-
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 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 -
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 -
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 -
Acta Dermato-venereologica Oct 2021Trichotillomania is formally classified as a mental health disorder, but it is commonly diagnosed by dermatologists. The aim of this systematic review is to assess the...
Trichotillomania is formally classified as a mental health disorder, but it is commonly diagnosed by dermatologists. The aim of this systematic review is to assess the diagnostic value of trichoscopy in diagnosing trichotillomania. The analysis identified the 7 most specific trichoscopic features in trichotillomania. These features had the following prevalence and specificity: trichoptilosis (57.5%; 73/127 and 97.5%, respectively), v-sign (50.4%; 63/125 and 99%), hook hairs (43.1%; 28/65 and 100%), flame hairs (37.1%; 52/140 and 96.5%), coiled hairs (36.8%; 46/125 and 99.6%), tulip hairs (36.4%; 28/77 and 89.6%), and hair powder (35.6%; 42/118 and 97.9%). The 2 most common, but least specific, features were broken hairs and black dots. In conclusion, trichoscopy is a reliable new diagnostic method for hair loss caused by hair pulling. Trichoscopy should be included as a standard procedure in the differential diagnosis of trichotillomania in clinical practice.
Topics: Alopecia; Dermoscopy; Diagnosis, Differential; Hair; Humans; Trichotillomania
PubMed: 34184065
DOI: 10.2340/00015555-3859 -
Frontiers in Medicine 2021Melanoma has the highest mortality rate among skin cancers, and early-diagnosis is essential to maximize survival rate. The current procedure for melanoma diagnosis is...
Melanoma has the highest mortality rate among skin cancers, and early-diagnosis is essential to maximize survival rate. The current procedure for melanoma diagnosis is based on dermoscopy, i.e., a qualitative visual inspection of lesions with intrinsic limited diagnostic reliability and reproducibility. Other non-invasive diagnostic techniques may represent valuable solutions to retrieve additional objective information of a lesion. This review aims to compare the diagnostic performance of non-invasive techniques, alternative to dermoscopy, for melanoma detection in clinical settings. A systematic review of the available literature was performed using PubMed, Scopus and Google scholar databases (2010-September 2020). All human, , non-invasive studies using techniques, alternative to dermoscopy, for melanoma diagnosis were included with no restriction on the recruited population. The reference standard was histology but dermoscopy was accepted only in case of benign lesions. Attributes of the analyzed studies were compared, and the quality was evaluated using CASP Checklist. For studies in which the investigated technique was implemented as a diagnostic tool (DTA studies), the QUADAS-2 tool was applied. For DTA studies that implemented a melanoma vs. other skin lesions classification task, a meta-analysis was performed reporting the SROC curves. Sixty-two references were included in the review, of which thirty-eight were analyzed using QUADAS-2. Study designs were: clinical trials (13), retrospective studies (10), prospective studies (8), pilot studies (10), multitiered study (1); the remain studies were proof of concept or had undefined study type. Studies were divided in categories based on the physical principle employed by each diagnostic technique. Twenty-nine out of thirty-eight DTA studies were included in the meta-analysis. Heterogeneity of studies' types, testing strategy, and diagnostic task limited the systematic comparison of the techniques. Based on the SROC curves, spectroscopy achieved the best performance in terms of sensitivity (93%, 95% CI 92.8-93.2%) and specificity (85.2%, 95%CI 84.9-85.5%), even though there was high concern regarding robustness of metrics. Reflectance-confocal-microscopy, instead, demonstrated higher robustness and a good diagnostic performance (sensitivity 88.2%, 80.3-93.1%; specificity 65.2%, 55-74.2%). Best practice recommendations were proposed to reduce bias in future DTA studies. Particular attention should be dedicated to widen the use of alternative techniques to conventional dermoscopy.
PubMed: 33968951
DOI: 10.3389/fmed.2021.637069 -
International Journal of Environmental... Feb 2021Early detection of melanoma is critical to reduce the mortality and morbidity rates of this tumor. Total body photography (TBP) may aid in the early detection of... (Review)
Review
Early detection of melanoma is critical to reduce the mortality and morbidity rates of this tumor. Total body photography (TBP) may aid in the early detection of melanoma. To summarize the current evidence on TBP for the early detection of melanoma, we performed a systematic literature search in Medline, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) for eligible records up to 6th August 2020. Outcomes of interest included melanoma incidence, incisional and excisional biopsy rates, as well as the Breslow's index of detected tumors. Results from individual studies were described qualitatively. The risks of bias and applicability of the included studies was assessed using the QUADAS-2 checklist. In total, 14 studies published between 1997 and 2020 with an overall sample size of = 12082 (range 100-4692) were included in the qualitative analysis. Individuals undergoing TBP showed a trend towards a lower Breslow's thickness and a higher proportion of in situ melanomas compared to those without TBP. The number needed to excise one melanoma varied from 3:1 to 14.3:1 and was better for lesions that arose de novo than for tracked ones. The included studies were judged to be of unclear methodological concern with specific deficiencies in the domains "flow and timing" and "reference standard". The use of TBP can improve the early detection of melanoma in high-risk populations. Future studies are warranted to reduce the heterogeneity of phenotypic risk factor definition and the technical implementation of TBP. Artificial intelligence-assisted analysis of images derived from 3-D TBP systems and digital dermoscopy may further improve the early detection of melanoma.
Topics: Artificial Intelligence; Dermoscopy; Humans; Melanoma; Photography; Sensitivity and Specificity; Skin Neoplasms
PubMed: 33578996
DOI: 10.3390/ijerph18041726