Diagnostic accuracy of dermoscopy

Simple, ultrasensitive detection of superoxide anion radical mutations in melanoma mice with SERS microneedles

Elevated levels of superoxide anion radicals (O2·-) have been implicated in the pathogenesis of a variety of diseases, such as cancer, inflammatory diseases and autoimmune diseases. To determine the O2·- concentration for assisting disease detection, a method based on surface-enhanced Raman scattering (SERS) combined with transparent polymer microneedles has been developed. Photocrosslinked NOA61 is used to prepare microneedles with sulfhydryl group, which can contribute to anchor gold nanoparticles (Au NPs) functionalized by p-mercaptobenzoic acid (PATP). This work successfully constructed SERS microneedles for in situ detection. A REDOX reaction occurred between PATP and O2·-, resulting in the formation of dimethylaminoborane (DMAB) and a subsequent change in Raman signal. Based on the quantitative relationship between the change of peak area ratio at 1042 cm-1 and 1077 cm-1 and the concentration change of O2·-, a standard curve with a linear range of 0-480 ng/mL was constructed. The SERS microneedles were effectively employed to track melanoma progression in mice, establishing a fundamental correlation between O2·- concentration and melanoma stage, as confirmed by ELISA. The benefits of this approach, including convenience, in situ applicability, and low cost, are anticipated to offer novel insights for non-invasive in situ detection, potentially enhancing disease monitoring and diagnosis.

Clinicopathologically Defined Naevus Subtypes and Melanoma Risk

Early detection of melanoma is a major determinant in disease outcome and drives the number of (over)excised naevi in clinical practice. This study aimed to evaluate demographic features and melanoma risk of clinically suspicious, mainly flat naevus subtypes. Based on the methodology of ex vivo dermoscopy and derm dotting, the 12 most prevalent naevus subtypes were identified in a collection of over 7000 naevi excised for medical reason. Dermoscopical, histopathological and clinical features of these subtypes were described. In addition, the association with melanoma history, histopathological atypia and melanoma occurrence within naevi was compared. Nearly half of the naevi removed for medical reasons were of the hypermelanotic subtype with no or mild histopathological atypia and low melanoma association, suggesting overtreatment in daily practice. Contrarily, the subtypes atypical lentiginous naevus and orange pulverocytic flat naevus were associated with higher proportions of (severe) atypia and melanoma (history). We believe these subtypes may reflect different tumoural and/or (germline) genetic entities with different melanoma risk. The data from this study may direct further prospective research on specific naevus subtypes in order to obtain better insights in associated clinical/genetic factors and melanoma risk.

Dermoscopy Differential Diagnosis Explorer (D3X) Ontology to Aggregate and Link Dermoscopic Patterns to Differential Diagnoses: Development and Usability Study

BACKGROUND

Dermoscopy is a growing field that uses microscopy to allow dermatologists and primary care physicians to identify skin lesions. For a given skin lesion, a wide variety of differential diagnoses exist, which may be challenging for inexperienced users to name and understand.

OBJECTIVE

In this study, we describe the creation of the dermoscopy differential diagnosis explorer (D3X), an ontology linking dermoscopic patterns to differential diagnoses.

METHODS

Existing ontologies that were incorporated into D3X include the elements of visuals ontology and dermoscopy elements of visuals ontology, which connect visual features to dermoscopic patterns. A list of differential diagnoses for each pattern was generated from the literature and in consultation with domain experts. Open-source images were incorporated from DermNet, Dermoscopedia, and open-access research papers.

RESULTS

D3X was encoded in the OWL 2 web ontology language and includes 3041 logical axioms, 1519 classes, 103 object properties, and 20 data properties. We compared D3X with publicly available ontologies in the dermatology domain using a semiotic theory-driven metric to measure the innate qualities of D3X with others. The results indicate that D3X is adequately comparable with other ontologies of the dermatology domain.

CONCLUSIONS

The D3X ontology is a resource that can link and integrate dermoscopic differential diagnoses and supplementary information with existing ontology-based resources. Future directions include developing a web application based on D3X for dermoscopy education and clinical practice.

Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial

BACKGROUND

Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has, in several retrospective studies, shown high levels of diagnostic accuracy on par with - or even outperforming - experienced dermatologists. However, the enthusiasm around these algorithms has not yet been matched by prospective clinical trials performed in authentic clinical settings. In several European countries, including Sweden, the initial clinical assessment of suspected skin cancer is principally conducted in the primary healthcare setting by primary care physicians, with or without access to teledermoscopic support from dermatology clinics.

OBJECTIVES

To determine the diagnostic performance of an AI-based clinical decision support tool for cutaneous melanoma detection, operated by a smartphone application (app), when used prospectively by primary care physicians to assess skin lesions of concern due to some degree of melanoma suspicion.

METHODS

This prospective multicentre clinical trial was conducted at 36 primary care centres in Sweden. Physicians used the smartphone app on skin lesions of concern by photographing them dermoscopically, which resulted in a dichotomous decision support text regarding evidence for melanoma. Regardless of the app outcome, all lesions underwent standard diagnostic procedures (surgical excision or referral to a dermatologist). After investigations were complete, lesion diagnoses were collected from the patients' medical records and compared with the app's outcome and other lesion data.

RESULTS

In total, 253 lesions of concern in 228 patients were included, of which 21 proved to be melanomas, with 11 thin invasive melanomas and 10 melanomas in situ. The app's accuracy in identifying melanomas was reflected in an area under the receiver operating characteristic (AUROC) curve of 0.960 [95% confidence interval (CI) 0.928-0.980], corresponding to a maximum sensitivity and specificity of 95.2% and 84.5%, respectively. For invasive melanomas alone, the AUROC was 0.988 (95% CI 0.965-0.997), corresponding to a maximum sensitivity and specificity of 100% and 92.6%, respectively.

CONCLUSIONS

The clinical decision support tool evaluated in this investigation showed high diagnostic accuracy when used prospectively in primary care patients, which could add significant clinical value for primary care physicians assessing skin lesions for melanoma.

BCN20000: Dermoscopic Lesions in the Wild

Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a collection of 18,946 dermoscopic images spanning from 2010 to 2016, collated at the Hospital Clínic in Barcelona, Spain. The BCN20000 dataset aims to address the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions in hard-to-diagnose locations such as those found in nails and mucosa, large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. Our dataset covers eight key diagnostic categories in dermoscopy, providing a diverse range of lesions for artificial intelligence model training. Furthermore, a ninth out-of-distribution (OOD) class is also present on the test set, comprised of lesions which could not be distinctively classified as any of the others. By providing a comprehensive collection of varied images, BCN20000 helps bridge the gap between the training data for machine learning models and the day-to-day practice of medical practitioners. Additionally, we present a set of baseline classifiers based on state-of-the-art neural networks, which can be extended by other researchers for further experimentation.

Comparison of Extended Skin Cancer Screening Using a Three-Step Advanced Imaging Programme vs. Standard-of-Care Examination in a High-Risk Melanoma Patient Cohort

Modern diagnostic procedures, such as three-dimensional total body photography (3D-TBP), digital dermoscopy (DD), and reflectance confocal microscopy (RCM), can improve melanoma diagnosis, particularly in high-risk patients. This study assessed the benefits of combining these advanced imaging techniques in a three-step programme in managing high-risk patients. This study included 410 high-risk melanoma patients who underwent a specialised imaging consultation in addition to their regular skin examinations in outpatient care. At each visit, the patients underwent a 3D-TBP, a DD for suspicious findings, and an RCM for unclear DD findings. The histological findings of excisions initiated based on imaging consultation and outpatient care were compared. Imaging consultation detected sixteen confirmed melanomas (eight invasive and eight in situ) in 39 excised pigmented lesions. Outpatient care examination detected seven confirmed melanomas (one invasive and six in situ) in 163 excised melanocytic lesions. The number needed to excise (NNE) in the imaging consultation was significantly lower than that in the outpatient care (2.4 vs. 23.3). The NNE was 2.6 for DD and 2.3 for RCM. DD, 3D-TBP, or RCM detected melanomas that were not detected by the other imaging methods. The three-step imaging programme improves melanoma detection and reduces the number of unnecessary excisions in high-risk patients.

Understanding the anatomy of dermoscopy of melanocytic skin tumours: Correlation in vivo with line-field optical coherence tomography

BACKGROUND

Early melanoma detection is the main factor affecting prognosis and survival. For that reason, non-invasive technologies have been developed to provide a more accurate diagnosis. Recently, line-field confocal optical coherence tomography (LC-OCT) was developed to provide an in vivo, imaging device, with deep penetration and cellular resolution in three dimensions. Combining the advantages of conventional OCT and reflectance confocal microscopy, this tool seems to be particularly suitable for melanocytic lesions.

OBJECTIVES

The objective of this study was to identify and describe the correlation between specific dermoscopic criteria and LC-OCT features in three dimensions associated with melanocytic lesions.

METHODS

Dermoscopic and LC-OCT images of 126 melanocytic lesions were acquired in three different centres. The following dermoscopic criteria have been considered: reticular pattern, dots and globules, structureless areas, blue-whitish veil, regression structures, negative network, homogeneous pattern, streaks and blotches.

RESULTS

69 (55%) benign and 57 (45%) malignant lesions were analysed. A regular reticular pattern was found associated in the 75% of the cases with the presence of elongated rete ridges with pigmented cells along the basal layer, while atypical reticular pattern showed an irregular organization of rete ridges with melanocytic hyperplasia, broadened and fused ridges and elongated nests. Both typical and atypical dots and globules were found associated with melanocytic nests in the dermis or at the dermoepidermal junction (DEJ), as well as with keratin cysts/pseudocysts. Grey globules corresponded to the presence of melanin-containing dermal inflammatory cells (melanophages) within the papillae. Structureless brown/black areas correlated with alterations of the DEJ. We observed the same DEJ alterations, but with the presence of dermal melanophages, in 36% of the cases of blue/white/grey structureless areas. A description of each LC-OCT/dermoscopy correlation was made.

CONCLUSIONS

LC-OCT permitted for the first time to perform an in vivo, 3D correlation between dermoscopic criteria and pathological-like features of melanocytic 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.

The effect of a dermoscopy training programme on diagnostic accuracy and management decisions regarding pigmented skin lesions: a comparison between dermal therapists and general practitioners

BACKGROUND

Dermoscopy is known to increase the diagnostic accuracy of pigmented skin lesions (PSLs) when used by trained professionals. The effect of dermoscopy training on the diagnostic ability of dermal therapists (DTs) has not been studied so far.

OBJECTIVES

This study aimed to investigate whether DTs, in comparison with general practitioners (GPs), benefited from a training programme including dermoscopy, in both their ability to differentiate between different forms of PSL and to assign the correct therapeutic strategy.

METHODS

In total, 24 DTs and 96 GPs attended a training programme on PSLs. Diagnostic skills as well as therapeutic strategy were assessed, prior to the training (pretest) and after the training (post-test) using clinical images alone, as well as after the addition of dermatoscopic images (integrated post-test). Bayesian hypothesis testing was used to determine statistical significance of differences between pretest, post-test and integrated post-test scores.

RESULTS

Both the DTs and the GPs demonstrated benefit from the training: at the integrated post-test, the median proportion of correctly diagnosed PSLs was 73% (range 30-90) for GPs and 63% (range 27-80) for DTs. A statistically significant difference between pretest results and integrated test results was seen, with a Bayes factor > 100. At 12 percentage points higher, the GPs outperformed DTs in the accuracy of detecting PSLs.

CONCLUSIONS

The study shows that a training programme focusing on PSLs while including dermoscopy positively impacts detection of PSLs by DTs and GPs. This training programme could form an integral part of the training of DTs in screening procedures, although additional research is needed.

Dermoscopy can be safely and reliably used in ophthalmology

Objective

To determine if dermoscopy, a technique widely utilized in dermatology for improved diagnosis of skin lesions, can be used comfortably for evaluating periorbital, eyelid, and conjunctival lesions.

Design

Proof-of-concept study in which a technique for performing dermoscopy near the eye was developed, related educational material was prepared, and a protocol for dermoscopic image capture was created.

Methods

Technicians used the developed materials to learn to take high-quality pictures with a 10x dermoscope attached to a standard cell phone camera. The images were assessed for diagnostic utility by an oculoplastic surgeon and two dermatologists.

Participants

115 patients recruited from ophthalmology clinics from July 2021 to April 2023 were photographed, yielding 129 lesions with high-quality dermoscopic images as assessed by an oculoplastic surgeon and two dermatologists.

Results

Technicians reported a significant increase in confidence (measured on a 1-10 scale) with dermoscopy after training (pre-instruction mean = 1.72, median = 1, mode = 1, IQR = 1.25 vs mean = 7.69, median = 7.75, mode = 7 and 8, IQR = 1.5 post-instruction. Wilcoxon rank sum test with continuity correction, W = 0, p < 0.001, paired t = 13.95, p < 0.0001). Incorporating a contact plate with a 4 × 4mm reticule on the dermoscope aided in photographing ocular and periocular lesions.

Conclusion

Medical support staff in eye-care offices can be taught to use dermoscopes to capture high-quality images of periorbital, eyelid, and conjunctival lesions. Dermoscopy illuminates diagnostic features of lesions and thus offers a new avenue to improve decision-making in ophthalmology. Dermoscopy can be incorporated into telemedicine evaluations by ophthalmologists, oculoplastic surgeons, or affiliated dermatologists for triage of or rendering advice to patients and for planning of surgery if needed.

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.

Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance

IMPORTANCE

Total body photography for skin cancer screening is a well-established tool allowing documentation and follow-up of the entire skin surface. Artificial intelligence-based systems are increasingly applied for automated lesion detection and diagnosis.

DESIGN AND PATIENTS

In this prospective observational international multicentre study experienced dermatologists performed skin cancer screenings and identified clinically relevant melanocytic lesions (CRML, requiring biopsy or observation). Additionally, patients received 2D automated total body mapping (ATBM) with automated lesion detection (ATBM master, Fotofinder Systems GmbH). Primary endpoint was the percentage of CRML detected by the bodyscan software. Secondary endpoints included the percentage of correctly identified "new" and "changed" lesions during follow-up examinations.

RESULTS

At baseline, dermatologists identified 1075 CRML in 236 patients and 999 CRML (92.9%) were also detected by the automated software. During follow-up examinations dermatologists identified 334 CRMLs in 55 patients, with 323 (96.7%) also being detected by ATBM with automated lesions detection. Moreover, all new (n = 13) or changed CRML (n = 24) during follow-up were detected by the software. Average time requirements per baseline examination was 14.1 min (95% CI [12.8-15.5]). Subgroup analysis of undetected lesions revealed either technical (e.g. covering by clothing, hair) or lesion-specific reasons (e.g. hypopigmentation, palmoplantar sites).

CONCLUSIONS

ATBM with lesion detection software correctly detected the vast majority of CRML and new or changed CRML during follow-up examinations in a favourable amount of time. Our prospective international study underlines that automated lesion detection in TBP images is feasible, which is of relevance for developing AI-based skin cancer screenings.

Near-infrared hyperspectral imaging and robust statistics for in vivo non-melanoma skin cancer and actinic keratosis characterisation

One of the most common forms of cancer in fair skinned populations is Non-Melanoma Skin Cancer (NMSC), which primarily consists of Basal Cell Carcinoma (BCC), and cutaneous Squamous Cell Carcinoma (SCC). Detecting NMSC early can significantly improve treatment outcomes and reduce medical costs. Similarly, Actinic Keratosis (AK) is a common skin condition that, if left untreated, can develop into more serious conditions, such as SCC. Hyperspectral imagery is at the forefront of research to develop non-invasive techniques for the study and characterisation of skin lesions. This study aims to investigate the potential of near-infrared hyperspectral imagery in the study and identification of BCC, SCC and AK samples in comparison with healthy skin. Here we use a pushbroom hyperspectral camera with a spectral range of ≈ 900 to 1600 nm for the study of these lesions. For this purpose, an ad hoc platform was developed to facilitate image acquisition. This study employed robust statistical methods for the identification of an optimal spectral window where the different samples could be differentiated. To examine these datasets, we first tested for the homogeneity of sample distributions. Depending on these results, either traditional or robust descriptive metrics were used. This was then followed by tests concerning the homoscedasticity, and finally multivariate comparisons of sample variance. The analysis revealed that the spectral regions between 900.66-1085.38 nm, 1109.06-1208.53 nm, 1236.95-1322.21 nm, and 1383.79-1454.83 nm showed the highest differences in this regard, with <1% probability of these observations being a Type I statistical error. Our findings demonstrate that hyperspectral imagery in the near-infrared spectrum is a valuable tool for analyzing, diagnosing, and evaluating non-melanoma skin lesions, contributing significantly to skin cancer research.

Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis

The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6-80.1) and specificity was 81.5% (95% CI 73.9-87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4-86.5) and specificity was 86.1% (95% CI 79.2-90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.

Line-field confocal optical coherence tomography in dermato-oncology: A literature review towards harmonized histopathology-integrated terminology

Non-invasive diagnostics like line-field confocal optical coherence tomography (LC-OCT) are being implemented in dermato-oncology. However, unification of terminology in LC-OCT is lacking. By reviewing the LC-OCT literature in the field of dermato-oncology, this study aimed to develop a unified terminological glossary integrated with traditional histopathology. A PRISMA-guided literature-search was conducted for English-language publications on LC-OCT of actinic keratosis (AK), keratinocyte carcinoma (KC), and malignant melanoma (MM). Study characteristics and terminology were compiled. To harmonize LC-OCT terminology and integrate with histopathology, synonymous terms for image features of AK, KC, and MM were merged by two authors, organized by skin layer and lesion-type. A subset of key LC-OCT image-markers with histopathological correlates that in combination were typical of AK, squamous cell carcinoma in situ (SCCis), invasive squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and MM in traditional histopathology, were selected from the glossary by an experienced dermatopathologist. Seventeen observational studies of AK (7 studies), KC (13 studies), MM (7 studies) utilizing LC-OCT were included, with 117 terms describing either AK, KC, or MM. These were merged to produce 45 merged-terms (61.5% reduction); 5 assigned to the stratum corneum (SC), 23 to the viable epidermis, 2 to dermo-epidermal junction (DEJ) and 15 to the dermis. For each lesion, mandatory key image-markers were a well-defined DEJ and presence of mild/moderate but not severe epidermal dysplasia for AK, severe epidermal dysplasia and well-defined DEJ for SCCis, interrupted DEJ and/or dermal broad infiltrative strands for invasive SCC, dermal lobules connected and/or unconnected to the epidermis for BCC, as well as single atypical melanocytes and/or nest of atypical melanocytes in the epidermis or dermis for MM. This review compiles evidence on LC-OCT in dermato-oncology, providing a harmonized histopathology-integrated terminology and key image-markers for each lesion. Further evaluation is required to determine the clinical value of these findings.

Dermoscopy Use in Africa: Determinants and Challenges

INTRODUCTION

Dermoscopy has evolved over the years beyond distinguishing benign pigmented lesions from melanoma to diagnosing virtually all diseases in dermatology. Overwhelming evidence demonstrates its utility in improving diagnostic accuracy, reducing unnecessary biopsies and lesion monitoring. Dermoscopy is widely used in Western nations, hence most descriptions of lesions in literature are predominantly on Fitzpatrick skin types I-III. Current evidence shows that there are unique dermoscopic features in the dark skin as a result of pigment and pathological reactions. Nationwide surveys and reports have been conducted across several continents to highlight prevalence and factors influencing dermoscopy use with the hope of maximizing its apparent benefits. There are currently no such reports from Africa.

OBJECTIVES

To evaluate dermoscopy use and its determinants among dermatologists in Africa.

METHODS

A cross-sectional study. Online forms were e-mailed to individual practicing dermatologists and members of the African Society of Dermatologists and Venereologists.

RESULTS

There were 196 respondents from 24 African countries. Half of them used dermoscopy. Training, practice settings and location, provision of dermatoscopes by institutions and knowledge of criteria were notable significant determinants. Multiple training exposures, knowledge of criteria, availability of dermatoscopes, use of both hand-held and videodermatoscopes, average number of patients seen per day, and a positive outlook towards dermoscopy were significant determinants of frequency of use. Leading impediments were lack of training and inadequate dermatoscopes in practice.

CONCLUSIONS

Dermoscopy use in Africa is relatively low. Incorporating dermoscopy training into the curriculum with provision of dermatoscopes by training institutions will promote wider usage.

Enhancing skin lesion classification with advanced deep learning ensemble models: a path towards accurate medical diagnostics

Skin cancer, including the highly lethal malignant melanoma, poses a significant global health challenge with a rising incidence rate. Early detection plays a pivotal role in improving survival rates. This study aims to develop an advanced deep learning-based approach for accurate skin lesion classification, addressing challenges such as limited data availability, class imbalance, and noise. Modern deep neural network designs, such as ResNeXt101, SeResNeXt101, ResNet152V2, DenseNet201, GoogLeNet, and Xception, which are used in the study and ze optimised using the SGD technique. The dataset comprises diverse skin lesion images from the HAM10000 and ISIC datasets. Noise and artifacts are tackled using image inpainting, and data augmentation techniques enhance training sample diversity. The ensemble technique is utilized, creating both average and weighted average ensemble models. Grid search optimizes model weight distribution. The individual models exhibit varying performance, with metrics including recall, precision, F1 score, and MCC. The "Average ensemble model" achieves harmonious balance, emphasizing precision, F1 score, and recall, yielding high performance. The "Weighted ensemble model" capitalizes on individual models' strengths, showcasing heightened precision and MCC, yielding outstanding performance. The ensemble models consistently outperform individual models, with the average ensemble model attaining a macro-average ROC-AUC score of 96 % and the weighted ensemble model achieving a macro-average ROC-AUC score of 97 %. This research demonstrates the efficacy of ensemble techniques in significantly improving skin lesion classification accuracy. By harnessing the strengths of individual models and addressing their limitations, the ensemble models exhibit robust and reliable performance across various metrics. The findings underscore the potential of ensemble techniques in enhancing medical diagnostics and contributing to improved patient outcomes in skin lesion diagnosis.

Dermoscopic Features of Pigmented Bowen Disease: A Multicenter Study on Behalf of the Ibero-Latin American College of Dermatology (CILAD)

INTRODUCTION

Studies focused on dermoscopic aspects of pigmented Bowen disease (pBD) in Latin American population are scarce and limited to only case reports or small series.

OBJECTIVES

To report dermoscopic findings in a large series of 147 pBD diagnosed in Ibero-Latin American population.

METHODS

We conducted a multicentric, retrospective study on 147 histologically proven pBD under the auspices of the Dermoscopy Chapter of the Ibero-Latin American College of Dermatology.

RESULTS

The study population consisted of 77 females (52%) and 70 males (48%) with a mean age of 68.6 years. 70.1% of patients had skin phototype 3, 15.6% to skin phototype 2, and 14.3% to skin phototype 4. On clinical examination, near 60% of pBD were flat, 70% presented with scales, and 90% were asymmetric. Under dermoscopy, structureless hypopigmented areas, dots brown and pink color were the most frequently observed. Regarding specific dermoscopic clues to pBD, the most prevalent were structureless hypopigmented areas, vessels arranged in linear fashion at the periphery, and pigmented lines or pigmented dots distributed in a linear fashion. Clustered, coiled, and dotted vessels were observed in 55.8%, 45.6%, and 45.6% of the cases, respectively.

CONCLUSIONS

We report a large series of cases of pBD in Latin American patients, with most patients being skin phototype 3 and 4. Distinctively in our study, the pigmented structures and the clues derived from the presence of melanin were much more frequent than in previous reports in fair skin.

Multi-sample ζ-mixup: richer, more realistic synthetic samples from a p-series interpolant

Modern deep learning training procedures rely on model regularization techniques such as data augmentation methods, which generate training samples that increase the diversity of data and richness of label information. A popular recent method, mixup, uses convex combinations of pairs of original samples to generate new samples. However, as we show in our experiments, mixup  can produce undesirable synthetic samples, where the data is sampled off the manifold and can contain incorrect labels. We propose ζ -mixup, a generalization of mixup  with provably and demonstrably desirable properties that allows convex combinations of T ≥ 2 samples, leading to more realistic and diverse outputs that incorporate information from T original samples by using a p-series interpolant. We show that, compared to mixup, ζ -mixup  better preserves the intrinsic dimensionality of the original datasets, which is a desirable property for training generalizable models. Furthermore, we show that our implementation of ζ -mixup  is faster than mixup, and extensive evaluation on controlled synthetic and 26 diverse real-world natural and medical image classification datasets shows that ζ -mixup  outperforms mixup, CutMix, and traditional data augmentation techniques. The code will be released at https://github.com/kakumarabhishek/zeta-mixup.

Terahertz in vivo imaging of human skin: Toward detection of abnormal skin pathologies

Terahertz (THz) imaging has long held promise for skin cancer detection but has been hampered by the lack of practical technological implementation. In this article, we introduce a technique for discriminating several skin pathologies using a coherent THz confocal system based on a THz quantum cascade laser. High resolution in vivo THz images (with diffraction limited to the order of 100 μm) of several different lesion types were acquired and compared against one another using the amplitude and phase values. Our system successfully separated pathologies using a combination of phase and amplitude information and their respective surface textures. The large scan field (50 × 40 mm) of the system allows macroscopic visualization of several skin lesions in a single frame. Utilizing THz imaging for dermatological assessment of skin lesions offers substantial additional diagnostic value for clinicians. THz images contain information complementary to the information contained in the conventional digital images.

Expert Agreement on the Presence and Spatial Localization of Melanocytic Features in Dermoscopy

Dermoscopy aids in melanoma detection; however, agreement on dermoscopic features, including those of high clinical relevance, remains poor. In this study, we attempted to evaluate agreement among experts on exemplar images not only for the presence of melanocytic-specific features but also for spatial localization. This was a cross-sectional, multicenter, observational study. Dermoscopy images exhibiting at least 1 of 31 melanocytic-specific features were submitted by 25 world experts as exemplars. Using a web-based platform that allows for image markup of specific contrast-defined regions (superpixels), 20 expert readers annotated 248 dermoscopic images in collections of 62 images. Each collection was reviewed by five independent readers. A total of 4,507 feature observations were performed. Good-to-excellent agreement was found for 14 of 31 features (45.2%), with eight achieving excellent agreement (Gwet's AC >0.75) and seven of them being melanoma-specific features. These features were peppering/granularity (0.91), shiny white streaks (0.89), typical pigment network (0.83), blotch irregular (0.82), negative network (0.81), irregular globules (0.78), dotted vessels (0.77), and blue-whitish veil (0.76). By utilizing an exemplar dataset, a good-to-excellent agreement was found for 14 features that have previously been shown useful in discriminating nevi from melanoma. All images are public (www.isic-archive.com) and can be used for education, scientific communication, and machine learning experiments.

Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check

The field of skin cancer detection offers a compelling use case for the application of artificial intelligence (AI) within the realm of image-based diagnostic medicine. Through the analysis of large datasets, AI algorithms have the capacity to classify clinical or dermoscopic images with remarkable accuracy. Although these AI-based applications can operate both autonomously and under human supervision, the best results are achieved through a collaborative approach that leverages the expertise of both AI and human experts. However, it is important to note that most studies focus on assessing the diagnostic accuracy of AI in artificial settings rather than in real-world scenarios. Consequently, the practical utility of AI-assisted diagnosis in a clinical environment is still largely unknown. Furthermore, there exists a knowledge gap concerning the optimal use cases and deployment settings for these AI systems as well as the practical challenges that may arise from widespread implementation. This review explores the advantages and limitations of AI in a variety of real-world contexts, with a specific focus on its value to consumers, general practitioners, and dermatologists.

Clinical Features Associated with the Demand of In-Person Care by Dermatologists: An Observational Cross-Sectional Study

Background: The factors necessitating the need for referrals for in-person evaluations by a dermatologist are not adequately understood and have not been studied using automated text mining so far. The objective of this study was to compare the prevalence of required in-person dermatologist care in the presence or absence of certain clinical features. Methods: Observational cross-sectional study of 11,661 teledermatology reports made from February 2017 to March 2020. Results: The need for dermoscopy was associated with a 348% increase in the possibility of referral for in-person dermatologist evaluations (prevalence ratio [PR]: 4.48, 95% confidence interval [CI]: 4.17-4.82). Infectious diseases were associated with a 64% lower possibility of referral (PR: 0.36, 95% CI: 0.30-0.43). Discussion: Some lesions and poorly documented cases are challenging to assess remotely. This study presents a different approach to research more detailed data from teledermatology reports, using text mining, and points out the risk magnitude for demanding dermatologic in-person care of which feature analyzed. As limitations, the variables related to lesion location, size, and extension were not analyzed and the dictionaries used were originally in Brazilian Portuguese. Conclusions: Teledermatology seems sufficient for the management of 75% of clinical cases, especially acute in young patients with inflammatory or infectious lesions. Referrals for in-person dermatologist consultations were not only strongly associated with the need for dermoscopy, but also for therapeutic reasons like surgical procedures, phototherapy, and the use of some systemic medications.

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

Abstract is missing (Short communication)

BioMEL: a translational research biobank of melanocytic lesions and melanoma

INTRODUCTION

Diagnosing invasive cutaneous melanoma (CM) can be challenging due to subjectivity in distinguishing equivocal nevi, melanoma in situ and thin CMs. The underlying molecular mechanisms of progression from nevus to melanoma must be better understood. Identifying biomarkers for treatment response, diagnostics and prognostics is crucial. Using biomedical data from biobanks and population-based healthcare data, translational research can improve patient care by implementing evidence-based findings. The BioMEL biobank is a prospective, multicentre, large-scale biomedical database on equivocal nevi and all stages of primary melanoma to metastases. Its purpose is to serve as a translational resource, enabling researchers to uncover objective molecular, genotypic, phenotypic and structural differences in nevi and all stages of melanoma. The main objective is to leverage BioMEL to significantly improve diagnostics, prognostics and therapy outcomes of patients with melanoma.

METHODS AND ANALYSIS

The BioMEL biobank contains biological samples, epidemiological information and medical data from adult patients who receive routine care for melanoma. BioMEL is focused on primary and metastatic melanoma, but equivocal pigmented lesions such as clinically atypical nevi and melanoma in situ are also included. BioMEL data are gathered by questionnaires, blood sampling, tumour imaging, tissue sampling, medical records and histopathological reports.

ETHICS AND DISSEMINATION

The BioMEL biobank project is approved by the national Swedish Ethical Review Authority (Dnr. 2013/101, 2013/339, 2020/00469, 2021/01432 and 2022/02421-02). The datasets generated are not publicly available due to regulations related to the ethical review authority.

TRIAL REGISTRATION NUMBER

NCT05446155.

The effects of the COVID-19 pandemic on the diagnosis and prognosis of melanoma 2 years after the pandemic in two Romanian counties

Background

The COVID-19 pandemic disrupted the healthcare system and negatively affected the diagnosis and management of melanoma worldwide. The purpose of this study is to investigate the long-term effects of the COVID-19 pandemic on the diagnosis and prognosis of melanoma.

Materials and methods

This retrospective cohort study included histopathologically confirmed melanoma cases from March 2019 to February 2023 in Cluj and Bihor counties. Data from the post-COVID-19 period (March 2021 to February 2023) were compared to the pre-COVID-19 period (March 2019 to February 2020) and the COVID-19 period (March 2020 to February 2021). Patient characteristics, monthly diagnostics, histological subtypes, and key histological features were analyzed using statistical tests.

Results

The number of melanoma cases diagnosed annually decreased by 31.37 and 23.75% in the first and second post-pandemic years, respectively, compared to pre-pandemic numbers. Diagnostic rates also decreased by 14.9 and 5.4% in the first and second post-pandemic years, respectively, compared to the pandemic period. Prognostic factors worsened in the post-pandemic period, with higher Breslow index and mitotic rate, and increased ulceration and thick melanomas compared to the pre-pandemic period.

Conclusion

The COVID-19 pandemic had a long-lasting impact on the diagnosis of melanoma in Romania, resulting in advanced stages and unfavorable prognostic factors. Larger global studies are needed to comprehensively understand the pandemic's long-term effects on the diagnosis of melanoma.

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.

The effects of procedural and conceptual knowledge on visual learning

Even though past research suggests that visual learning may benefit from conceptual knowledge, current interventions for medical image evaluation often focus on procedural knowledge, mainly by teaching classification algorithms. We compared the efficacy of pure procedural knowledge (three-point checklist for evaluating skin lesions) versus combined procedural plus conceptual knowledge (histological explanations for each of the three points). All students then trained their classification skills with a visual learning resource that included images of two types of pigmented skin lesions: benign nevi and malignant melanomas. Both treatments produced significant and long-lasting effects on diagnostic accuracy in transfer tasks. However, only students in the combined procedural plus conceptual knowledge condition significantly improved their diagnostic performance in classifying lesions they had seen before in the pre- and post-tests. Findings suggest that the provision of additional conceptual knowledge supported error correction mechanisms.

4D spectral-spatial computational photoacoustic dermoscopy

Photoacoustic dermoscopy (PAD) is an emerging non-invasive imaging technology aids in the diagnosis of dermatological conditions by obtaining optical absorption information of skin tissues. Despite advances in PAD, it remains unclear how to obtain quantitative accuracy of the reconstructed PAD images according to the optical and acoustic properties of multilayered skin, the wavelength and distribution of excitation light, and the detection performance of ultrasound transducers. In this work, a computing method of four-dimensional (4D) spectral-spatial imaging for PAD is developed to enable quantitative analysis and optimization of structural and functional imaging of skin. This method takes the optical and acoustic properties of heterogeneous skin tissues into account, which can be used to correct the optical field of excitation light, detectable ultrasonic field, and provide accurate single-spectrum analysis or multi-spectral imaging solutions of PAD for multilayered skin tissues. A series of experiments were performed, and simulation datasets obtained from the computational model were used to train neural networks to further improve the imaging quality of the PAD system. All the results demonstrated the method could contribute to the development and optimization of clinical PADs by datasets with multiple variable parameters, and provide clinical predictability of photoacoustic (PA) data for human skin.

The Role of Dermoscopy in Provider-to-Provider Store-and-Forward Dermatology eConsults: A Scoping Review of the Recent Literature

Purpose of Review

This scoping review maps recent literature on dermatology provider-to-provider asynchronous store-and-forward (SAF) electronic consult (eConsult) platforms with dermoscopy. It offers a descriptive overview, highlighting benefits and challenges.

Recent Findings

Incorporating dermoscopy into SAF eConsults improves diagnostic accuracy for benign and malignant skin neoplasms. Diagnostic and treatment concordance with traditional face-to-face (FTF) visits is high. SAF eConsults with dermoscopy enhance access to dermatological care by improving triage and reducing wait times for FTF visits. Pediatric patients benefit with improved evaluation of melanocytic and vascular growths. eConsult platforms with dermoscopy serve as a telementoring opportunity for clinicians interested in improving their dermoscopy skills.

Summary

Adding dermoscopy to SAF eConsults is valuable and results in improved diagnostic accuracy and reduced need for FTF visits. Implementation barriers can be overcome through collaboration between primary care and dermatology. Dermoscopy in SAF eConsults has significant potential for managing skin conditions and reducing the burden caused by unnecessary FTF visit and biopsies.

Line-Field Confocal Optical Coherence Tomography (LC-OCT) for Skin Imaging in Dermatology

Line-field confocal optical coherence tomography (LC-OCT) is a non-invasive optical imaging technique based on a combination of the principles of optical coherence tomography and reflectance confocal microscopy with line-field illumination, which can generate cell-resolved images of the skin in vivo. This article reports on the LC-OCT technique and its application in dermatology. The principle of the technique is described, and the latest technological innovations are presented. The technology has been miniaturized to fit within an ergonomic handheld probe, allowing for the easy access of any skin area on the body. The performance of the LC-OCT device in terms of resolution, field of view, and acquisition speed is reported. The use of LC-OCT in dermatology for the non-invasive detection, characterization, and therapeutic follow-up of various skin pathologies is discussed. Benign and malignant melanocytic lesions, non-melanocytic skin tumors, such as basal cell carcinoma, squamous cell carcinoma and actinic keratosis, and inflammatory and infectious skin conditions are considered. Dedicated deep learning algorithms have been developed for assisting in the analysis of LC-OCT images of skin lesions.

Increasing melanoma incidence and survival trend shifts with improved melanoma-specific survival between 1990 and 2020 in Sweden

BACKGROUND

Melanoma-specific survival (MSS) is heterogenous between stages and is highly dependent on the T stage for primary localized disease. New systemic therapies for metastatic cutaneous melanoma (CM) have been introduced since 2012 in Sweden.

OBJECTIVES

To analyse the incidence and MSS time trends between 1990 and 2020 in Sweden.

METHODS

Nationwide, population-based and prospectively collected clinico-pathological data on invasive CM from the Swedish Melanoma Registry (SweMR) were analysed for survival trends between 1990 and 2020 using Kaplan-Meier curves and Cox proportional hazard ratios (HRs).

RESULTS

In total, 77 036 primary invasive CMs were diagnosed in 70 511 patients in Sweden between 1990 and 2020. The 5-year MSS [95% confidence interval (CI)] was 88.9% (88.3-89.4) for 1990-2000, 89.2% (88.7-89.6) for 2001-2010 and 93.0% (92.7-93.9) for 2011-2020. The odds ratios for being diagnosed with nodular melanoma (vs. superficial spreading melanoma) was significantly reduced by 20% (2001-2010) and by 46% (2011-2020) vs. the reference period 1990-2000. Overall, the MSS improved over both diagnostic periods (2001-2010 and 2011-2020) vs. the reference period 1990-2000 among men and women, respectively [HRmen: 2001-2010: 0.89 (95% CI 0.82-0.96) and 2011-2020: 0.62 (95% CI 0.56-0.67); HRwomen: 2001-2010: 0.82 (95% CI 0.74-0.91) and 2011-2020: 0.62 (95% CI 0.56-0.70)]. The risk of death from CM was significantly lower in all age groups for both men and women in the most recent diagnostic period (2011-2020 vs.1990-2000).

CONCLUSIONS

The results emphasize the improved MSS among men and women in Sweden. The MSS improvements, specifically for the period 2011-2020, may be correlated to the introduction of new systemic therapies and are here shown for the first time in detail for Sweden.

A country-wide teledermatoscopy service in Estonia shows results comparable to those in experimental settings in management plan development and diagnostic accuracy: A retrospective database study

Background

Teledermatoscopy accuracy has been examined in experimental settings and is recommended for primary care despite lacking real-world implementation evidence. A teledermatoscopy service has been provided in Estonia since 2013, where lesions are evaluated based on the patient's or general practitioner's suggestion.

Objective

The management plan and diagnostic accuracy of a real-world store-and-forward teledermatoscopy service for melanoma diagnosis were evaluated.

Methods

A retrospective study analyzed 4748 cases from 3403 patients using the service between October 16, 2017 and August 30, 2019 by matching country-wide databases. Management plan accuracy was calculated as the percentage of melanoma found that was managed correctly. Diagnostic accuracy parameters were sensitivity, specificity, and positive and negative predictive values.

Results

Management plan accuracy for melanoma detection was 95.5% (95% CI, 77.2-99.9). Diagnostic accuracy showed a sensitivity of 90.48% (95% CI, 69.62-98.83) and a specificity of 92.57% (95% CI, 91.79-93.31).

Limitations

Matching the lesions was limited to SNOMED CT location standard precision. Diagnostic accuracy was calculated based on a combination of diagnosis and management plan data.

Conclusion

Teledermatoscopy for detecting and managing melanoma in real-world clinical practice displays results comparable with those in experimental setting studies.

DEVO: an ontology to assist with dermoscopic feature standardization

BACKGROUND

The utilization of dermoscopic analysis is becoming increasingly critical for diagnosing skin diseases by physicians and even artificial intelligence. With the expansion of dermoscopy, its vocabulary has proliferated, but the rapid evolution of the vocabulary of dermoscopy without standardized control is counterproductive. We aimed to develop a domain-specific ontology to formally represent knowledge for certain dermoscopic features.

METHODS

The first phase involved creating a fundamental-level ontology that covers the fundamental aspects and elements in describing visualizations, such as shapes and colors. The second phase involved creating a domain ontology that harnesses the fundamental-level ontology to formalize the definitions of dermoscopic metaphorical terms.

RESULTS

The Dermoscopy Elements of Visuals Ontology (DEVO) contains 1047 classes, 47 object properties, and 16 data properties. It has a better semiotic score compared to similar ontologies of the same domain. Three human annotators also examined the consistency, complexity, and future application of the ontology.

CONCLUSIONS

The proposed ontology was able to harness the definitions of metaphoric terms by decomposing them into their visual elements. Future applications include providing education for trainees and diagnostic support for dermatologists, with the goal of generating responses to queries about dermoscopic features and integrating these features to diagnose skin diseases.

Implementation of a dermatoscopy curriculum during residency at Augsburg University Hospital in Germany

BACKGROUND AND OBJECTIVES

To date, there is no structured program for dermatoscopy training during residency in Germany. Whether and how much dermatoscopy training is acquired is left to the initiative of each resident, although dermatoscopy is one of the core competencies of dermatological training and daily practice. The aim of the study was to establish a structured dermatoscopy curriculum during residency at the University Hospital Augsburg.

PATIENTS AND METHODS

An online platform with dermatoscopy modules was created, accessible regardless of time and place. Practical skills were acquired under the personal guidance of a dermatoscopy expert. Participants were tested on their level of knowledge before and after completing the modules. Test scores on management decisions and correct dermatoscopic diagnosis were analyzed.

RESULTS

Results of 28 participants showed improvements in management decisions from pre- to posttest (74.0% vs. 89.4%) and in dermatoscopic accuracy (65.0% vs. 85.6%). Pre- vs. posttest differences in test score (7.05/10 vs. 8.94/10 points) and correct diagnosis were significant (p < 0.001).

CONCLUSIONS

The dermatoscopy curriculum increases the number of correct management decisions and dermatoscopy diagnoses. This will result in more skin cancers being detected, and fewer benign lesions being excised. The curriculum can be offered to other dermatology training centers and medical professionals.

Implementierung eines Dermatoskopie-Curriculums in der Facharztausbildung am Universitätsklinikum Augsburg

Hintergrund und Ziele

Bislang gibt es in Deutschland kein strukturiertes Programm für die Dermatoskopieausbildung während der Facharztausbildung. Es bleibt der Initiative des einzelnen Assistenzarztes überlassen, ob und in welchem Umfang er sich in der Dermatoskopie weiterbildet, obwohl die Dermatoskopie zu den Kernkompetenzen der dermatologischen Ausbildung und der täglichen Praxis gehört. Ziel der Studie war die Etablierung eines strukturierten Dermatoskopie‐Curriculums während der dermatologischen Facharztausbildung am Universitätsklinikum Augsburg.

Patienten und Methodik

Es wurde eine Online‐Plattform mit Dermatoskopie‐Modulen geschaffen, auf die von überall und jederzeit zugegriffen werden kann. Praktische Fertigkeiten wurden unter individueller Anleitung eines Dermatoskopie‐Experten erworben. Die Teilnehmer wurden vor und nach Abschluss der Module auf ihren Wissensstand getestet. Die Testergebnisse zum therapeutischen Management und zur korrekten dermatoskopischen Diagnose wurden analysiert.

Ergebnisse

Die Ergebnisse der 28 Teilnehmer verbesserten sich vom Eingangs‐ zum Abschlusstest bei der Managemententscheidung (74,0% vs. 89,4%) und bei der dermatoskopischen Genauigkeit (65,0% vs. 85,6%). Die Unterschiede zwischen Eingangs‐ und Abschlusstest bei der Gesamtpunktzahl (7,05/10 vs. 8,94/10 Punkte) und bei der richtigen Diagnose waren signifikant (p < 0,001).

Schlussfolgerungen

Das Dermatoskopie‐Curriculum verbessert die Managemententscheidungen und die dermatoskopische Diagnostik der Teilnehmer. Das wird dazu führen, dass mehr Hautkrebsfälle erkannt werden und weniger gutartige Läsionen reseziert werden müssen. Das Curriculum kann anderen dermatologischen Ausbildungszentren und Gesundheitsberufen angeboten werden.

Progressive growing of Generative Adversarial Networks for improving data augmentation and skin cancer diagnosis

Early melanoma diagnosis is the most important factor in the treatment of skin cancer and can effectively reduce mortality rates. Recently, Generative Adversarial Networks have been used to augment data, prevent overfitting and improve the diagnostic capacity of models. However, its application remains a challenging task due to the high levels of inter and intra-class variance seen in skin images, limited amounts of data, and model instability. We present a more robust Progressive Growing of Adversarial Networks based on residual learning, which is highly recommended to ease the training of deep networks. The stability of the training process was increased by receiving additional inputs from preceding blocks. The architecture is able to produce plausible photorealistic synthetic 512 × 512 skin images, even with small dermoscopic and non-dermoscopic skin image datasets as problem domains. In this manner, we tackle the lack of data and the imbalance problems. Additionally, the proposed approach leverages a skin lesion boundary segmentation algorithm and transfer learning to enhance the diagnosis of melanoma. Inception score and Matthews Correlation Coefficient were used to measure the performance of the models. The architecture was evaluated qualitatively and quantitatively through the use of an extensive experimental study on sixteen datasets, illustrating its effectiveness in the diagnosis of melanoma. Finally, four state-of-the-art data augmentation techniques applied in five convolutional neural network models were significantly outperformed. The results indicated that a bigger number of trainable parameters will not necessarily obtain a better performance in melanoma diagnosis.

MSF-Net: A Lightweight Multi-Scale Feature Fusion Network for Skin Lesion Segmentation

Segmentation of skin lesion images facilitates the early diagnosis of melanoma. However, this remains a challenging task due to the diversity of target scales, irregular segmentation shapes, low contrast, and blurred boundaries of dermatological graphics. This paper proposes a multi-scale feature fusion network (MSF-Net) based on comprehensive attention convolutional neural network (CA-Net). We introduce the spatial attention mechanism in the convolution block through the residual connection to focus on the key regions. Meanwhile, Multi-scale Dilated Convolution Modules (MDC) and Multi-scale Feature Fusion Modules (MFF) are introduced to extract context information across scales and adaptively adjust the receptive field size of the feature map. We conducted many experiments on the public data set ISIC2018 to verify the validity of MSF-Net. The ablation experiment demonstrated the effectiveness of our three modules. The comparison experiment with the existing advanced network confirms that MSF-Net can achieve better segmentation under fewer parameters.

A survey on deep learning for skin lesion segmentation

Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online³.

Skin Cancer Classification Using Deep Spiking Neural Network

Skin cancer is one of the primary causes of death globally, and experts diagnose it by visual inspection, which can be inaccurate. The need for developing a computer-aided method to aid dermatologists in diagnosing skin cancer is highlighted by the fact that early identification can lower the number of deaths caused by skin malignancies. Among computer-aided techniques, deep learning is the most popular for identifying cancer from skin lesion images. Due to their power-efficient behavior, spiking neural networks are attractive deep neural networks for hardware implementation. We employed deep spiking neural networks using the surrogate gradient descent method to classify 3670 melanoma and 3323 non-melanoma images from the ISIC 2019 dataset. We achieved an accuracy of 89.57% and an F1 score of 90.07% using the proposed spiking VGG-13 model, which is higher than the VGG-13 and AlexNet using less trainable parameters.

Line-field confocal optical coherence tomography (LC-OCT): principles and practical use

Line-field confocal optical coherence tomography (LC-OCT) is a non-invasive optical imaging technique based on a combination of the optical principles of optical coherence tomography and reflectance confocal microscopy with line-field illumination, which can generate cell-resolved images of the skin, in vivo, in vertical section, horizontal section and in three dimensions. This article reviews the optical principles of LC-OCT, including low coherence interferometry, confocal filtering and line-field arrangement. The optical setup allowing for the acquisition of color images of the skin surface in parallel with LC-OCT images, without compromising LC-OCT performance, is also presented. Practical use of LC-OCT is demonstrated through an overview of the workflow of examining a patient using a commercial handheld LC-OCT probe (deepLive™, DAMAE Medical), from creating the patient record in the software, acquiring the images, to reviewing and interpreting the images. LC-OCT can generate a significant amount of data, making automated deep learning algorithms particularly relevant for assisting in the analysis of LC-OCT images. A review of algorithms developed for skin layer segmentation, keratinocyte nuclei segmentation, and automatic detection of atypical keratinocyte nuclei is provided.

Do not PASS any melanoma without diagnosis: a new simplified dermoscopic algorithm

INTRODUCTION

Dermoscopic algorithms for melanoma diagnosis could be time-expending, and their reliability in daily practice lower than expected.

OBJECTIVE

To propose a simplified dermoscopic algorithm for melanoma diagnosis.

MATERIAL AND METHODS

A multicenter retrospective analysis of 1,120 dermoscopic images of atypical melanocytic tumors (320 melanomas and 800 non-melanomas) was performed. An algorithm based on polychromia, asymmetry in colors or structures, and some melanoma-specific structures was designed. Univariate and multivariate logistic regression analysis was calculated to estimate the coefficients of each potential predictor for melanoma diagnosis. A score was developed based on the dermoscopic evaluations performed by four experts blinded to histological diagnosis.

RESULTS

Most melanomas had ≥3 colors (280; 84.5%), asymmetry in colors or structures (289; 90.3%), and at least one melanoma-specific structure (316; 98.7%). PASS score ≥3 had a 91.9% sensibility, 87% specificity, and 88.4% diagnostic accuracy for melanoma. PASS algorithm showed an area under the curve (AUC) of 0.947 (95% CI 0.935-0.959).

LIMITATIONS

This study was retrospective. A comparison between the performances of different dermoscopic algorithms is difficult because of their designs.

CONCLUSION

PASS algorithm showed a very good diagnostic accuracy, independently of the observers' experience, and it seems easier to perform than previous dermoscopic algorithms.

Sensitivity investigation of open-ended coaxial probe in skin cancer detection

Open-ended coaxial probe method is one of the most common modalities in measuring dielectric properties (DPs) of biological tissues. Due to the significant differences between the tumors and normal tissues in DPs, the technique can be used to detect skin cancer in the early stage. Although various studies have been reported, systematic assessment is in urgent need to advance it to clinical applications, for its parameters interactions and detecting limitations remained unclear. In this study, we aim to provide a comprehensive examination of this method, including the minimum detectable tumor size by using a three-layer skin model via simulation and demonstrated that open-ended coaxial probe method can be used for detection of early-stage skin cancer. The smallest detecting size are subject to different subtypes: for BCC, inside the skin is 0.5 mm radius × 0.1 mm height; for SCC, inside the skin is 1.4 mm × 1.3 mm in radius and height; the smallest distinguishing size of BCC is 0.6 mm × 0.7 mm in radius and height; for SCC is 1.0 mm × 1.0 mm in radius and height; for MM is 0.7 mm × 0.4 mm in radius and height. The experiment results showed that sensitivity was affected by tumor dimension, probe size, skin height, and cancer subtype. The probe is more sensitive to cylinder tumor radius than height growing on the surface of the skin while the smallest size probe is the most sensitive among the working probes. We provide a detailed systematic evaluation of the parameters employed in the method for further applications.

CI-Net: Clinical-Inspired Network for Automated Skin Lesion Recognition

The lesion recognition of dermoscopy images is significant for automated skin cancer diagnosis. Most of the existing methods ignore the medical perspective, which is crucial since this task requires a large amount of medical knowledge. A few methods are designed according to medical knowledge, but they ignore to be fully in line with doctors' entire learning and diagnosis process, since certain strategies and steps of those are conducted in practice for doctors. Thus, we put forward Clinical-Inspired Network (CI-Net) to involve the learning strategy and diagnosis process of doctors, as for a better analysis. The diagnostic process contains three main steps: the zoom step, the observe step and the compare step. To simulate these, we introduce three corresponding modules: a lesion area attention module, a feature extraction module and a lesion feature attention module. To simulate the distinguish strategy, which is commonly used by doctors, we introduce a distinguish module. We evaluate our proposed CI-Net on six challenging datasets, including ISIC 2016, ISIC 2017, ISIC 2018, ISIC 2019, ISIC 2020 and PH2 datasets, and the results indicate that CI-Net outperforms existing work. The code is publicly available at https://github.com/lzh19961031/Dermoscopy_classification.

ACCPG-Net: A skin lesion segmentation network with Adaptive Channel-Context-Aware Pyramid Attention and Global Feature Fusion

The computer-aided diagnosis system based on dermoscopic images has played an important role in the clinical treatment of skin lesion. An accurate, efficient, and automatic skin lesion segmentation method is an important auxiliary tool for clinical diagnosis. At present, skin lesion segmentation still suffers from great challenges. Existing deep-learning-based automatic segmentation methods frequently use convolutional neural networks (CNN). However, the globally-sharing feature re-weighting vector may not be optimal for the prediction of lesion areas in dermoscopic images. The presence of hairs and spots in some samples aggravates the interference of similar categories, and reduces the segmentation accuracy. To solve this problem, this paper proposes a new deep network for precise skin lesion segmentation based on a U-shape structure. To be specific, two lightweight attention modules: adaptive channel-context-aware pyramid attention (ACCAPA) module and global feature fusion (GFF) module, are embedded in the network. The ACCAPA module can model the characteristics of the lesion areas by dynamically learning the channel information, contextual information and global structure information. GFF is used for different levels of semantic information interaction between encoder and decoder layers. To validate the effectiveness of the proposed method, we test the performance of ACCPG-Net on several public skin lesion datasets. The results show that our method achieves better segmentation performance compared to other state-of-the-art methods.

Long-Term Sequential Digital Dermoscopy of Low-Risk Patients May Not Improve Early Diagnosis of Melanoma Compared to Periodical Handheld Dermoscopy

Sequential digital dermoscopy (SDD) enables the diagnosis of a subgroup of slow-growing melanomas that lack suspicious features at baseline examination but exhibit detectable change on follow-up. The combined use of total-body photography and SDD is recommended in high-risk subjects by current guidelines. To establish the usefulness of SDD for low-risk individuals, we conducted a retrospective study using electronic medical records of low-risk patients with a histopathological diagnosis of cutaneous melanoma between 1 January 2016 and 31 December 2019, who had been referred and monitored for long-term follow-up of clinically suspicious melanocytic nevi. We sought to compare the distribution of "early" cutaneous melanoma, defined as melanoma in situ and pT1a melanoma, between SDD and periodical handheld dermoscopy in low-risk patients. A total of 621 melanomas were diagnosed in a four-year timespan; 471 melanomas were diagnosed by handheld dermoscopy and 150 by digital dermoscopy. Breslow tumor thickness was significantly higher for melanomas diagnosed by handheld compared to digital dermoscopy (0.56 ± 1.53 vs. 0.26 ± 0.84, p = 0.030, with a significantly different distribution of pT stages between the two dermoscopic techniques. However, no significant difference was found with respect to the distribution of pT stages, mean Breslow tumor thickness, ulceration, and prevalence of associated melanocytic nevus in tumors diagnosed on periodical handheld dermoscopy compared to SDD. Our results confirm that periodical dermoscopic examination enables the diagnosis of cutaneous melanoma at an earlier stage compared to first-time examination as this was associated in our patients with better prognostic features. However, in our long-term monitoring of low-risk subjects, Breslow tumor thickness and pT stage distribution did not differ between handheld periodical dermoscopy and SDD.

Convolutional neural network-based skin image segmentation model to improve classification of skin diseases in conventional and non-standardized picture images

BACKGROUND

For dermatological practices, non-standardized conventional photo images are taken and collected as a mixture of variable fields of the image view, including close-up images focusing on designated lesions and long-shot images including normal skin and background of the body surface. Computer-aided detection/diagnosis (CAD) models trained using non-standardized conventional photo images exhibit lower performance rates than CAD models that detect lesions in a localized small area, such as dermoscopic images.

OBJECTIVE

We aimed to develop a convolutional neural network (CNN) model for skin image segmentation to generate a skin disease image dataset suitable for CAD of multiple skin disease classification.

METHODS

We trained a DeepLabv3 + -based CNN segmentation model to detect skin and lesion areas and segmented out areas that satisfy the following conditions: more than 80% of the image will be the skin area, and more than 10% of the image will be the lesion area.

RESULTS

The generated CNN-segmented image database was examined using CAD of skin disease classification and achieved approximately 90% sensitivity and specificity to differentiate atopic dermatitis from malignant diseases and complications, such as mycosis fungoides, impetigo, and herpesvirus infection. The accuracy of skin disease classification in the CNN-segmented image dataset was almost equal to that of the manually cropped image dataset and higher than that of the original image dataset.

CONCLUSION

Our CNN segmentation model, which automatically extracts lesions and segmented images of the skin area regardless of image fields, will reduce the burden of physician annotation and improve CAD performance.

SL-HarDNet: Skin lesion segmentation with HarDNet

Automatic segmentation of skin lesions from dermoscopy is of great significance for the early diagnosis of skin cancer. However, due to the complexity and fuzzy boundary of skin lesions, automatic segmentation of skin lesions is a challenging task. In this paper, we present a novel skin lesion segmentation network based on HarDNet (SL-HarDNet). We adopt HarDNet as the backbone, which can learn more robust feature representation. Furthermore, we introduce three powerful modules, including: cascaded fusion module (CFM), spatial channel attention module (SCAM) and feature aggregation module (FAM). Among them, CFM combines the features of different levels and effectively aggregates the semantic and location information of skin lesions. SCAM realizes the capture of key spatial information. The cross-level features are effectively fused through FAM, and the obtained high-level semantic position information features are reintegrated with the features from CFM to improve the segmentation performance of the model. We apply the challenge dataset ISIC-2016&PH2 and ISIC-2018, and extensively evaluate and compare the state-of-the-art skin lesion segmentation methods. Experiments show that our SL-HarDNet performance is always superior to other segmentation methods and achieves the latest performance.

Diagnostic accuracy of dermoscopy for 934 basal cell carcinomas: A single-center retrospective study

Although the efficacy of dermoscopic diagnosis of basal cell carcinoma (BCC) has already been established, most studies have been conducted in Western countries. However, there are racial differences in the clinicopathological characteristics of BCC, highlighting the need for a survey among Asians. Herein, we aimed to investigate the diagnostic accuracy of dermoscopy in 934 Japanese patients with BCC and statistically analyze the clinicopathological factors affecting diagnostic accuracy. We analyzed 5093 skin lesions, including 934 BCCs that were diagnosed consecutively from 1998 to 2018. The sensitivity and specificity of dermoscopic diagnosis for BCC were calculated. The sensitivity and specificity of dermoscopic diagnosis were 92.2% and 96.0%, respectively. There were 73 false-negative cases of BCCs that were clinically diagnosed with other diseases. The most common incorrect clinical diagnosis was seborrheic keratosis (n = 18), followed by melanocytic nevus (n = 15). Multiple logistic regression analysis showed that sensitivity was significantly lower in BCCs located on the trunk and extremities, which showed low pigmentation (less than 10% of the lesion surface) and were diagnosed by a resident dermatologist. Experience of 3-6 months of 12 resident dermatologists revealed increased sensitivity. Dermoscopy is a reliable tool for the accurate diagnosis of BCC in Japanese individuals. Care should be taken when diagnosing BCCs of the trunk and extremities, and the less-pigmented subtype because of lower sensitivity. A certain amount of experience is required to improve the skills for dermoscopy.

HI-MViT: A lightweight model for explainable skin disease classification based on modified MobileViT

Objective

To develop an explainable lightweight skin disease high-precision classification model that can be deployed to the mobile terminal.

Methods

In this study, we present HI-MViT, a lightweight network for explainable skin disease classification based on Modified MobileViT. HI-MViT is mainly composed of ordinary convolution, Improved-MV2, MobileViT block, global pooling, and fully connected layers. Improved-MV2 uses the combination of shortcut and depth classifiable convolution to substantially decrease the amount of computation while ensuring the efficient implementation of information interaction and memory. The MobileViT block can efficiently encode local and global information. In addition, semantic feature dimensionality reduction visualization and class activation mapping visualization methods are used for HI-MViT to further understand the attention area of the model when learning skin lesion images.

Results

The International Skin Imaging Collaboration has assembled and made available the ISIC series dataset. Experiments using the HI-MViT model on the ISIC-2018 dataset achieved scores of 0.931, 0.932, 0.961, and 0.977 on F1-Score, Accuracy, Average Precision (AP), and area under the curve (AUC). Compared with the top five algorithms of ISIC-2018 Task 3, Marco's average F1-Score, AP, and AUC have increased by 6.9%, 6.8%, and 0.8% compared with the suboptimal performance model. Compared with ConvNeXt, the most competitive convolutional neural network architecture, our model is 5.0%, 3.4%, 2.3%, and 2.2% higher in F1-Score, Accuracy, AP, and AUC, respectively. The experiments on the ISIC-2017 dataset also achieved excellent results, and all indicators were better than the top five algorithms of ISIC-2017 Task 3. Using the trained model to test on the PH2 dataset, an excellent performance score is obtained, which shows that it has good generalization performance.

Conclusions

The skin disease classification model HI-MViT proposed in this article shows excellent classification performance and generalization performance in experiments. It demonstrates how the classification outcomes can be applied to dermatologists' computer-assisted diagnostics, enabling medical professionals to classify various dermoscopic images more rapidly and reliably.