Dermoscopy, with and without visual inspection, for diagnosing melanoma in adults

Towards Characterization of Skin Melanoma in the Clinic by Electron Paramagnetic Resonance (EPR) Spectroscopy and Imaging of Melanin

The incidence of melanoma is continuously increasing over time. Melanoma is the most aggressive skin cancer, significantly reducing quality of life and survival rates of patients at advanced stages. Therefore, early diagnosis remains the key to change the prognosis of patients with melanoma. In this context, advanced technologies are under evaluation to increase the accuracy of the diagnostic, to better characterize the lesions and visualize their possible invasiveness in the epidermis. Among the innovative methods, because melanin is paramagnetic, clinical low frequency electron paramagnetic resonance (EPR) that characterizes the melanin content in the lesion has the potential to be an adjunct diagnostic method of melanoma. In this review, we first summarize the challenges faced by dermatologists and oncologists in melanoma diagnostic and management. We also provide a historical perspective on melanin detection with a focus on EPR spectroscopy/imaging of melanomas. We describe key elements that allow EPR to move from in vitro studies to in vivo and finally to patients for melanoma studies. Finally, we provide a critical view on challenges to meet to make EPR operational in the clinic to characterize pigmented lesions.

Highly Sensitive Detection of Melanin in Melanomas Using Multi-harmonic Low Frequency EPR

PURPOSE

Low frequency EPR can noninvasively detect endogenous free radical melanin in melanocytic skin lesions and could potentially discriminate between benign atypical nevi and malignant melanoma lesions. We recently succeeded in demonstrating the ability of clinical EPR to noninvasively detect the endogenous melanin free radical in skin lesions of patients. However, the signal-to-noise ratio (SNR) was extremely low warranting further research to boost the sensitivity of detection. In the present study, we assessed the performance of a clinical EPR system with the capability to perform multi-harmonic (MH) analysis for the detection of melanin.

PROCEDURES

The sensitivity of MH-EPR was compared with a classical continuous wave (CW)-EPR (1st harmonic) detection in vitro in melanin phantoms, in vivo in melanoma models with cells implanted in the skin, in lymph nodes and having colonized the lungs, and finally on phantoms placed at the surface of human skin.

RESULTS

In vitro, we observed an increase in SNR by a factor of 10 in flat melanin phantoms when using MH analysis compared to CW combined with an increase in modulation amplitude. In B16 melanomas having grown in the skin of hairless mice, we observed a boost in sensitivity in vivo similar to that observed in vitro with the capability to detect melanoma cells at an earlier stage of development. MH-EPR was also able to detect non-invasively the melanin signal coming from melanoma cells present in lymph nodes as well as in lungs. We also observed a boost of sensitivity using phantoms of melanin placed at the surface of human skin.

CONCLUSIONS

Overall, our results are paving the way for new clinical trials that will use MH clinical EPR for the characterization of pigmented skin lesions.

Dermoscopic structures and patterns used in melanoma detection

Melanoma is the leading cause of skin cancer-related deaths. Yet, early detection remains the most cost-effective means of preventing death from melanoma. Early detection can be achieved by a physician and/or the patient (also known as a self-skin exam). Skin exams performed by physicians are further enhanced using dermoscopy. Dermoscopy is a non-invasive technique that allows for the visualization of subsurface structures that are otherwise not visible to the naked eye. Evidence demonstrates that dermoscopy improves the diagnostic accuracy for skin cancer, including melanoma; it decreases the number of unnecessary skin biopsies of benign lesions and improves the benign-to-malignant biopsy ratio. Yet, these improvements are contingent on acquiring dermoscopy training. Dermoscopy is used by clinicians who evaluate skin lesions and perform skin cancer screenings. In general, under dermoscopy nevi tend to appear as organized lesions, with one or two structures and colors, and no melanoma-specific structures. In contrast, melanomas tend to manifest a disorganized pattern, with more than two colors and, usually, at least one melanoma-specific structure. This review is intended to familiarize the reader with the dermoscopic structures and patterns used in melanoma detection.

Facilitating clinically relevant skin tumor diagnostics with spectroscopy-driven machine learning

In the dawning era of artificial intelligence (AI), health care stands to undergo a significant transformation with the increasing digitalization of patient data. Digital imaging, in particular, will serve as an important platform for AI to aid decision making and diagnostics. A growing number of studies demonstrate the potential of automatic pre-surgical skin tumor delineation, which could have tremendous impact on clinical practice. However, current methods rely on having ground truth images in which tumor borders are already identified, which is not clinically possible. We report a novel approach where hyperspectral images provide spectra from small regions representing healthy tissue and tumor, which are used to generate prediction maps using artificial neural networks (ANNs), after which a segmentation algorithm automatically identifies the tumor borders. This circumvents the need for ground truth images, since an ANN model is trained with data from each individual patient, representing a more clinically relevant approach.

Training Family Medicine Residents in Dermoscopy Using an e-Learning Course: Pilot Interventional Study

BACKGROUND

Skin cancers are the most common group of cancers diagnosed worldwide. Aging and sun exposure increase their risk. The decline in the number of dermatologists is pushing the issue of dermatological screening back onto family doctors. Dermoscopy is an easy-to-use tool that increases the sensitivity of melanoma diagnosis by 60% to 90%, but its use is limited due to lack of training. The characteristics of "ideal" dermoscopy training have yet to be established. We created a Moodle (Moodle HQ)-based e-learning course to train family medicine residents in dermoscopy.

OBJECTIVE

This study aimed to evaluate the evolution of dermoscopy knowledge among family doctors immediately and 1 and 3 months after e-learning training.

METHODS

We conducted a prospective interventional study between April and November 2020 to evaluate an educational program intended for family medicine residents at the University of Montpellier-Nîmes, France. They were asked to complete an e-learning course consisting of 2 modules, with an assessment quiz repeated at 1 (M1) and 3 months (M3). The course was based on a 2-step algorithm, a method of dermoscopic analysis of pigmented skin lesions that is internationally accepted. The objectives of modules 1 and 2 were to differentiate melanocytic lesions from nonmelanocytic lesions and to precisely identify skin lesions by looking for dermoscopic morphological criteria specific to each lesion. Each module consisted of 15 questions with immediate feedback after each question.

RESULTS

In total, 134 residents were included, and 66.4% (n=89) and 47% (n=63) of trainees fully participated in the evaluation of module 1 and module 2, respectively. This study showed a significant score improvement 3 months after the training course in 92.1% (n=82) of participants for module 1 and 87.3% (n=55) of participants for module 2 (P<.001). The majority of the participants expressed satisfaction (n=48, 90.6%) with the training course, and 96.3% (n=51) planned to use a dermatoscope in their future practice. Regarding final scores, the only variable that was statistically significant was the resident's initial scores (P=.003) for module 1. No measured variable was found to be associated with retention (midtraining or final evaluation) for module 2. Residents who had completed at least 1 dermatology rotation during medical school had significantly higher initial scores in module 1 at M0 (P=.03). Residents who reported having completed at least 1 dermatology rotation during their family medicine training had a statistically significant higher score at M1 for module 1 and M3 for module 2 (P=.01 and P=.001).

CONCLUSIONS

The integration of an e-learning training course in dermoscopy into the curriculum of FM residents results in a significant improvement in their diagnosis skills and meets their expectations. Developing a program combining an e-learning course and face-to-face training for residents is likely to result in more frequent and effective dermoscopy use by family doctors.

The Status of Dermoscopy in Chile: First National Study in Dermatologists

INTRODUCTION

Scientific evidence supports dermoscopy as an essential tool in dermatological diagnosis.

OBJECTIVES

The objective is to know the factors that influence its use in Chilean dermatologists.

METHODS

Analytical cross-sectional study. An adapted version of the survey was submitted from the pan-European study by Forsea et al to members of the Chilean Society of Dermatology, between September and December 2020. Analysis using descriptive statistics and multivariate analysis with ordinal logistic regression looking for factors associated with greater use of.

RESULTS

One hundred and ninety-eight responses, mean age 46.3 years and 14.6 years on average practicing as dermatologists. 61.6% trained in dermoscopy during their residency. 98% use a dermatoscope. More than 80% consider dermoscopy useful for the diagnosis of melanomas, follow-up of melanocytic lesions, and diagnosis of pigmented and non-pigmented tumors. Between 50% and 70% consider it useful for monitoring non-melanocytic lesions, nail and hair pathologies. Greater confidence when evaluating pigmented and non-pigmented tumors and capillary pathology. Adjusting for age, sex, confidence, and education, participation in teaching was associated with greater use of dermoscopy in non-pigmented and pigmented tumors, and capillary pathology.

CONCLUSIONS

Percentage of participation in the survey and training in dermoscopy higher than in the reference study, recognizing the usefulness of dermoscopy for the diagnosis and follow-up of tumor pathologies. Participating in teaching is a strong independent factor that is associated with a greater use of dermoscopy in Chile. Dermoscopy is positioned as a tool widely used by Chilean dermatologists in their daily practice.

Diagnostic Accuracy and Safety of Teledermoscopy for Cutaneous Melanoma Triage in Northern Sweden

Abstract is missing (Short communication)

Identification of immune-related genes and integrated analysis of immune-cell infiltration in melanoma

OBJECTIVE

This study was conducted to screen out immune-related genes in connection with the prognosis of melanoma, construct a prognosis model and explore the relevant mechanisms.

METHODS AND MATERIALS

1973 genes associated with immune system were derived from the Immport database, and RNA-seq data of melanoma and information of patients were searched from the Xena database. Cox univariate analysis, Lasso analysis and Cox multivariate analysis were used to screen out six genes to construct the model. Then the risk scores were estimated for patients based on our constructed prognosis model. Estimate was used to affirm that the model was about immune infiltration, and CIBERSORT was used to screen out immune cells associated with prognosis. TIDE was applied to predict the efficacy of immunotherapy. Finally, GSE65904 and GSE19234 were used to confirm the effectiveness of the model.

RESULTS

ADCYAP1R1, GPI, NTS might cause poor prognosis while IFITM1, KIR2DL4, LIF were more likely conductive to prognosis of melanoma patients and a model of prognosis was constructed on the basis of these six genes. The effectiveness of the model has been proven by the ROC curve, and the miRNAs targeting the screened genes were found out, suggesting that the immune system might impact on the prognosis of melanoma by T cell CD8+, T cell CD4+ memory and NK cells.

CONCLUSIONS

In this study, the screened six genes were associated with the prognosis of melanoma, which was conductive to clinical prognostic prediction and individualized treatment strategy.

Protocol for a systematic review of reviews on training primary care providers in dermoscopy to detect skin cancers

INTRODUCTION

Globally, incidence, prevalence and mortality rates of skin cancers are escalating. Earlier detection by well-trained primary care providers in techniques such as dermoscopy could reduce unnecessary referrals and improve longer term outcomes. A review of reviews is planned to compare and contrast the conduct, quality, findings and conclusions of multiple systematic and scoping reviews addressing the effectiveness of training primary care providers in dermoscopy, which will provide a critique and synthesis of the current body of review evidence.

METHODS AND ANALYSIS

Four databases (Cochrane, CINAHL, EMBASE and MEDLINE Complete) will be comprehensively searched from database inception to identify published, peer-reviewed English-language articles describing scoping and systematic reviews of the effectiveness of training primary care providers in the use of dermoscopy to detect skin cancers. Two researchers will independently conduct the searches and screen the results for potentially eligible studies using 'Research Screener' (a semi-automated machine learning tool). Backwards and forwards citation tracing will be conducted to supplement the search. A narrative summary of included reviews will be conducted. Study characteristics, for example, population; type of educational programme, including content, delivery method, duration and assessment; and outcomes for dermoscopy will be extracted into a standardised table. Data extraction will be checked by the second reviewer. Methodological quality will be evaluated by two reviewers independently using the Critical Appraisal Tool for Health Promotion and Prevention Reviews. Results of the assessments will be considered by the two reviewers and any discrepancies will be resolved by team consensus.

ETHICS AND DISSEMINATION

Ethics approval is not required to conduct the planned systematic review of peer-reviewed, published articles because the research does not involve human participants. Findings will be published in a peer-reviewed journal, presented at leading public health, cancer and primary care conferences, and disseminated via website postings and social media channels.

PROSPERO REGISTRATION NUMBER

CRD42023396276.

Dermoscopy of Lentiginous Melanomas and Equivocal Benign Pigmented Macules of the Scalp: A Case-Control Multicentric Study

INTRODUCTION

Although the dermoscopic features of facial lentiginous melanomas (LM), including lentigo maligna and lentigo maligna melanoma, have been extensively studied, the literature about those located on the scalp is scarce. This study aims to describe the dermoscopic features of scalp LM and assess the diagnostic accuracy of dermoscopy to discriminate them from equivocal benign pigmented macules.

METHODS

Consecutive cases of scalp LM and histopathology-proven benign but clinically equivocal pigmented macules (actinic keratoses, solar lentigos, seborrhoeic keratoses, and lichen planus-like keratoses) from four referral centres were included. Dermoscopic features were analysed by two blinded experts. The diagnostic performance of a predictive model was assessed.

RESULTS

56 LM and 44 controls were included. Multiple features previously described for facial and extrafacial LM were frequently identified in both groups. Expert's sensitivity to diagnose scalp LM was 76.8% (63.6-87.0) and 78.6% (65.6-88.4), with specificity of 54.5% (38.9-69.6) and 56.8% (41.0-71.7), and fair agreement (kappa coefficient 0.248). The strongest independent predictors of malignancy were (OR, 95% CI) chaos of colour (15.43, 1.48-160.3), pigmented reticular lines (14.96, 1.68-132.9), increased density of vascular network (3.45, 1.09-10.92), and perifollicular grey circles (2.89, 0.96-8.67). The predictive model achieved 85.7% (73.8-93.6) sensitivity, 61.4% (45.5-75.6) specificity, and 81.5 (73.0-90.0) area under curve to discriminate benign and malignant lesions. A diagnostic flowchart was proposed, which should improve the diagnostic performance of dermoscopy.

CONCLUSION

Both facial and extrafacial dermoscopic patterns can be identified in scalp LM, with considerable overlap with benign pigmented macules, leading to low specificity and interobserver agreement on dermoscopy.

Advances in melanoma: epidemiology, diagnosis, and prognosis

Unraveling the multidimensional complexities of melanoma has required concerted efforts by dedicated community of researchers and clinicians battling against this deadly form of skin cancer. Remarkable advances have been made in the realm of epidemiology, classification, diagnosis, and therapy of melanoma. The treatment of advanced melanomas has entered the golden era as targeted personalized therapies have emerged that have significantly altered the mortality rate. A paradigm shift in the approach to melanoma classification, diagnosis, prognosis, and staging is underway, fueled by discoveries of genetic alterations in melanocytic neoplasms. A morphologic clinicopathologic classification of melanoma is expected to be replaced by a more precise molecular based one. As validated, convenient, and cost-effective molecular-based tests emerge, molecular diagnostics will play a greater role in the clinical and histologic diagnosis of melanoma. Artificial intelligence augmented clinical and histologic diagnosis of melanoma is expected to make the process more streamlined and efficient. A more accurate model of prognosis and staging of melanoma is emerging based on molecular understanding melanoma. This contribution summarizes the recent advances in melanoma epidemiology, classification, diagnosis, and prognosis.

Generation of a Melanoma and Nevus Data Set From Unstandardized Clinical Photographs on the Internet

Importance

Artificial intelligence (AI) training for diagnosing dermatologic images requires large amounts of clean data. Dermatologic images have different compositions, and many are inaccessible due to privacy concerns, which hinder the development of AI.

Objective

To build a training data set for discriminative and generative AI from unstandardized internet images of melanoma and nevus.

Design, Setting, and Participants

In this diagnostic study, a total of 5619 (CAN5600 data set) and 2006 (CAN2000 data set; a manually revised subset of CAN5600) cropped lesion images of either melanoma or nevus were semiautomatically annotated from approximately 500 000 photographs on the internet using convolutional neural networks (CNNs), region-based CNNs, and large mask inpainting. For unsupervised pretraining, 132 673 possible lesions (LESION130k data set) were also created with diversity by collecting images from 18 482 websites in approximately 80 countries. A total of 5000 synthetic images (GAN5000 data set) were generated using the generative adversarial network (StyleGAN2-ADA; training, CAN2000 data set; pretraining, LESION130k data set).

Main Outcomes and Measures

The area under the receiver operating characteristic curve (AUROC) for determining malignant neoplasms was analyzed. In each test, 1 of the 7 preexisting public data sets (total of 2312 images; including Edinburgh, an SNU subset, Asan test, Waterloo, 7-point criteria evaluation, PAD-UFES-20, and MED-NODE) was used as the test data set. Subsequently, a comparative study was conducted between the performance of the EfficientNet Lite0 CNN on the proposed data set and that trained on the remaining 6 preexisting data sets.

Results

The EfficientNet Lite0 CNN trained on the annotated or synthetic images achieved higher or equivalent mean (SD) AUROCs to the EfficientNet Lite0 trained using the pathologically confirmed public data sets, including CAN5600 (0.874 [0.042]; P = .02), CAN2000 (0.848 [0.027]; P = .08), and GAN5000 (0.838 [0.040]; P = .31 [Wilcoxon signed rank test]) and the preexisting data sets combined (0.809 [0.063]) by the benefits of increased size of the training data set.

Conclusions and Relevance

The synthetic data set in this diagnostic study was created using various AI technologies from internet images. A neural network trained on the created data set (CAN5600) performed better than the same network trained on preexisting data sets combined. Both the annotated (CAN5600 and LESION130k) and synthetic (GAN5000) data sets could be shared for AI training and consensus between physicians.

Improving Skin Cancer Diagnostics Through a Mobile App With a Large Interactive Image Repository: Randomized Controlled Trial

BACKGROUND

Skin cancer diagnostics is challenging, and mastery requires extended periods of dedicated practice.

OBJECTIVE

The aim of the study was to determine if self-paced pattern recognition training in skin cancer diagnostics with clinical and dermoscopic images of skin lesions using a large-scale interactive image repository (LIIR) with patient cases improves primary care physicians' (PCPs') diagnostic skills and confidence.

METHODS

A total of 115 PCPs were randomized (allocation ratio 3:1) to receive or not receive self-paced pattern recognition training in skin cancer diagnostics using an LIIR with patient cases through a quiz-based smartphone app during an 8-day period. The participants' ability to diagnose skin cancer was evaluated using a 12-item multiple-choice questionnaire prior to and 8 days after the educational intervention period. Their thoughts on the use of dermoscopy were assessed using a study-specific questionnaire. A learning curve was calculated through the analysis of data from the mobile app.

RESULTS

On average, participants in the intervention group spent 2 hours 26 minutes quizzing digital patient cases and 41 minutes reading the educational material. They had an average preintervention multiple choice questionnaire score of 52.0% of correct answers, which increased to 66.4% on the postintervention test; a statistically significant improvement of 14.3 percentage points (P<.001; 95% CI 9.8-18.9) with intention-to-treat analysis. Analysis of participants who received the intervention as per protocol (500 patient cases in 8 days) showed an average increase of 16.7 percentage points (P<.001; 95% CI 11.3-22.0) from 53.9% to 70.5%. Their overall ability to correctly recognize malignant lesions in the LIIR patient cases improved over the intervention period by 6.6 percentage points from 67.1% (95% CI 65.2-69.3) to 73.7% (95% CI 72.5-75.0) and their ability to set the correct diagnosis improved by 10.5 percentage points from 42.5% (95% CI 40.2%-44.8%) to 53.0% (95% CI 51.3-54.9). The diagnostic confidence of participants in the intervention group increased on a scale from 1 to 4 by 32.9% from 1.6 to 2.1 (P<.001). Participants in the control group did not increase their postintervention score or their diagnostic confidence during the same period.

CONCLUSIONS

Self-paced pattern recognition training in skin cancer diagnostics through the use of a digital LIIR with patient cases delivered by a quiz-based mobile app improves the diagnostic accuracy of PCPs.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05661370; https://classic.clinicaltrials.gov/ct2/show/NCT05661370.

Implementation of artificial intelligence for the detection of cutaneous melanoma within a primary care setting: prevalence and types of skin cancer in outdoor enthusiasts

Background

There is enthusiasm for implementing artificial intelligence (AI) to assist clinicians detect skin cancer. Performance metrics of AI from dermoscopic images have been promising, with studies documenting sensitivity and specificity values equal to or superior to specialists for the detection of malignant melanomas (MM). Early detection rates would particularly benefit Australia, which has the worlds highest incidence of MM per capita. The detection of skin cancer may be delayed due to late screening or the inherent difficulty in diagnosing early skin cancers which often have a paucity of clinical features and may blend into sun damaged skin. Individuals who participate in outdoor sports and recreation experience high levels of intermittent ultraviolet radiation (UVR), which is associated with the development of skin cancer, including MM. This research aimed to assess the prevalence of skin cancer in individuals who regularly participate in activities outdoors and to report the performance parameters of a commercially available AI-powered software to assess the predictive risk of MM development.

Methods

Cross-sectional study design incorporating a survey, total body skin cancer screening and AI-embedded software capable of predictive scoring of queried MM.

Results

A total of 423 participants consisting of surfers (n = 108), swimmers (n = 60) and walkers/runners (n = 255) participated. Point prevalence for MM was highest for surfers (6.48%), followed by walkers/runners (4.3%) and swimmers (3.33%) respectively. When compared to the general Australian population, surfers had the highest odds ratio (OR) for MM (OR 119.8), followed by walkers/runners (OR 79.74), and swimmers (OR 61.61) rounded out the populations. Surfers and swimmers reported comparatively lower lifetime hours of sun exposure (5,594 and 5,686, respectively) but more significant amounts of activity within peak ultraviolet index compared with walkers/runners (9,554 h). A total of 48 suspicious pigmented lesions made up of histopathology-confirmed MM (n = 15) and benign lesions (n = 33) were identified. The performance of the AI from this clinical population was found to have a sensitivity of 53.33%, specificity of 54.44% and accuracy of 54.17%.

Conclusions

Rates of both keratinocyte carcinomas and MM were notably higher in aquatic and land-based enthusiasts compared to the general Australian population. These findings further highlight the clinical importance of sun-safe protection measures and regular skin screening in individuals who spend significant time outdoors. The use of AI in the early identification of MM is promising. However, the lower-than-expected performance metrics of the AI software used in this study indicated reservations should be held before recommending this particular version of this AI software as a reliable adjunct for clinicians in skin imaging diagnostics in patients with potentially sun damaged skin.

Measuring the quality of skin cancer management in primary care: A scoping review

Skin cancer is a growing global problem and a significant health and economic burden. Despite the practical necessity for skin cancer to be managed in primary care settings, little is known about how quality of care is or should be measured in this setting. This scoping review aimed to capture the breadth and range of contemporary evidence related to the measurement of quality in skin cancer management in primary care settings. Six databases were searched for relevant texts reporting on quality measurement in primary care skin cancer management. Data from 46 texts published since 2011 were extracted, and quality measures were catalogued according to the three domains of the Donabedian model of healthcare quality (structure, process and outcome). Quality measures within each domain were inductively analysed into 13 key emergent groups. These represented what were deemed to be the most relevant components of skin cancer management as related to structure, process or outcomes measurement. Four groups related to the structural elements of care provision (e.g. diagnostic tools and equipment), five related to the process of care delivery (e.g. diagnostic processes) and four related to the outcomes of care (e.g. poor treatment outcomes). A broad range of quality measures have been documented, based predominantly on articles using retrospective cohort designs; systematic reviews and randomised controlled trials were limited.

Genomic and proteomic findings in early melanoma and opportunities for early diagnosis

Overdiagnosis of early melanoma is a significant problem. Due to subtle unique and overlapping clinical and histological criteria between pigmented lesions and the risk of mortality from melanoma, some benign pigmented lesions are diagnosed as melanoma. Although histopathology is the gold standard to diagnose melanoma, there is a demand to find alternatives that are more accurate and cost-effective. In the current "omics" era, there is gaining interest in biomarkers to help diagnose melanoma early and to further understand the mechanisms driving tumor progression. Genomic investigations have attempted to differentiate malignant melanoma from benign pigmented lesions. However, genetic biomarkers of early melanoma diagnosis have not yet proven their value in the clinical setting. Protein biomarkers may be more promising since they directly influence tissue phenotype, a result of by-products of genomic mutations, posttranslational modifications and environmental factors. Uncovering relevant protein biomarkers could increase confidence in their use as diagnostic signatures. Currently, proteomic investigations of melanoma progression from pigmented lesions are limited. Studies have previously characterised the melanoma proteome from cultured cell lines and clinical samples such as serum and tissue. This has been useful in understanding how melanoma progresses into metastasis and development of resistance to adjuvant therapies. Currently, most studies focus on metastatic melanoma to find potential drug therapy targets, prognostic factors and markers of resistance. This paper reviews recent advancements in the genomics and proteomic fields and reports potential avenues, which could help identify and differentiate melanoma from benign pigmented lesions and prevent the progression of melanoma.

Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study

BACKGROUND

Dermoscopy is commonly used for the evaluation of pigmented lesions, but agreement between experts for identification of dermoscopic structures is known to be relatively poor. Expert labeling of medical data is a bottleneck in the development of machine learning (ML) tools, and crowdsourcing has been demonstrated as a cost- and time-efficient method for the annotation of medical images.

OBJECTIVE

The aim of this study is to demonstrate that crowdsourcing can be used to label basic dermoscopic structures from images of pigmented lesions with similar reliability to a group of experts.

METHODS

First, we obtained labels of 248 images of melanocytic lesions with 31 dermoscopic "subfeatures" labeled by 20 dermoscopy experts. These were then collapsed into 6 dermoscopic "superfeatures" based on structural similarity, due to low interrater reliability (IRR): dots, globules, lines, network structures, regression structures, and vessels. These images were then used as the gold standard for the crowd study. The commercial platform DiagnosUs was used to obtain annotations from a nonexpert crowd for the presence or absence of the 6 superfeatures in each of the 248 images. We replicated this methodology with a group of 7 dermatologists to allow direct comparison with the nonexpert crowd. The Cohen κ value was used to measure agreement across raters.

RESULTS

In total, we obtained 139,731 ratings of the 6 dermoscopic superfeatures from the crowd. There was relatively lower agreement for the identification of dots and globules (the median κ values were 0.526 and 0.395, respectively), whereas network structures and vessels showed the highest agreement (the median κ values were 0.581 and 0.798, respectively). This pattern was also seen among the expert raters, who had median κ values of 0.483 and 0.517 for dots and globules, respectively, and 0.758 and 0.790 for network structures and vessels. The median κ values between nonexperts and thresholded average-expert readers were 0.709 for dots, 0.719 for globules, 0.714 for lines, 0.838 for network structures, 0.818 for regression structures, and 0.728 for vessels.

CONCLUSIONS

This study confirmed that IRR for different dermoscopic features varied among a group of experts; a similar pattern was observed in a nonexpert crowd. There was good or excellent agreement for each of the 6 superfeatures between the crowd and the experts, highlighting the similar reliability of the crowd for labeling dermoscopic images. This confirms the feasibility and dependability of using crowdsourcing as a scalable solution to annotate large sets of dermoscopic images, with several potential clinical and educational applications, including the development of novel, explainable ML tools.

Updates in Cutaneous Oncology

Cutaneous oncology is currently a rapidly evolving field. Dermoscopy, total body photography, biomarkers, and artificial intelligence are affecting the way skin cancers, especially melanoma, are diagnosed and monitored. The medical management of locally advanced and metastatic skin cancer is also changing. In this article, we will discuss recent developments in cutaneous oncology with a particular focus on treatment of advanced cancers.

Thematic analyses of participant survey responses following dermatology ECHO programs with dermoscopy: Practical tips and lessons learned

Introduction

Skin cancer is a major public health concern in the United States, reflecting approximately one in every three cancer diagnoses. Despite the high incidence of skin cancer, access to dermatologists is limited, especially in rural areas. Primary care physicians play a pivotal role in the evaluation of skin conditions, but dermatology training gaps exist in primary care training programs.

Objectives

This study examines the use of the Project ECHO (Extension for Community Healthcare Outcomes) knowledge-sharing framework to provide dermoscopy and skin cancer detection training to primary care providers (PCPs).

Methods

Responses to surveys administered to participants in two separate dermoscopy-focused Project ECHO courses were analyzed. Survey responses were collected over a 4-year period for the two courses, which were delivered in Maine and Texas. Thematic analysis of the qualitative data was performed, revealing codes and subcodes that indicated several overall trends.

Results

Overall, most respondents indicated the ECHO sessions to be helpful, reporting an increase in confidence and knowledge in dermoscopy. Other codes reflected a positive reception of the learning materials and teaching styles. Furthermore, participant survey analyses highlighted areas of improvement for future ECHO course sessions.

Conclusions

This thematic analysis of Project ECHO courses in dermatology with dermoscopy demonstrates the feasibility of using virtual educational platforms to effectively teach PCPs about dermoscopy and skin cancer, with high levels of participant satisfaction. The need to keeping the educational sessions brief, avoid scheduling sessions on high-volume patient care days, and provide a means for participants to obtain hands-on training in the operation of a dermatoscope were among the top lessons learned.

[The Rise of Artificial Intelligence - High Prediction Accuracy in Early Detection of Pigmented Melanoma]

The incidence of malignant melanoma is increasing worldwide. If detected early, melanoma is highly treatable, so early detection is vital.Skin cancer early detection has improved significantly in recent decades, for example by the introduction of screening in 2008 and dermoscopy. Nevertheless, in particular visual detection of early melanomas remains challenging because they show many morphological overlaps with nevi. Hence, there continues to be a high medical need to further develop methods for early skin cancer detection in order to be able to reliably diagnosemelanomas at a very early stage.Routine diagnostics for melanoma detection include visual whole body inspection, often supplemented by dermoscopy, which can significantly increase the diagnostic accuracy of experienced dermatologists. A procedure that is additionally offered in some practices and clinics is wholebody photography combined with digital dermoscopy for the early detection of malignant melanoma, especially for monitoring high-risk patients.In recent decades, numerous noninvasive adjunctive diagnostic techniques were developed for the examination of suspicious pigmented moles, that may have the potential to allow improved and, in some cases, automated evaluation of these lesions. First, confocal laser microscopy should be mentioned here, as well as electrical impedance spectroscopy, multiphoton laser tomography, multispectral analysis, Raman spectroscopy or optical coherence tomography. These diagnostic techniques usually focus on high sensitivity to avoid malignant melanoma being overlooked. However, this usually implies lower specificity, which may lead to unnecessary excision of benign lesions in screening. Also, some of the procedures are time-consuming and costly, which also limits their applicability in skin cancer screening. In the near future, the use of artificial intelligence might change skin cancer diagnostics in many ways. The most promising approach may be the analysis of routine macroscopic and dermoscopic images by artificial intelligence.For the classification of pigmented skin lesions based on macroscopic and dermoscopic images, artificial intelligence, especially in form of neural networks, has achieved comparable diagnostic accuracies to dermatologists under experimental conditions in numerous studies. In particular, it achieved high accuracies in the binary melanoma/nevus classification task, but it also performed comparably well to dermatologists in multiclass differentiation of various skin diseases. However, proof of the basic applicability and utility of such systems in clinical practice is still pending. Prerequisites that remain to be established to enable translation of such diagnostic systems into dermatological routine are means that allow users to comprehend the system's decisions as well as a uniformly high performance of the algorithms on image data from other hospitals and practices.At present, hints are accumulating that computer-aided diagnosis systems could provide their greatest benefit as assistance systems, since studies indicate that a combination of human and machine achieves the best results. Diagnostic systems based on artificial intelligence are capable of detecting morphological characteristics quickly, quantitatively, objectively and reproducibly, and could thus provide a more objective analytical basis - in addition to medical experience.

Dermoscopy use in primary care: a qualitative study with general practitioners

Background

Skin assessments constitute a significant proportion of consultations with family physicians (commonly called general practitioners or GPs in the UK), and referrals to hospital dermatology departments have risen significantly in recent years. Research has shown that dermoscopy use may help GPs to assess and triage skin lesions, including suspected skin cancers, more accurately. However, dermoscopy is used by a small minority of GPs in the UK. Previous questionnaire studies have aimed to establish in a limited way some perceptions of dermoscopy among GPs: this study aimed to explore more deeply the factors influencing the use of dermoscopy among GPs.

Methods

This was a qualitative interview study set in UK general practice. A purposive sample was taken of GPs who were established dermoscopy users, GPs who had recently adopted dermoscopy, and those who did not use dermoscopy. A total of twelve semi-structured interviews were conducted. Audio-recordings were transcribed verbatim and analysed using a thematic analysis (Braun and Clarke).

Results

GPs’ capability to use dermoscopy necessitated receiving adequate training, while previous dermatology experience and support from colleagues were also considered factors that enabled dermoscopy use. The impact of dermoscopy on patient consultations about skin complaints was generally considered to be positive, as was having an ‘in-house’ dermoscopy user within a GP practice to refer patients to. However, training in dermoscopy was not considered a priority for many GPs either due to other more pressing concerns within their practices or the perceived complexity of dermoscopy, alongside barriers such as equipment costs. Significant ethical concerns with posting patient photographs online for training and teaching purposes were also highlighted.

Conclusions

Both GPs who use dermoscopy, and those who do not, consider it to have an important role in improving skin assessments within primary care. However the need for adequate training in dermoscopy and dermatology more generally was highlighted as a key barrier to its wider use. The development of competency standards for the use of dermoscopy could allow the adequacy of training to be assessed and developed.

The Hunt for Baby Melanomas: A Prospective Study of the Dermoscopy Features on 100 Small Melanoma Cases with in Vivo Surface Diameters up to a Maximum of 6 mm

INTRODUCTION

Early diagnosis can improve melanoma prognosis. Dermoscopy can enhance early melanoma recognition.

OBJECTIVES

Examine the dermoscopy features of early melanoma up to a maximum surface diameter of 6 mm.

METHODS

Consecutive melanoma cases were collected from two medical practices in Sydney, Australia 2019-2021. Dermoscopy features were recorded for melanomas by maximum surface diameter, to the nearest 0.1 mm, to a limit of 6 mm.

RESULTS

Total cases numbered 100; with males (N = 48) and females (N = 52), melanoma in situ (MIS, N = 96) and invasive (N = 4). The most frequent anatomic sites on both males and females were back (males N = 20, females N = 16) then knee or leg (males N = 8, females N = 12). Minimum respective MIS diameters for males/females was 1.2/2.0 mm and for invasive cases 2.0/3.4 mm. Highest frequency dermoscopy features were: light brown, dark brown, gray and asymmetric melanoma shape. Brown pigment in hair follicles were more frequent on legs compared to other anatomic sites (odds ratio [OR] 14.6; 95% CI 1.29-165.17, P 0.03). Pseudopods were substantially increased in frequency comparing diameters less than 4 mm with 4 up to 6 mm (OR 8.81; 95% CI 1.05-73.9, P 0.004). Structureless area cases recorded increased gray (OR 7.08; 95% CI 1.61-31.11, P=0.01). Melanomas with edge angulation were noted in 20%-50% of cases across diameters 1-6 mm, less frequent were pigmented circles and polygons.

CONCLUSIONS

Watch out! MIS presented with a surface diameter of just 1.2 mm and invasive melanoma 2.5 mm. Pseudopods were a strong clue to melanomas with a surface diameter less than 5mm. We found melanomas on leg sites displayed more frequent pigmented hair follicles.

The degradation of performance of a state-of-the-art skin image classifier when applied to patient-driven internet search

Model Dermatology ( https://modelderm.com ; Build2021) is a publicly testable neural network that can classify 184 skin disorders. We aimed to investigate whether our algorithm can classify clinical images of an Internet community along with tertiary care center datasets. Consecutive images from an Internet skin cancer community ('RD' dataset, 1,282 images posted between 25 January 2020 to 30 July 2021; https://reddit.com/r/melanoma ) were analyzed retrospectively, along with hospital datasets (Edinburgh dataset, 1,300 images; SNU dataset, 2,101 images; TeleDerm dataset, 340 consecutive images). The algorithm's performance was equivalent to that of dermatologists in the curated clinical datasets (Edinburgh and SNU datasets). However, its performance deteriorated in the RD and TeleDerm datasets because of insufficient image quality and the presence of out-of-distribution disorders, respectively. For the RD dataset, the algorithm's Top-1/3 accuracy (39.2%/67.2%) and AUC (0.800) were equivalent to that of general physicians (36.8%/52.9%). It was more accurate than that of the laypersons using random Internet searches (19.2%/24.4%). The Top-1/3 accuracy was affected by inadequate image quality (adequate = 43.2%/71.3% versus inadequate = 32.9%/60.8%), whereas participant performance did not deteriorate (adequate = 35.8%/52.7% vs. inadequate = 38.4%/53.3%). In this report, the algorithm performance was significantly affected by the change of the intended settings, which implies that AI algorithms at dermatologist-level, in-distribution setting, may not be able to show the same level of performance in with out-of-distribution settings.

Dermoscopy Use Leads to Earlier Cutaneous Melanoma Diagnosis in Terms of Invasiveness and Size? A Single-Center, Retrospective Experience

Background: The incidence of cutaneous melanoma has risen in recent years. The aim of this study was to compare cutaneous melanomas diagnosed at the Dermatology Unit of Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy, from 2006 to 2020 and between two specific biennia, i.e., 2006−2007 and 2019−2020. Methods: Retrospective chart review, with dermoscopic image collection, of cutaneous melanomas diagnosed at the Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy, from 1 January 2006 to 31 December 2020 Results: A statistically significant increase was shown in the proportions of in situ melanoma and melanoma measuring less than 6 mm, i.e., small-diameter melanoma (SDM), across the studied period (p < 0.001). Moreover, in the biennium 2006−2007, among 220 melanoma diagnoses, 6 were in situ (2.7%), as compared with 68 melanomas in situ out of a total of 236 (28.8%) melanomas diagnosed in the biennium 2019−2020. A statistically significant difference in the proportion of in situ melanoma between the two biennia was demonstrated (p < 0.001). Furthermore, during the first biennium, 27/220 (12.3%) SDM were identified, as compared with 61/236 (25.9%) in the last. A statistically significant difference was shown in the proportion of SDM between the two (p < 0.001). Conclusions: The percentage of in situ melanomas and those that can be detected at a diameter <6 mm has increased. The latter has been shown to be around one-third of excised lesions, thus undermining the practicality of the ABCD mnemonic. Dermoscopic criteria for SDM are needed to help further refine melanoma diagnosis.

Early Melanoma Detection in Primary Care: Clinical Recognition of Melanoma is Not Enough, One Must Also Learn the Basics

To improve early melanoma detection, educational programs have been developed for general practitioners (GPs). This study aimed to determine whether the adjunct of teaching basic knowledge of pigmented skin lesions (PSL) to the training in melanoma diagnosis improves the GPs' diagnostic accuracy of melanoma. An interventional prospective study was conducted over a 3-month period where GPs attended a 2-h training course. The 1^st session taught clinical melanoma recognition and the 2^nd session instructed basic knowledge of PSL. Prior to training, after the 1^st, and after the 2^nd session, GPs were asked to select the malignant or benign nature of 15 clinical images associated to their clinical history. In total, 56 GPs participated in this study. The number of GPs identifying correctly ≥ 50% of the melanomas increased the most after the 1^st session from 15 (26.8%; CI = (15.2; 38.4)) to 44 (78.6%; CI = (67.8; 89.3)) GPs (P < 0.001). The number of GPs correctly identifying ≥ 50% of the benign PSL only increased after completing the entire training, going from 10 (17.9%; CI = [(7.8; 27.9)) GPs to 50 (89.3%; CI = (81.2; 97.4)) GPs (P < 0.001). In this study, GPs identified benign PSL most accurately after the 2^nd session. This suggested that teaching GPs the basics of PSL would especially improve their diagnostic accuracy for benign PSL, which could reduce unnecessary referrals to dermatologists. Teaching basic knowledge of PSL in addition to melanoma recognition seemed to enable GPs to triage skin lesions more effectively than when they were only trained to recognize melanoma.

Fractal Dimension Analysis of Melanocytic Nevi and Melanomas in Normal and Polarized Light-A Preliminary Report

Clinical diagnosis of pigmented lesions can be a challenge in everyday practice. Benign and dysplastic nevi and melanomas may have similar clinical presentations, but completely different prognoses. Fractal dimensions of shape and texture can describe the complexity of the pigmented lesion structure. This study aims to apply fractal dimension analysis to differentiate melanomas, dysplastic nevi, and benign nevi in polarized and non-polarized light. A total of 87 Eighty-four patients with 97 lesions were included in this study. All examined lesions were photographed under polarized and non-polarized light, surgically removed, and examined by a histopathologist to establish the correct diagnosis. The obtained images were then processed and analyzed. Area, perimeter, and fractal dimensions of shape and texture were calculated for all the lesions under polarized and non-polarized light. The fractal dimension of shape in polarized light enables differentiating melanomas, dysplastic nevi, and benign nevi. It also makes it possible to distinguish melanomas from benign and dysplastic nevi under non-polarized light. The fractal dimension of texture allows distinguishing melanomas from benign and dysplastic nevi under polarized light. All examined parameters of shape and texture can be used for developing an automatic computer-aided diagnosis system. Polarized light is superior to non-polarized light for imaging texture details.

Skin Cancer Education Interventions for Primary Care Providers: A Scoping Review

Primary care physicians (PCPs) are often the first line of defense against skin cancers. Despite this, many PCPs do not receive a comprehensive training in skin conditions. Educational interventions aimed at skin cancer screening instruction for PCPs offer an opportunity to detect skin cancer at earlier stages and subsequent improved morbidity and mortality. A scoping review was conducted to collect data about previously reported skin cancer screening interventions for PCPs. A structured literature search found 51 studies describing 37 unique educational interventions. Curriculum elements utilized by the interventions were divided into categories that would facilitate comparison including curriculum components, delivery format, delivery timing, and outcome measures. The interventions varied widely in design, including literature-based interventions, live teaching sessions, and online courses with durations ranging from 5 min to 24 months. While several interventions demonstrated improvements in skin cancer knowledge and competency by written exams, only a few revealed positive clinical practice changes by biopsy review or referral analysis. Examining successful interventions could aid in developing a skin cancer detection curriculum for PCPs that can produce positive clinical practice and population-based changes in the management of skin cancer.

Texture Analysis in Diagnosing Skin Pigmented Lesions in Normal and Polarized Light-A Preliminary Report

The differential diagnosis of benign nevi (BN), dysplastic nevi (DN), and melanomas (MM) represents a considerable clinical problem. These lesions are similar in clinical examination but have different prognoses and therapeutic management techniques. A texture analysis (TA) is a mathematical and statistical analysis of pixel patterns of a digital image. This study aims to demonstrate the relationship between the TA of digital images of pigmented lesions under polarized and non-polarized light and their histopathological diagnosis. Ninety pigmented lesions of 76 patients were included in this study. We obtained 166 regions of interest (ROI) images for MM, 166 for DN, and 166 for BN. The pictures were taken under polarized and non-polarized light. Selected image texture features (entropy and difference entropy and long-run emphasis) of ROIs were calculated. Those three equations were used to construct the texture index (TI) and bone index (BI). All of the presented features distinguish melanomas, benign and dysplastic lesions under polarized light very well. In non-polarized images, only the long-run emphasis moment and both indices effectively differentiated nevi from melanomas. TA is an objective method of assessing pigmented lesions and can be used in automatic diagnostic systems.

Point prevalence of non-melanoma and melanoma skin cancers in Australian surfers and swimmers in Southeast Queensland and Northern New South Wales

Background

Surfing and swimming are two popular outdoor aquatic activities in Australia with an estimated 2.7 million surfers and three million swimmers; however, these activities are associated with intermittent exposure to ultraviolet radiation. Our aim was to determine the point prevalence of pre-skin cancer (actinic keratosis (PSC)), non-melanoma (NMSC) and melanoma skin cancers (MSC) in Australian surfers and swimmers.

Methods

This cross-sectional study involved Australian surfers who completed a survey that included physiological demographics, aquatic activity-specific demographics, history of skin cancer followed by screening.

Results

A total of 171 surfers (n = 116) and swimmers (n = 55) participated in the study. Both groups were identified as having a history of skin cancer (surfers 41.4%, swimmers 36.4%) and a family history of skin cancer (surfers 52.6%, swimmers 43.6%). The majority of both groups reported using a high percentage of a chemical or physical skin cancer prevention strategy (surfers 100%, Swimmers 92.7%, P = 0.003). Significantly more surfers were identified with a skin cancer of any type vs. swimmers (50% vs. 27.3%; OR 2.67; P = 0.005) with most the common skin cancer being PSC (44.7% vs. 11.3%, P = 0.076) followed by basal cell carcinoma (BCC) (24.2% vs. 7.6%, P = 0.068). There was a total of seven MSC identified in surfers and swimmers (4.6% vs. 0.8%, respectively, P = 0.137). Most skin cancers in surfers were located on the face (28.0%) followed by the arm and back (12.1% each), whereas in swimmers, the majority of skin cancers were identified on the face (17.3%), followed by the arm and lower leg (15.4% each). The highest number of melanomas were identified in surfers (n = 6) and mainly located on the face (n = 2) and back (n = 2). There was a single melanoma identified on the back in a swimmer. With the groups combined, the majority (42.9%) of melanomas were identified on the back in participants, followed by the face (28.6%). Rates per 100,000 of NMSC and MSC in surfers and swimmers (respectively) were BCC (11,206 vs. 14,545), squamous cell carcinoma (SCC) in situ (13,793 vs. 12,727), SCC (1,724 vs. 3,636) and MSC (5,172 vs. 1,818). When compared to the general Australian population, surfers and swimmers had higher odds ratios (OR), which included BCCs (OR 7.3 and 9.4, respectively), SCCs (OR 1.7 and 3.5, respectively) and MSC (OR 96.7 and 18.8, respectively).

Conclusion

Surfers and swimmers had consistently higher rates of PSC, NMSC and MSC than the general Australian population. Point prevalence of MSC (groups combined) was 76-fold higher than the general Australian population. These findings highlight the clinical importance of regular skin cancer screenings in individuals who surf or swim for early detection and treatment of skin cancer. Additionally, these aquatic enthusiasts should be advised of the benefits of sun protection strategies such as chemical and physical barriers to reduce the likelihood of developing skin cancer.

Development and Validation of a Novel Survival Model for Cutaneous Melanoma Based on Necroptosis-Related Genes

Background

Necroptosis is crucial for organismal development and pathogenesis. To date, the role of necroptosis in skin cutaneous melanoma (SKCM) is yet unveiled. In addition, the part of melanin pigmentation was largely neglected in the bioinformatic analysis. In this study, we aimed to construct a novel prognostic model based on necroptosis-related genes and analysis the pigmentation phenotype of patients to provide clinically actionable information for SKCM patients.

Methods

We downloaded the SKCM data from the TCGA and GEO databases in this study and identified the differently expressed and prognostic necroptosis-related genes. Patients’ pigmentation phenotype was evaluated by the GSVA method. Then, using Lasso and Cox regression analysis, a novel prognostic model was constructed based on the intersected genes. The risk score was calculated and the patients were divided into two groups. The survival differences between the two groups were compared using Kaplan-Meier analysis. The ROC analysis was performed and the area under curves was calculated to evaluate the prediction performances of the model. Then, the GO, KEGG and GSEA analyses were performed to elucidate the underlying mechanisms. Differences in the tumor microenvironment, patients’ response to immune checkpoint inhibitors (ICIs) and pigmentation phenotype were analyzed. In order to validate the mRNA expression levels of the selected genes, quantitative real-time PCR (qRT-PCR) was performed.

Results

Altogether, a novel prognostic model based on four genes (BOK, CD14, CYLD and FASLG) was constructed, and patients were classified into high and low-risk groups based on the median risk score. Low-risk group patients showed better survival status. The model showed high accuracy in the training and the validation cohort. Pathway and functional enrichment analysis indicated that immune-related pathways were differently activated in the two groups. In addition, immune cells infiltration patterns and sensitivity of ICIs showed a significant difference between patients from two risk groups. The pigmentation score was positively related to the risk score in pigmentation phenotype analysis.

Conclusion

In conclusion, this study established a novel prognostic model based on necroptosis-related genes and revealed the possible connections between necroptosis and melanin pigmentation. It is expected to provide a reference for clinical treatment.

Follow-up of primary melanoma patients with high risk of recurrence: recommendations based on evidence and consensus

In spite of the good prognosis of patients with early-stage melanoma, there is a substantial proportion of them that develop local or distant relapses. With the introduction of targeted and immune therapies for advanced melanoma, including at the adjuvant setting, early detection of recurrent melanoma and/or second primary lesions is crucial to improve clinical outcomes. However, there is a lack of universal guidelines regarding both frequency of surveillance visits and diagnostic imaging and/or laboratory evaluations. In this article, a multidisciplinary expert panel recommends, after careful review of relevant data in the field, a consensus- and experience-based follow-up strategy for melanoma patients, taking into account prognostic factors and biomarkers and the high-risk periods and patterns of recurrence in each (sub) stage of the disease. Apart from the surveillance intensity, healthcare professionals should focus on patients' education to perform regular self-examinations of the skin and palpation of lymph nodes.

Usefulness of Smartphones in Dermatology: A US-Based Review

(1) Background: As smartphones have become more widely used, they have become an appealing tool for health-related functions. For dermatology alone, hundreds of applications (apps) are available to download for both patients and providers. (2) Methods: The Google Play Store and Apple App Store were searched from the United States using dermatology-related terms. Apps were categorized based on description, and the number of reviews, download cost, target audience, and use of AI were recorded. The top apps from each category by number of reviews were reported. Additionally, literature on the benefits and limitations of using smartphones for dermatology were reviewed. (3) Results: A total of 632 apps were included in the study: 395 (62.5%) were marketed towards patients, 203 (32.1%) towards providers, and 34 (5.4%) towards both; 265 (41.9%) were available only on the Google Play Store, 146 (23.1%) only on the Apple App Store, and 221 (35.0%) were available on both; and 595 (94.1%) were free to download and 37 (5.9%) had a cost to download, ranging from USD 0.99 to USD 349.99 (median USD 37.49). A total of 99 apps (15.7%) reported the use of artificial intelligence. (4) Conclusions: Although there are many benefits of using smartphones for dermatology, lack of regulation and high-quality evidence supporting the efficacy and accuracy of apps hinders their potential.

Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome

Weltweit steigt die Inzidenz des malignen Melanoms an. Bei frühzeitiger Erkennung ist das Melanom gut behandelbar, eine Früherkennung ist also lebenswichtig.

Die Hautkrebs-Früherkennung hat sich in den letzten Jahrzehnten bspw. durch die Einführung des Screenings im Jahr 2008 und die Dermatoskopie deutlich verbessert. Dennoch bleibt die visuelle Erkennung insbesondere von frühen Melanomen eine Herausforderung, weil diese viele morphologische Überlappungen mit Nävi zeigen. Daher ist der medizinische Bedarf weiterhin hoch, die Methoden zur Hautkrebsfrüherkennung gezielt weiterzuentwickeln, um Melanome bereits in einem sehr frühen Stadium sicher diagnostizieren zu können.

Die Routinediagnostik zur Hautkrebs-Früherkennung umfasst die visuelle Ganzkörperinspektion, oft ergänzt durch die Dermatoskopie, durch die sich die diagnostische Treffsicherheit erfahrener Hautärzte deutlich erhöhen lässt. Ein Verfahren, was in einigen Praxen und Kliniken zusätzlich angeboten wird, ist die kombinierte Ganzkörperfotografie mit der digitalen Dermatoskopie für die Früherkennung maligner Melanome, insbesondere für das Monitoring von Hochrisiko-Patienten.

In den letzten Jahrzenten wurden zahlreiche nicht invasive zusatzdiagnostische Verfahren zur Beurteilung verdächtiger Pigmentmale entwickelt, die das Potenzial haben könnten, eine verbesserte und z. T. automatisierte Bewertung dieser Läsionen zu ermöglichen. In erster Linie ist hier die konfokale Lasermikroskopie zu nennen, ebenso die elektrische Impedanzspektroskopie, die Multiphotonen-Lasertomografie, die Multispektralanalyse, die Raman-Spektroskopie oder die optische Kohärenztomografie. Diese diagnostischen Verfahren fokussieren i. d. R. auf hohe Sensitivität, um zu vermeiden, ein malignes Melanom zu übersehen. Dies bedingt allerdings üblicherweise eine geringere Spezifität, was im Screening zu unnötigen Exzisionen vieler gutartiger Läsionen führen kann. Auch sind einige der Verfahren zeitaufwendig und kostenintensiv, was die Anwendbarkeit im Screening ebenfalls einschränkt.

In naher Zukunft wird insbesondere die Nutzung von künstlicher Intelligenz die Diagnosefindung in vielfältiger Weise verändern. Vielversprechend ist v. a. die Analyse der makroskopischen und dermatoskopischen Routine-Bilder durch künstliche Intelligenz. Für die Klassifizierung von pigmentierten Hautläsionen anhand makroskopischer und dermatoskopischer Bilder erzielte die künstliche Intelligenz v. a. in Form neuronaler Netze unter experimentellen Bedingungen in zahlreichen Studien bereits eine vergleichbare diagnostische Genauigkeit wie Dermatologen. Insbesondere bei der binären Klassifikationsaufgabe Melanom/Nävus erreichte sie hohe Genauigkeiten, doch auch in der Multiklassen-Differenzierung von verschiedenen Hauterkrankungen zeigt sie sich vergleichbar gut wie Dermatologen. Der Nachweis der grundsätzlichen Anwendbarkeit und des Nutzens solcher Systeme in der klinischen Praxis steht jedoch noch aus. Noch zu schaffende Grundvoraussetzungen für die Translation solcher Diagnosesysteme in die dermatologischen Routine sind Möglichkeiten für die Nutzer, die Entscheidungen des Systems nachzuvollziehen, sowie eine gleichbleibend gute Leistung der Algorithmen auf Bilddaten aus fremden Kliniken und Praxen.

Derzeit zeichnet sich ab, dass computergestützte Diagnosesysteme als Assistenzsysteme den größten Nutzen bringen könnten, denn Studien deuten darauf hin, dass eine Kombination von Mensch und Maschine die besten Ergebnisse erzielt. Diagnosesysteme basierend auf künstlicher Intelligenz sind in der Lage, Merkmale schnell, quantitativ, objektiv und reproduzierbar zu erfassen, und könnten somit die Medizin auf eine mathematische Grundlage stellen – zusätzlich zur ärztlichen Erfahrung.

Discrimination Between Invasive and In Situ Melanomas Using Clinical Close-Up Images and a De Novo Convolutional Neural Network

Background: Melanomas are often easy to recognize clinically but determining whether a melanoma is in situ (MIS) or invasive is often more challenging even with the aid of dermoscopy. Recently, convolutional neural networks (CNNs) have made significant and rapid advances within dermatology image analysis. The aims of this investigation were to create a de novo CNN for differentiating between MIS and invasive melanomas based on clinical close-up images and to compare its performance on a test set to seven dermatologists.

Methods: A retrospective study including clinical images of MIS and invasive melanomas obtained from our department during a five-year time period (2016–2020) was conducted. Overall, 1,551 images [819 MIS (52.8%) and 732 invasive melanomas (47.2%)] were available. The images were randomized into three groups: training set (n = 1,051), validation set (n = 200), and test set (n = 300). A de novo CNN model with seven convolutional layers and a single dense layer was developed.

Results: The area under the curve was 0.72 for the CNN (95% CI 0.66–0.78) and 0.81 for dermatologists (95% CI 0.76–0.86) (P < 0.001). The CNN correctly classified 208 out of 300 lesions (69.3%) whereas the corresponding number for dermatologists was 216 (72.0%). When comparing the CNN performance to each individual reader, three dermatologists significantly outperformed the CNN.

Conclusions: For this classification problem, the CNN was outperformed by the dermatologist. However, since the algorithm was only trained and validated on 1,251 images, future refinement and development could make it useful for dermatologists in a real-world setting.

Clinical, dermoscopic and histological assessment of melanocytic lesions: a comparative study of the accuracy of the diagnostic methods

BACKGROUND

  Worldwide, the incidence of melanoma is increasing, while late diagnosis is related to poor prognosis. A significant risk marker for melanoma is the presence of atypical nevi; therefore, it is of outstanding importance to make accurate clinical classification of common benign nevi, atypical nevi, and melanomas. The non-invasive method of dermoscopy allowed for the visualization of structures invisible to the naked eye and undoubtedly advanced the assessment of melanocytic lesions to a new dimension. This study aimed to evaluate the sensitivity and specificity of naked-eye examination and dermoscopy in diagnosing melanocytic lesions compared to the histopathological results, constituting the gold standard of diagnosis.

MATERIAL AND METHODS

One hundred eighteen melanocytic lesions were clinically evaluated via the naked eye and dermoscopic examination, using Pattern Analysis Methodology, and afterward, they were excised. The histopathological results were correlated with the findings.

RESULTS

According to the final histopathological analysis, 63 common benign nevi, 41 dysplastic nevi, and 14 cutaneous melanomas were excised in total. Clinical examination via the naked eye showed 78.2 % sensitivity and 71.4 % specificity in identifying the clinical atypia, while dermoscopy demonstrated 89.1 % sensitivity and 93.7 % specificity.

CONCLUSIONS

The results of the present study indicate a higher sensitivity and specificity of dermoscopy in evaluating and diagnosing melanocytic lesions compared to the naked-eye examination. HIPPOKRATIA 2021, 25 (4):156-161.

Assessing exposure to dermoscopy in plastic surgery training programs

BACKGROUND

Dermoscopy is a noninvasive tool that improves the diagnostic accuracy of melanoma and other cutaneous malignancies; yet, it is not widely used by plastic surgeons, who commonly manage skin lesions. Thus, the purpose of this study was to explore current practice patterns and knowledge of dermoscopy among plastic surgeons and postgraduate plastic surgery trainees. Additionally, interest to establish a formal dermoscopy curriculum as part of plastic surgery residency training was evaluated.

METHODS

An online electronic questionnaire was developed and distributed through email to practicing plastic surgeons and plastic surgery trainees at two Canadian universities.

RESULTS

Of the 59 potential participants, 27 (46%) responded. While the majority of participants were familiar with dermoscopy (n = 26; 96%), only one respondent reported using dermoscopy in clinical practice. However, all respondents reported exposure to melanoma clinically (n = 26; one participant did not provide a response). A lack of training, along with lack of access to dermatoscopes, were the most frequently cited reasons for not using dermoscopy. Knowledge scores with regard to dermoscopic features were also low; coupled with a noted propensity toward diagnostic or excisional biopsy, whichcould raise the benign to malignant ratio. Overall, 89% (n = 24) of respondents expressed interest in dermoscopy training in plastic surgery postgraduate training.

CONCLUSIONS

Few responding plastic surgeons or plastic surgery residents currently use dermoscopy in training or practice but are interested in formal dermoscopy training in residency.

Assessment of Diagnostic Accuracy of Dermoscopic Structures and Patterns Used in Melanoma Detection: A Systematic Review and Meta-analysis

Importance

Dermoscopy increases the diagnostic accuracy for melanoma. However, the accuracy of individual structures and patterns used in melanoma detection has not been systematically evaluated.

Objective

To assess the diagnostic accuracy of individual dermoscopic structures and patterns used in melanoma detection.

Data Sources

A search of Ovid Medline, Embase, Cochrane CENTRAL, Scopus, and Web of Science was conducted from inception to July 2020.

Study Selection

Studies evaluating the dermoscopic structures and patterns among melanomas in comparison with nonmelanoma lesions were included. Excluded were studies with fewer than 3 patients, studies in languages other than English or Spanish, studies not reporting dermoscopic structures per lesion type, and studies assessing only nail, mucosal, acral, facial, or metastatic melanomas or melanomas on chronically sun-damaged skin. Multiple reviewers applied these criteria, and 0.7% of studies met selection criteria.

Data Extraction and Synthesis

The Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline and Meta-analysis of Observational Studies in Epidemiology reporting guideline were followed. Guidelines were applied via independent extraction by multiple observers. Data were pooled using a random-effects model.

Main Outcomes and Measures

The prespecified outcome measures were diagnostic accuracy (sensitivity and specificity) and risk (odds ratio [OR]) of melanoma for the following dermoscopic structures/patterns: atypical dots/globules, atypical network, blue-white veil, negative network, off-centered blotch, peripheral-tan structureless areas, atypical vessels (eg, linear irregular, polymorphous), pseudopods, streaks, regression (ie, peppering, scarlike areas), shiny white structures, angulated lines, irregular pigmentation, and a multicomponent pattern.

Results

A total of 40 studies including 22 796 skin lesions and 5736 melanomas were evaluated. The structures and patterns with the highest ORs were shiny white structures (OR, 6.7; 95% CI, 2.5-17.9), pseudopods (OR, 6.7; 95% CI, 2.7-16.1), irregular pigmentation (OR, 6.4; 95% CI, 2.0-20.5), blue-white veil (OR, 6.3; 95% CI, 3.7-10.7), and peppering (OR, 6.3; 95% CI, 2.4-16.1). The structures with the highest specificity were pseudopods (97.3%; 95% CI, 94.3%-98.7%), shiny white structures (93.6%; 95% CI, 85.6%-97.3%), peppering (93.4%; 95% CI, 81.9%-97.8%), and streaks (92.1%; 95% CI, 88.4%-94.7%), whereas features with the highest sensitivity were irregular pigmentation (62.3%; 95% CI, 31.2%-85.8%), blue-white veil (60.6%; 95% CI, 46.7%-72.9%), atypical network (56.8%; 95% CI, 43.6%-69.2%), and a multicomponent pattern (53.7%; 95% CI, 40.4%-66.4%).

Conclusions and Relevance

The findings of this systematic review and meta-analysis support the diagnostic importance of dermoscopic structures associated with melanoma detection (eg, shiny white structures, blue-white veil), further corroborate the importance of the overall pattern, and may suggest a hierarchy in the significance of these structures and patterns.

Advances in the Etiology, Detection, and Clinical Management of Seborrheic Keratoses

Seborrheic keratoses (SKs) are ubiquitous, generally benign skin tumors that exhibit high clinical variability. While age is a known risk factor, the precise roles of UV exposure and immune abnormalities are currently unclear. The underlying mechanisms of this benign disorder are paradoxically driven by oncogenic mutations and may have profound implications for our understanding of the malignant state. Advances in molecular pathogenesis suggest that inhibition of Akt and APP, as well as existing treatments for skin cancer, may have therapeutic potential in SK. Dermoscopic criteria have also become increasingly important to the accurate detection of SK, and other noninvasive diagnostic methods, such as reflectance confocal microscopy and optical coherence tomography, are rapidly developing. Given their ability to mimic malignant tumors, SK cases are often used to train artificial intelligence-based algorithms in the computerized detection of skin disease. These technologies are becoming increasingly accurate and have the potential to significantly augment clinical practice. Current treatment options for SK cause discomfort and can lead to adverse post-treatment effects, especially in skin of color. In light of the discontinuation of ESKATA in late 2019, promising alternatives, such as nitric-zinc and trichloroacetic acid topicals, should be further developed. There is also a need for larger, head-to-head trials of emerging laser therapies to ensure that future treatment standards address diverse patient needs.

Pigmented Lesion Assay for Suspected Melanoma Lesions: A Health Technology Assessment

BACKGROUND

Early detection of melanoma is key, as survival rates are substantially better when the cancer is detected in its early stages. Currently, the standard of care is to biopsy any lesion suspected of melanoma for diagnostic confirmation by histopathology. As a result, most people who undergo biopsy receive negative melanoma results. If effective, a non-invasive alternative, such as pigmented lesion assay, could minimize the number of unnecessary biopsies performed. We conducted a health technology assessment of pigmented lesion assay for people with suspected melanoma lesions, which included an evaluation of diagnostic accuracy, clinical utility, the budget impact of publicly funding pigmented lesion assay, and the preferences and values of people who have undergone biopsy for suspected melanoma.

METHODS

We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each included study using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and the Risk of Bias Assessment Tool for Non-randomized Studies (RoBANS). We assessed the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We performed a systematic literature search of the economic evidence. We also analyzed the budget impact of publicly funding pigmented lesion assay in adults with suspected melanoma in Ontario. To contextualize the potential value of pigmented lesion assay, we spoke with people who had undergone skin biopsy for melanoma. We also used the qualitative research synthesis from a report by the Canadian Agency for Drugs and Technologies in Health to provide context for the preferences and values of those with suspected melanoma.

RESULTS

We included seven studies in the clinical evidence review. Pigmented lesion assay has a sensitivity of 79% (95% confidence interval [CI] 58%-93%) and a specificity of 80% (95% CI 73%-85%; GRADE: Low). We found one published cost-effectiveness study with potentially serious limitations. Therefore, the cost-effectiveness of pigmented lesion assay compared with the standard care pathway is currently uncertain. Assuming a very low uptake, we estimated that the budget impact of publicly funding pigmented lesion assay in Ontario over the next 5 years is about $3.44 million if the test is used exclusively by primary care providers, or about $2.56 million if it is used exclusively by specialists. The people with whom we spoke who had experienced biopsy for suspected melanoma responded positively to the potential benefits of pigmented lesion assay, emphasizing its ease-of-use, potential increase in early detection of melanoma, and reduction in physical and emotional burden of unnecessary biopsies. Participants also felt that the accuracy of this tool was essential to ensure minimal false negatives.

CONCLUSIONS

There is uncertainty because of the low-quality evidence for the diagnostic accuracy of pigmented lesion assay. The cost-effectiveness of pigmented lesion assay compared with standard care is also uncertain. We estimated that publicly funding pigmented lesion assay in Ontario over the next 5 years would result in additional costs of $3.44 million (if used exclusively by primary care providers) or $2.56 million (if used exclusively by specialists). For people who had experienced biopsy for suspected melanoma, it was felt that pigmented lesion assay could represent an effective tool to increase early detection and avoid unnecessary biopsies, if the tool was accurate.

Can Dermoscopy Be Used to Predict if a Melanoma Is In Situ or Invasive?

Background

The preoperative prediction of whether melanomas are invasive or in situ can influence initial management.

Objectives

This study evaluated the accuracy rate, interobserver concordance, sensitivity and specificity in determining if a melanoma is invasive or in situ, as well as the ability to predict invasive melanoma thickness based on clinical and dermoscopic images.

Methods

In this retrospective, single-center investigation, 7 dermatologists independently reviewed clinical and dermoscopic images of melanomas to predict if they were invasive or in situ and, if invasive, their Breslow thickness. Fleiss' and Cohen's kappa (κ) were used for interobserver concordance and agreement with histopathological diagnosis.

Results

We included 184 melanomas (110 invasive and 74 in situ). Diagnostic accuracy ranged from 67.4% to 76.1%. Accuracy rates for in situ and invasive melanomas were 57.5% (95% confidence interval [CI], 53.1%-61.8%) and 81.7% (95% CI, 78.8%-84.4%), respectively. Interobserver concordance was moderate (κ = 0.47; 95% CI, 0.44-0.51). Sensitivity for predicting invasiveness ranged from 63.6% to 91.8% for 7 observers, while specificity was 32.4%-82.4%. For all correctly predicted invasive melanomas, agreement between predictions and correct thickness over or under 1.0 mm was moderate (κ = 0.52; 95% CI, 0.45-0.58). All invasive melanomas incorrectly predicted by any observer as in situ had a thickness <1.0 mm. All 32 melanomas >1.0 mm were correctly predicted to be invasive by all observers.

Conclusions

Accuracy rates for predicting thick melanomas were excellent, melanomas inaccurately predicted as in situ were all thin, and interobserver concordance for predicting in situ or invasive melanomas was moderate. Preoperative dermoscopy of suspected melanomas is recommended for choosing appropriate surgical margins.

Training general practitioners in melanoma diagnosis: a scoping review of the literature

BACKGROUND

General practitioners (GPs) play a key role in early melanoma detection. To help GPs deal with suspicious skin lesions, melanoma diagnostic training programmes have been developed. However, it is unclear whether these programmes guarantee the acquisition of skills that will be applied by GPs in their daily clinical practice and maintained over time.

OBJECTIVES

This scoping review aimed to examine and compare educational programmes designed to train GPs in melanoma diagnosis using clinical (naked eye) examination alone or dermoscopy±clinical examination, and sought to inform on the long-term sustainability of the GPs' acquired skills.

ELIGIBILITY CRITERIA

Studies eligible for inclusion evaluated educational programmes for teaching diagnosis of melanoma to GPs. MEDLINE, EMBASE and Cochrane databases were searched for relevant articles from 1995 to May 2020.

RESULTS

Forty-five relevant articles were found assessing 31 educational programmes. Most programmes that improved the diagnostic accuracy and long-term performances of the GPs, that is, increase in confidence, decrease in dermatologist referral for benign skin lesions and improvement in the benign/malignant ratio of excised skin lesions, trained the GPs in clinical diagnosis, followed by dermoscopy. To maintain long-term performances, these programmes provided refresher training material.

CONCLUSION

This review shows that studies generally report positive outcomes from the training of GPs in melanoma diagnosis. However, refresher training material seemed necessary to maintain the acquired skills. The optimal form and ideal frequency for these updates have yet to be defined.

Melanoma diagnosis using deep learning techniques on dermatoscopic images

BACKGROUND

Melanoma has become more widespread over the past 30 years and early detection is a major factor in reducing mortality rates associated with this type of skin cancer. Therefore, having access to an automatic, reliable system that is able to detect the presence of melanoma via a dermatoscopic image of lesions and/or skin pigmentation can be a very useful tool in the area of medical diagnosis.

METHODS

Among state-of-the-art methods used for automated or computer assisted medical diagnosis, attention should be drawn to Deep Learning based on Convolutional Neural Networks, wherewith segmentation, classification and detection systems for several diseases have been implemented. The method proposed in this paper involves an initial stage that automatically crops the region of interest within a dermatoscopic image using the Mask and Region-based Convolutional Neural Network technique, and a second stage based on a ResNet152 structure, which classifies lesions as either "benign" or "malignant".

RESULTS

Training, validation and testing of the proposed model was carried out using the database associated to the challenge set out at the 2017 International Symposium on Biomedical Imaging. On the test data set, the proposed model achieves an increase in accuracy and balanced accuracy of 3.66% and 9.96%, respectively, with respect to the best accuracy and the best sensitivity/specificity ratio reported to date for melanoma detection in this challenge. Additionally, unlike previous models, the specificity and sensitivity achieve a high score (greater than 0.8) simultaneously, which indicates that the model is good for accurate discrimination between benign and malignant lesion, not biased towards any of those classes.

CONCLUSIONS

The results achieved with the proposed model suggest a significant improvement over the results obtained in the state of the art as far as performance of skin lesion classifiers (malignant/benign) is concerned.

Overcoming Immune Evasion in Melanoma

Melanoma is the most aggressive and dangerous form of skin cancer that develops from transformed melanocytes. It is crucial to identify melanoma at its early stages, in situ, as it is "curable" at this stage. However, after metastasis, it is difficult to treat and the five-year survival is only 25%. In recent years, a better understanding of the etiology of melanoma and its progression has made it possible for the development of targeted therapeutics, such as vemurafenib and immunotherapies, to treat advanced melanomas. In this review, we focus on the molecular mechanisms that mediate melanoma development and progression, with a special focus on the immune evasion strategies utilized by melanomas, to evade host immune surveillances. The proposed mechanism of action and the roles of immunotherapeutic agents, ipilimumab, nivolumab, pembrolizumab, and atezolizumab, adoptive T- cell therapy plus T-VEC in the treatment of advanced melanoma are discussed. In this review, we implore that a better understanding of the steps that mediate melanoma onset and progression, immune evasion strategies exploited by these tumor cells, and the identification of biomarkers to predict treatment response are critical in the design of improved strategies to improve clinical outcomes for patients with this deadly disease.

Patients' perception of using a smartphone light source in the clinical environment

Introduction A satisfactory light source is of paramount importance during an oral head and neck examination. It has become common practice for the light source on a smartphone to be used during inpatient intra-oral examination. We determined if patients identified the use of a smartphone as a light source, during head and neck examinations, as unprofessional.Methods and materials A clinical photograph illustrating professional errors was presented to patients as a pilot survey (n = 10); a smartphone camera flash being used as a light source was featured. Patients were then asked which aspects they considered unprofessional. Following staff training and improvements to the survey wording, the same photo was presented to patients (n = 150) as the main study.Results Of the patients surveyed, 97% considered the use of a smartphone in the staged clinical photograph as unprofessional. They also noted: a clinician sitting on the bed (88%), clinicians not wearing gloves (81%), lack of privacy/curtain not drawn (62%), long hair not tied back (50%), a clinician's name badge not visible (23%), clinician bare below the elbows (15%) and clinician not wearing a tie (12%).Conclusions This is the first piece of research into the use of a smartphone light source within clinical examination. The use of a smart phone light source during clinical examination was the most reported 'error' and was recognised more than a clinician not wearing gloves. Eighty-five percent of patients considered the use of a smartphone light source unprofessional. Authors, therefore, do not advocate the use of a smartphone light source in the clinical care of patients.

Development of a clinical-dermoscopic model for the diagnosis of urticarial vasculitis

The clinical criteria for the diagnosis of urticarial vasculitis lack accuracy, according to previous studies. The aim of the study was to assess the accuracy of a clinical and a clinical-dermoscopic model for the differential diagnosis of chronic spontaneous urticaria (CSU) and urticarial vasculitis (UV). Dermoscopic images of lesions with histopathologically confirmed diagnosis of CSU and UV were evaluated for the presence of selected criteria (purpuric patches/globules (PG) and red linear vessels). Clinical criteria of CSU and UV were also registered. Univariate and adjusted odds ratios were calculated. Multivariate regression analyses were conducted separately for clinical variables (clinical diagnostic model) and for both clinical and dermoscopic variables (clinical-dermoscopic diagnostic model). 108 patients with CSU and 27 patients with UV were included in the study. The clinical-dermoscopic model notably showed higher diagnostic sensitivity than the clinical approach (63% vs. 44%). Dermoscopic purpuric patches/globules (PG) was the variable that better discriminated UV, increasing by 19-fold the odds for this diagnosis. In conclusion, dermoscopy helps the clinical discrimination between CSU and UV. The visualization of dermoscopic PG may contribute to optimize decisions regarding biopsy in patients with urticarial rashes.

What is AI? Applications of artificial intelligence to dermatology

In the past, the skills required to make an accurate dermatological diagnosis have required exposure to thousands of patients over many years. However, in recent years, artificial intelligence (AI) has made enormous advances, particularly in the area of image classification. This has led computer scientists to apply these techniques to develop algorithms that are able to recognize skin lesions, particularly melanoma. Since 2017, there have been numerous studies assessing the accuracy of algorithms, with some reporting that the accuracy matches or surpasses that of a dermatologist. While the principles underlying these methods are relatively straightforward, it can be challenging for the practising dermatologist to make sense of a plethora of unfamiliar terms in this domain. Here we explain the concepts of AI, machine learning, neural networks and deep learning, and explore the principles of how these tasks are accomplished. We critically evaluate the studies that have assessed the efficacy of these methods and discuss limitations and potential ethical issues. The burden of skin cancer is growing within the Western world, with major implications for both population skin health and the provision of dermatology services. AI has the potential to assist in the diagnosis of skin lesions and may have particular value at the interface between primary and secondary care. The emerging technology represents an exciting opportunity for dermatologists, who are the individuals best informed to explore the utility of this powerful novel diagnostic tool, and facilitate its safe and ethical implementation within healthcare systems.

What is already known about this topic?

There is considerable interest in the application of artificial intelligence to medicine.

Several publications in recent years have described computer algorithms that can diagnose melanoma or skin lesions.

Multiple groups have independently evaluated algorithms for the diagnosis of melanoma and skin lesions.

What does this study add?

We combine an introduction to the field with a summary of studies comparing dermatologists against artificial intelligence algorithms with the aim of providing a comprehensive resource for clinicians.

This review will equip clinicians with the relevant knowledge to critically appraise future studies, and also assess the clinical utility of this technology.

A better informed and engaged cohort of clinicians will ensure that the technology is applied effectively and ethically.

Plain language summary available online