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Acta Dermato-venereologica Dec 2021Research interest in dermoscopy is increasing, but the complete dermoscopic image sets used in inter-observer studies of skin tumours are not often shared in research...
Research interest in dermoscopy is increasing, but the complete dermoscopic image sets used in inter-observer studies of skin tumours are not often shared in research publications. The aim of this systematic review was to analyse what proportion of images depicting skin tumours are published in studies investigating inter-observer variations in the assessment of dermoscopic features and/or patterns. Embase, MEDLINE and Scopus databases were screened for eligible studies published from inception to 2 July 2020. For included studies the proportion of lesion images presented in the papers and/or supplements was extracted. A total of 61 studies (53 original studies and 8 shorter reports (i.e. research letters or concise reports)). published in the period 1997 to 2020 were included. These studies combined included 14,124 skin tumours, of which 373 (3%) images were published. This systematic review highlights that the vast majority of images included in dermoscopy research are not published. Data sharing should be a requirement for future studies, and must be enabled and standardized by the dermatology research community and editorial offices.
PubMed: 34853864
DOI: 10.2340/actadv.v101.865 -
The Journal of Dermatology Jan 2022Trichoscopy represents a non-invasive diagnostic modality widely used in daily practice. Despite the common perception that this technique has been fairly established,... (Review)
Review
Trichoscopy represents a non-invasive diagnostic modality widely used in daily practice. Despite the common perception that this technique has been fairly established, some key issues remain to be addressed. Complexity and inconsistency in terminology in past literature are likely to confuse investigators when they are recording, reporting, and retrieving the findings. In addition, a diagnostic algorithm adopting sufficiently integrated and updated findings is not readily available. By adopting a systematic review approach, this review attempted to redefine major trichoscopic findings and integrate their synonyms individually into the most frequently used terms besides identifying and discussing terms which potentially cause confusion. The findings are categorized into five subgroups: hair shaft, follicular, perifollicular, scalp findings, and hair distribution pattern abnormalities. The calculation of sensitivities and positive predictive values of such redefined findings was conducted by reviewing the descriptions in the past literature on major hair diseases, including alopecia areata, androgenetic alopecia/female pattern hair loss, telogen effluvium, trichotillomania, lichen planopilaris, frontal fibrosing alopecia, central centrifugal cicatricial alopecia, discoid lupus erythematosus, folliculitis decalvans, tinea capitis, and dissecting cellulitis, to confirm the diagnostically meaningful findings for representative diseases. This attempt redefined, for instance, yellow dots, short vellus hairs, exclamation mark hairs, black dots, and broken hairs as the findings of diagnostic significance for alopecia areata and hair diameter diversity, peripilar sign, and focal atrichia for androgenetic alopecia/female pattern hair loss. An updated diagnostic flowchart is proposed with the instructions to maximize its usefulness. Current limitations and future perspectives of trichoscopy as well as other emerging non-invasive diagnostic modalities for hair diseases are also discussed.
Topics: Alopecia; Alopecia Areata; Dermoscopy; Female; Hair; Hair Diseases; Humans; Software Design
PubMed: 34806223
DOI: 10.1111/1346-8138.16233 -
The Lancet. Digital Health Jan 2022Publicly available skin image datasets are increasingly used to develop machine learning algorithms for skin cancer diagnosis. However, the total number of datasets and...
Publicly available skin image datasets are increasingly used to develop machine learning algorithms for skin cancer diagnosis. However, the total number of datasets and their respective content is currently unclear. This systematic review aimed to identify and evaluate all publicly available skin image datasets used for skin cancer diagnosis by exploring their characteristics, data access requirements, and associated image metadata. A combined MEDLINE, Google, and Google Dataset search identified 21 open access datasets containing 106 950 skin lesion images, 17 open access atlases, eight regulated access datasets, and three regulated access atlases. Images and accompanying data from open access datasets were evaluated by two independent reviewers. Among the 14 datasets that reported country of origin, most (11 [79%]) originated from Europe, North America, and Oceania exclusively. Most datasets (19 [91%]) contained dermoscopic images or macroscopic photographs only. Clinical information was available regarding age for 81 662 images (76·4%), sex for 82 848 (77·5%), and body site for 79 561 (74·4%). Subject ethnicity data were available for 1415 images (1·3%), and Fitzpatrick skin type data for 2236 (2·1%). There was limited and variable reporting of characteristics and metadata among datasets, with substantial under-representation of darker skin types. This is the first systematic review to characterise publicly available skin image datasets, highlighting limited applicability to real-life clinical settings and restricted population representation, precluding generalisability. Quality standards for characteristics and metadata reporting for skin image datasets are needed.
Topics: Datasets as Topic; Dermoscopy; Humans; Machine Learning; Skin Neoplasms
PubMed: 34772649
DOI: 10.1016/S2589-7500(21)00252-1 -
Journal of Oral Biology and... 2021The practice of dermoscopy in dental and oral examination is low due to less popularity and not well established of the diagnostic tool in dental practice. The... (Review)
Review
INTRODUCTION
The practice of dermoscopy in dental and oral examination is low due to less popularity and not well established of the diagnostic tool in dental practice. The dermoscopy examination provides a specific dermoscopes details for pigmented papillary fungiform of tongue (PPFT) as cobblestone appearance and rose-petal appearance. With this dermoscopes details serves as a non-invasive diagnostic tool and prevents biopsy procedure.
OBJECTIVE
We performed a systematic review to evaluate the published papers related to pigmented papillary fungiform on the tongue, aiming to understand the diagnostic role of dermoscopy examination in pigmented papillary fungiform.
DATA SYNTHESIS
Initial result was 136 studies. Final exclusion of 27 articles was made based on the following factors: reports with no clinical images, studies that did not confirm the diagnosis of PPFT and studies that did not use the dermoscopes details. Finally, seventeen studies with nineteen cases, reported of pigmented papillary fungiform of the tongue. Six studies (consist six cases) reported the dermoscopy and histopathology diagnosis of pigmented papillary fungiform, eleven studies (consist thirteen cases) reported only the dermoscopy. The dermoscopy examination presented cobblestone appearance is 47.37% and rose petal appearance is 52.63%. The comparation study by histopathology diagnosis was done, revealed no specific appearances.
CONCLUSIONS
The clinical appearance and dermoscopy is the key for diagnosis of the papillary fungiform on the tongue. Further research is needed for determining the etiology and predisposing factor in papillary fungiform so that the possibility of developing this condition can be predicted and proper treatment could be performed.
PubMed: 34729344
DOI: 10.1016/j.jobcr.2021.09.008 -
Pediatric Dermatology Sep 2021Vulvar vitiligo (VV) and vulvar lichen sclerosus (VLS), both feature skin and mucosal hypo-/depigmentation. The aim of this study was to describe the clinical and... (Review)
Review
Vulvar vitiligo (VV) and vulvar lichen sclerosus (VLS), both feature skin and mucosal hypo-/depigmentation. The aim of this study was to describe the clinical and dermoscopic features of VV and VLS in the pediatric population, providing diagnostic clues, and to define their association. We performed a systematic literature review of the clinical and dermoscopic features of pediatric VV and VLS. An observational study was conducted on children affected by VLS associated with VV, referred to the Dermatology Unit of the Sant'Orsola Polyclinic in Bologna, Italy. Medical history, age at diagnosis, ethnicity, clinical and dermoscopic features, and symptoms were recorded for all patients. 124 cases of VLS and 10 cases of VV were reviewed. Clinical manifestations included hypo-/depigmented patches in both conditions, while ecchymosis/purpura and fissures/erosion were observed in VLS. Symptoms including pruritus, pain, or burning were reported only by VLS patients. In our study five patients with VLS associated with VV were retrieved. Clinical features included well-demarcated depigmented patches in VV and translucent areas, erythema, ecchymoses/purpura, and labial fusion in VLS. Dermoscopy showed white structureless areas with a whipped cream-like appearance, linear or dotted vessels, white chrysalis-like structures, erosion and red-purpuric blotches in VLS and reduced pigment network or pigment absence, intralesional spots of residual pigmentation and telangiectasias in VV. Symptoms were present in all patients. Both VV and VLS show hypo-/depigmented patches. In the presence of associated symptoms, possible VLS should be investigated with clinical and dermoscopic examination to achieve a prompt diagnosis.
Topics: Child; Female; Humans; Lichen Sclerosus et Atrophicus; Observational Studies as Topic; Skin; Vitiligo; Vulvar Lichen Sclerosus
PubMed: 34561885
DOI: 10.1111/pde.14771 -
European Journal of Cancer (Oxford,... Oct 2021Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the...
BACKGROUND
Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice.
OBJECTIVE
The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians.
METHODS
PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma. The search terms skin cancer classification, deep learning, convolutional neural network (CNN), melanoma (detection), digital biomarkers, histopathology and whole slide imaging were combined. Based on the search results, only studies that considered direct comparison of AI results with clinicians and had a diagnostic classification as their main objective were included.
RESULTS
A total of 19 reader studies fulfilled the inclusion criteria. Of these, 11 CNN-based approaches addressed the classification of dermoscopic images; 6 concentrated on the classification of clinical images, whereas 2 dermatopathological studies utilised digitised histopathological whole slide images.
CONCLUSIONS
All 19 included studies demonstrated superior or at least equivalent performance of CNN-based classifiers compared with clinicians. However, almost all studies were conducted in highly artificial settings based exclusively on single images of the suspicious lesions. Moreover, test sets mainly consisted of holdout images and did not represent the full range of patient populations and melanoma subtypes encountered in clinical practice.
Topics: Automation; Biopsy; Clinical Competence; Deep Learning; Dermatologists; Dermoscopy; Diagnosis, Computer-Assisted; Humans; Image Interpretation, Computer-Assisted; Melanoma; Microscopy; Neural Networks, Computer; Pathologists; Predictive Value of Tests; Reproducibility of Results; Skin Neoplasms
PubMed: 34509059
DOI: 10.1016/j.ejca.2021.06.049 -
The Australasian Journal of Dermatology Feb 2022Non-invasive skin imaging features of main skin inflammatory and autoimmune diseases have been reported, although a comprehensive review of their correlation with...
BACKGROUND/OBJECTIVES
Non-invasive skin imaging features of main skin inflammatory and autoimmune diseases have been reported, although a comprehensive review of their correlation with histopathologic features is currently lacking. Therefore, the aim of this paper was to review the correlation of dermoscopic, reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) criteria of main inflammatory and autoimmune skin diseases with their corresponding histopathologic criteria correlation.
METHODS
Studies on human subjects affected by main inflammatory and autoimmune diseases, defining the correlation of dermoscopic, RCM or OCT with histopathologic criteria, were included in the review. Five groups of diseases were identified and described: psoriasiform, spongiotic and interface dermatitis, bullous diseases and scleroderma.
RESULTS
Psoriasiform dermatitis was typified by white scales, corresponding to hyperkeratosis, and vessels, observed with RCM and OCT. Spongiosis, corresponding to dark areas within the epidermis with RCM and OCT, was the main feature of spongiotic dermatitis. Interface dermatitis was characterised by dermoepidermal junction obscuration. Blisters, typical of bullous diseases, were visualised as dark areas with RCM and OCT while scleroderma lesions were characterised by dermoscopic fibrotic beams, related to dermal thickness variations, with specific OCT and histopathologic correlations.
CONCLUSIONS
Although the role of RCM and OCT has yet to be defined in clinical practice, non-invasive skin imaging shows promising results on inflammatory and autoimmune skin diseases, due to the correlation with histopathologic features.
Topics: Dermatitis; Dermoscopy; Humans; Microscopy, Confocal; Psoriasis; Scleroderma, Localized; Skin Diseases, Vesiculobullous; Tomography, Optical Coherence
PubMed: 34423852
DOI: 10.1111/ajd.13695 -
JAMA Dermatology Sep 2021Dermoscopy increases the diagnostic accuracy for melanoma. However, the accuracy of individual structures and patterns used in melanoma detection has not been... (Meta-Analysis)
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.
Topics: Dermoscopy; Humans; Melanoma; Pigmentation Disorders; Retrospective Studies; Skin Diseases; Skin Neoplasms
PubMed: 34347005
DOI: 10.1001/jamadermatol.2021.2845 -
Dermatology (Basel, Switzerland) 2022The common inflammatory scalp diseases, such as psoriasis, seborrheic dermatitis, lichen planopilaris, discoid lupus erythematosus, contact dermatitis, or pemphigus may... (Review)
Review
BACKGROUND
The common inflammatory scalp diseases, such as psoriasis, seborrheic dermatitis, lichen planopilaris, discoid lupus erythematosus, contact dermatitis, or pemphigus may share similar clinical features.
OBJECTIVE
To identify and systematically review the available evidence on the accuracy of trichoscopy in inflammatory scalp disorders.
METHODS
A systematic review was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 58 articles were included in the analysis.
RESULTS
The following trichoscopy features were found to show the highest specificity for the respective diseases: in psoriasis: diffuse scaling, simple and twisted red loops, red dots and globules, and glomerular vessels; in seborrheic dermatitis: atypical vessels, thin arborizing vessels, and structureless red areas; in discoid lupus erythematosus: follicular plugs and erythema encircling follicles; in lichen planopilaris: milky red areas or fibrotic patches; in contact dermatitis: twisted red loops; in pemphigus foliaceus: white polygonal structures and serpentine vessels; in pemphigus vulgaris: red dots with whitish halo and lace-like vessels; and in dermatomyositis: lake-like vascular structures.
LIMITATIONS
Different nomenclature and variability in parameters, which were analyzed in different studies.
CONCLUSION
This systemic analysis indicates that trichoscopy may be used with high accuracy in the differential diagnosis of inflammatory scalp diseases.
Topics: Dermatitis, Contact; Dermatitis, Seborrheic; Dermoscopy; Humans; Lichen Planus; Lupus Erythematosus, Discoid; Pemphigus; Psoriasis; Scalp; Scalp Dermatoses
PubMed: 34265772
DOI: 10.1159/000517516 -
Journal of Medical Internet Research Jul 2021Recent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on par... (Review)
Review
BACKGROUND
Recent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on par with or better than dermatologists with respect to the classification tasks of single images. However, in clinical practice, dermatologists also use other patient data beyond the visual aspects present in a digitized image, further increasing their diagnostic accuracy. Several pilot studies have recently investigated the effects of integrating different subtypes of patient data into CNN-based skin cancer classifiers.
OBJECTIVE
This systematic review focuses on the current research investigating the impact of merging information from image features and patient data on the performance of CNN-based skin cancer image classification. This study aims to explore the potential in this field of research by evaluating the types of patient data used, the ways in which the nonimage data are encoded and merged with the image features, and the impact of the integration on the classifier performance.
METHODS
Google Scholar, PubMed, MEDLINE, and ScienceDirect were screened for peer-reviewed studies published in English that dealt with the integration of patient data within a CNN-based skin cancer classification. The search terms skin cancer classification, convolutional neural network(s), deep learning, lesions, melanoma, metadata, clinical information, and patient data were combined.
RESULTS
A total of 11 publications fulfilled the inclusion criteria. All of them reported an overall improvement in different skin lesion classification tasks with patient data integration. The most commonly used patient data were age, sex, and lesion location. The patient data were mostly one-hot encoded. There were differences in the complexity that the encoded patient data were processed with regarding deep learning methods before and after fusing them with the image features for a combined classifier.
CONCLUSIONS
This study indicates the potential benefits of integrating patient data into CNN-based diagnostic algorithms. However, how exactly the individual patient data enhance classification performance, especially in the case of multiclass classification problems, is still unclear. Moreover, a substantial fraction of patient data used by dermatologists remains to be analyzed in the context of CNN-based skin cancer classification. Further exploratory analyses in this promising field may optimize patient data integration into CNN-based skin cancer diagnostics for patients' benefits.
Topics: Dermoscopy; Humans; Melanoma; Neural Networks, Computer; Skin Neoplasms
PubMed: 34255646
DOI: 10.2196/20708