cautery (p < 0.05). The average health-care costs associated with a skin check visit increased from $90 ± 37 (standard deviation) in the lowest risk group to $149 ± 97 in the highest risk group (p < 0.0001). CONCLUSIONS: We observed a positive association between higher predicted risk of skin cancer and the prescription of chemoprophylaxis and health-care costs involved with opportunistic community skin cancer screening. A clinical use of risk stratification may be to provide an opportunity for clinicians to discuss skin cancer prevention and chemoprophylaxis with individual patients.

Response to 'The essential role of dermatology publications in enhancing professional diversity, equity and inclusion'.

LINKED ARTICLE: Burgin et al. Br J Dermatol 2021; 185:473-4.

Anatomic Distribution of Cherry Angiomas in the General Population.

BACKGROUND: Cherry angiomas are common benign vascular skin lesions of unknown aetiology, found largely on the trunk. However, their exact anatomic distribution besides their truncal predisposition, and how they manifest in the general population, has not been characterised. METHODS: Three-dimensional (3D) total body imaging was obtained from 163 adult participants of a general population cohort study in Brisbane, Australia. Demographic, phenotypic, and sun behaviour characteristics were collected using a standard questionnaire along with history of melanoma and keratinocyte cancers. Cherry angiomas were identified using an automated classification algorithm with a sensitivity of 87% and a specificity of 99%, developed specifically for this study population. RESULTS: The 3D total body images of 163 participants were analysed. Participants had a median age of 57 years and 61% were male. On average, males had more angiomas than females (median of 16 vs. 12) and the number and size of cherry angiomas increased with age. In addition to male sex and age, an increase in angiomas was associated with Caucasian ancestry other than British/Irish only, fair skin colour opposed to medium/olive, having green/hazel eyes compared to blue/grey, and personal history of melanoma. The most common site for cherry angiomas was the front trunk, followed by the back. Interestingly, although males had more angiomas overall, females had more angiomas on the legs. CONCLUSION: Describing the distribution of cherry angiomas by body site is an important step towards further understanding of the aetiology of angiomas. While personal history of melanoma is associated with an increased number of cherry angiomas, whether this association is prognostic, co-occurs with development of melanoma, or is merely fortuitous requires further investigation.

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.

The Additive Value of 3D Total Body Imaging for Sequential Monitoring of Skin Lesions: A Case Series.

BACKGROUND: Timely diagnosis is the cornerstone of melanoma morbidity and mortality reduction. 2D total body photography and dermoscopy are routinely used to assist with early detection of skin malignancies. Polarized 3D total body photography is a novel technique that enables fast image acquisition of almost the entire skin surface. We aimed to determine the added value of 3D total body photography alongside dermoscopy for monitoring cutaneous lesions. METHODS: Lesion images from high-risk individuals were assessed for long-term substantial changes via dermoscopy and 3D total body photography. Three case studies are presented demonstrating how 3D total body photography may enhance lesion analysis alongside traditional dermoscopy. RESULTS: 3D total body photography can assist clinicians by presenting cutaneous lesions in their skin ecosystem, thereby providing additional clinical context and enabling a more holistic assessment to aid dermoscopy interpretation. For lesion cases where previous dermoscopy is unavailable, corresponding 3D images can substitute for baseline dermoscopy. Additionally, 3D total body photography is not susceptible to artificial stretch artefacts. CONCLUSION: 3D total body photography is valuable alongside dermoscopy for monitoring cutaneous lesions. Furthermore, it is capable of surveilling almost the entire skin surface, including areas not traditionally monitored by sequential imaging.

Consumer Preference and Willingness to Pay for Direct-to-Consumer Mobile Teledermoscopy Services in Australia.

OBJECTIVE: To investigate consumer preference and willingness to pay for mobile teledermoscopy services in Australia. METHODS: Consumers who were taking part in a randomised controlled trial comparing mobile teledermoscopy and skin self-examination were asked to complete a survey which incorporated a discrete choice experiment (DCE) and a contingent valuation question. Responses were used to determine their willingness to pay for mobile teledermoscopy services in Australia and their overall service preferences. RESULTS: The 199 consumers who responded were 71% female and had a mean age of 42 years (range, 18-73). The DCE results showed that consumers prefer a trained medical professional to be involved in their skin cancer screening. Consumers were willing to pay AUD 41 to change from a general practitioner reviewing their lesions in-person to having a dermatologist reviewing the teledermoscopy images. Additionally, they were willing to pay for services that had shorter waiting times, that reduced the time away from their usual activities, and that have higher accuracy and lower likelihood of unnecessary excision of a skin lesion. When asked directly about their willingness to pay for a teledermoscopy service using a contingent valuation question, the majority (73%) of consumers selected the lowest two value brackets of AUD 1-20 or AUD 21-40. CONCLUSION: Consumers are willing to pay out of pocket to access services with attributes such as a dermatologist review, improved accuracy, and fewer excisions.

Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks.

BACKGROUND: The number of naevi on a person is the strongest risk factor for melanoma; however, naevus counting is highly variable due to lack of consistent methodology and lack of inter-rater agreement. Machine learning has been shown to be a valuable tool for image classification in dermatology. OBJECTIVES: To test whether automated, reproducible naevus counts are possible through the combination of convolutional neural networks (CNN) and three-dimensional (3D) total body imaging. METHODS: Total body images from a study of naevi in the general population were used for the training (82 subjects, 57,742 lesions) and testing (10 subjects; 4,868 lesions) datasets for the development of a CNN. Lesions were labelled as naevi, or not ("non-naevi"), by a senior dermatologist as the gold standard. Performance of the CNN was assessed using sensitivity, specificity, and Cohen's kappa, and evaluated at the lesion level and person level. RESULTS: Lesion-level analysis comparing the automated counts to the gold standard showed a sensitivity and specificity of 79% (76-83%) and 91% (90-92%), respectively, for lesions ≥2 mm, and 84% (75-91%) and 91% (88-94%) for lesions ≥5 mm. Cohen's kappa was 0.56 (0.53-0.59) indicating moderate agreement for naevi ≥2 mm, and substantial agreement (0.72, 0.63-0.80) for naevi ≥5 mm. For the 10 individuals in the test set, person-level agreement was assessed as categories with 70% agreement between the automated and gold standard counts. Agreement was lower in subjects with numerous seborrhoeic keratoses. CONCLUSION: Automated naevus counts with reasonable agreement to those of an expert clinician are possible through the combination of 3D total body photography and CNNs. Such an algorithm may provide a faster, reproducible method over the traditional in person total body naevus counts.

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

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), in-app 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. CONCLUSION: Very few skin imaging applications included more than one image acquisition technique feature. Feature richness varied significantly by audience, function, and user categories. Users of digital dermatology tools should consider which applications have standardized features that improve image quality.

Digital skin imaging applications, part II: a comprehensive survey of post-acquisition image utilization features and technology standards.

BACKGROUND: Despite the increasing ubiquity and accessibility of teledermatology applications, few studies have comprehensively surveyed their features and technical standards. Importantly, features implemented after the point of capture are often intended to augment image utilization, while technical standards affect interoperability with existing healthcare systems. We aim to comprehensively survey image utilization features and technical characteristics found within publicly discoverable digital skin imaging applications. MATERIALS AND METHODS: Applications were identified and categorized as described in Part I. Included applications were then further assessed by three independent reviewers for post-imaging content, tools, and functionality. Publicly available information was used to determine the presence or absence of relevant technology standards and/or data characteristics. RESULTS: A total of 20 post-image acquisition features were identified across three general categories: (1) metadata attachment, (2) functional tools (i.e., those that utilized images or in-app content to perform a user-directed function), and (3) image processing. Over 80% of all applications implemented metadata features, with nearly half having metadata features only. Individual feature occurred and feature richness varied significantly by primary audience (p < 0.0001) and function (p < 0.0001). On average, each application included under three features. Less than half of all applications requested consent for user-uploaded photos and fewer than 10% provided clear data use and privacy policies. CONCLUSION: Post-imaging functionality in skin imaging applications varies significantly by primary audience and intended function, though nearly all applications implemented metadata labeling. Technical standards are often not implemented or reported consistently. Gaps in the provision of clear consent, data privacy, and data use policies should be urgently addressed.