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Journal of Clinical Medicine Mar 2024: The umbilicus is a fibrous remnant located in the centre of the abdomen. Various entities may be encountered in this special anatomical location; however, little is... (Review)
Review
: The umbilicus is a fibrous remnant located in the centre of the abdomen. Various entities may be encountered in this special anatomical location; however, little is known about their dermoscopic presentation. The aim of this study was to provide a comprehensive summary of existing evidence on dermoscopic features of umbilical lesions. : Studies assessing dermoscopic images of umbilical lesions were included in this study. No age, ethnicity or skin phototype restrictions were applied. Papers assessing lesions outside of the umbilical area, lacking dermoscopic images and/or dermoscopic description and not related to the topic were excluded. Embase, Medline and Cochrane Library were searched from inception to the end of May 2023. The Joanna Briggs Institute critical appraisal tools were used to evaluate the risk of bias of the selected studies. The quality and the level of evidence of included studies were assessed according to the Oxford 2011 Levels of Evidence. Thirty-four studies reporting a total of 39 lesions met the inclusion criteria and were included in qualitative analysis. : A qualitative synthesis of the following entities was performed: melanoma, nevi, basal cell carcinoma, fibroepithelioma of Pinkus, Sister Mary Joseph nodule, mycosis fungoides, dermatofibroma, endometriosis, epidermal cyst, granuloma, intravascular papillary endothelial hyperplasia, lichen planus, omphalolith, seborrheic keratosis, and syringoma. : Dermoscopy is a non-invasive technique that may be useful in the differential diagnosis of umbilical lesions. The main limitations of this study were lack of a high level of evidence in the studies and the lack of uniformity in applied dermoscopic terminology between included studies.
PubMed: 38542014
DOI: 10.3390/jcm13061790 -
Frontiers in Medicine 2023Vitiligo is a multifaceted autoimmune depigmenting disorder affecting around 0.5 to 2.0% of individuals globally. Standardizing diagnosis and therapy tracking can be...
UNLABELLED
Vitiligo is a multifaceted autoimmune depigmenting disorder affecting around 0.5 to 2.0% of individuals globally. Standardizing diagnosis and therapy tracking can be arduous, as numerous clinical evaluation methods are subject to interobserver variability and may not be validated. Therefore, there is a need for diagnostic tools that are objective, dependable, and preferably non-invasive.
AIMS
This systematic review provides a comprehensive overview of the non-invasive objective skin measurement methods that are currently used to evaluate the diagnosis, severity, and progression of vitiligo, as well as the advantages and limitations of each technique.
METHODS
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was used for the systematic review. Scopus, Embase, Cochrane Library, and Web of Science databases were comprehensively searched for non-invasive imaging and biophysical skin measuring methods to diagnose, evaluate the severity of, or monitor the effects of vitiligo treatment. The risk of bias in included articles was assessed using the QUADAS-2 quality assessment scale.
RESULTS
An extensive literature search resulted in 64 studies for analysis, describing eight imaging techniques (reflectance confocal microscopy, computer-aided imaging analysis, optical coherence tomography, infrared photography, third-harmonic generation microscopy, multiphoton microscopy, ultraviolet light photography, and visible light/digital photograph), and three biophysical approaches (dermoscopy, colorimetry, spectrometry) used in diagnosing and assessing vitiligo. Pertinent information about functionality, mechanisms of action, sensitivity, and specificity was obtained for all studies, and insights into the strengths and limitations of each diagnostic technique were addressed. Methodological study quality was adequate; however, statistical analysis was not achievable because of the variety of methods evaluated and the non-standardized reporting of diagnostic accuracy results.
CONCLUSIONS
The results of this systematic review can enhance clinical practice and research by providing a comprehensive overview of the spectrum of non-invasive imaging and biophysical techniques in vitiligo assessment. Studies with larger sample sizes and sound methodology are required to develop verified methods for use in future practice and research.
SYSTEMATIC REVIEW REGISTRATION
(PROSPERO) database, (CRD42023395996).
PubMed: 37575985
DOI: 10.3389/fmed.2023.1200963 -
Journal of Clinical Medicine Mar 2024: Androgenetic alopecia, the most common cause of non-scarring hair loss, is a consequence of the gradual miniaturization of the hair follicles. In the majority of male... (Review)
Review
: Androgenetic alopecia, the most common cause of non-scarring hair loss, is a consequence of the gradual miniaturization of the hair follicles. In the majority of male androgenetic alopecia cases, a patient's history and clinical evaluation may be sufficient to establish the diagnosis, while for women, they should be supplemented with trichoscopy. : The PubMed and Scopus databases were used to collate published studies and to analyze the most typical trichoscopic findings in patients diagnosed with androgenetic alopecia. A total of 34 articles were retrieved after exclusion. : The most common features identified using trichoscopy included hair diameter variability (94.07% of patients), vellus hairs (66.45%) and the peripilar sign (43.27%). Others, such as the honeycomb pattern, yellow and white dots, were less relevant. : We concluded that hair diameter variability, vellus hairs and the peripilar sign represented valuable indicators for the diagnosis of androgenetic alopecia.
PubMed: 38610726
DOI: 10.3390/jcm13071962 -
Frontiers in Artificial Intelligence 2023Detecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used...
INTRODUCTION
Detecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used to identify and study skin cancer, but the blurred boundaries between lesions and besieging tissues can lead to incorrect identification. Artificial Intelligence (AI) models, including vision transformers, have been proposed as a solution, but variations in symptoms and underlying effects hinder their performance.
OBJECTIVE
This scoping review synthesizes and analyzes the literature that uses vision transformers for skin lesion detection.
METHODS
The review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Revise) guidelines. The review searched online repositories such as IEEE Xplore, Scopus, Google Scholar, and PubMed to retrieve relevant articles. After screening and pre-processing, 28 studies that fulfilled the inclusion criteria were included.
RESULTS AND DISCUSSIONS
The review found that the use of vision transformers for skin cancer detection has rapidly increased from 2020 to 2022 and has shown outstanding performance for skin cancer detection using dermoscopy images. Along with highlighting intrinsic visual ambiguities, irregular skin lesion shapes, and many other unwanted challenges, the review also discusses the key problems that obfuscate the trustworthiness of vision transformers in skin cancer diagnosis. This review provides new insights for practitioners and researchers to understand the current state of knowledge in this specialized research domain and outlines the best segmentation techniques to identify accurate lesion boundaries and perform melanoma diagnosis. These findings will ultimately assist practitioners and researchers in making more authentic decisions promptly.
PubMed: 37529760
DOI: 10.3389/frai.2023.1202990 -
Dermatology Practical & Conceptual Oct 2023Hair and scalp disorders are of significant interest for physicians dealing with dark phototypes due to their prevalence and potential aesthetic impact resulting from a... (Review)
Review
Hair and scalp disorders are of significant interest for physicians dealing with dark phototypes due to their prevalence and potential aesthetic impact resulting from a higher tendency for scarring. In order to facilitate their non-invasive diagnosis, several dermoscopic studies have been published, yet data are sparse and no systematic analysis of the literature has been performed so far. This systematic literature review summarizes published data on trichoscopy of hair and scalp diseases (trichoscopic findings, used setting, pathological correlation, and level of evidence of studies). A total of 60 papers addressing 19 different disorders (eight non-cicatricial alopecias, nine cicatricial alopecias, and two hair shaft disorders) were assessed, for a total of 2636 instances. They included one cross-sectional analysis, 20 case-control studies, 25 case-series, and 14 single case-reports, so the level of evidence was V and IV in 65% and 33% of cases, respectively, with only one study showing a level of evidence of III. Notably, although there is a considerable body of literature on trichoscopy of hair/scalp diseases, our review underlined that potentially significant variables (e.g., disease stage or hair texture) are often not taken into account in published analyses, with possible biases on trichoscopic patterns, especially when it comes to hair shaft changes. Further analyses considering all such issues are therefore needed.
PubMed: 37874991
DOI: 10.5826/dpc.1304S1a310S -
Journal of Personalized Medicine May 2024Acral amelanotic melanomas (AAMs), a rare subset of melanomas located on acral sites such as the palms, soles, and subungual areas, are diagnostically challenging due to... (Review)
Review
BACKGROUND
Acral amelanotic melanomas (AAMs), a rare subset of melanomas located on acral sites such as the palms, soles, and subungual areas, are diagnostically challenging due to their lack of typical pigmentation and often benign clinical appearance. Misdiagnosis is common, leading to delays in treatment and potentially worse outcomes. This systematic review aims to synthesise evidence on cases of AAM initially misdiagnosed as other conditions, to better understand their clinical and epidemiological characteristics, diagnostic pitfalls, and management strategies.
METHODS
A comprehensive search of the MEDLINE/PubMed, EMBASE, and SCOPUS databases was conducted up to March 2024. Case reports and small case series of AAMs initially misdiagnosed as other conditions were included. Data on patient demographics, clinical presentation, and diagnostic methods were collected and analyzed.
RESULTS
Of the 152 records identified, 26 cases from 23 articles met the inclusion criteria. A demographic analysis revealed that the gender distribution appears to be perfectly balanced, with an age range of 38 to 91 years. Misdiagnoses included non-healing ulcers or traumatic lesions (37.5%), benign proliferative lesions (29.2%) and infectious lesions (20.8%). The foot was the most affected site (53.8%). Notably, a histological evaluation was performed in 50% of cases involving the upper extremities, in contrast to only 7.1% of cases involving the foot and 0% of cases of the heel. This discrepancy suggests a reluctance to perform biopsies in the lower extremities, which may contribute to a higher misdiagnosis rate in these areas.
CONCLUSIONS
The underutilization of biopsy in the diagnosis of lower extremity lesions contributes significantly to the misdiagnosis and delay in treatment of AAMs. Especially when the clinical assessment and dermoscopy are inconclusive, biopsies of suspicious lesions are essential. Immunohistochemistry and markers such as PRAME are critical in differentiating melanoma from other malignancies such as clear cell sarcoma. This review highlights the need for increased vigilance and a proactive diagnostic approach to increase early detection rates and improve prognostic outcomes.
PubMed: 38793100
DOI: 10.3390/jpm14050518 -
Cancers Sep 2023Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes.... (Review)
Review
BACKGROUND
Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma.
OBJECTIVE
The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma.
METHODS
A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives.
RESULTS
We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%.
CONCLUSIONS
Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability.
PubMed: 37835388
DOI: 10.3390/cancers15194694 -
Dermatology Practical & Conceptual Apr 2024Rosettes are a cluster of shiny white dots in the shape of a four-leaf clover seen under polarized dermoscopic light. Historically, rosettes were primarily reported in... (Review)
Review
INTRODUCTION
Rosettes are a cluster of shiny white dots in the shape of a four-leaf clover seen under polarized dermoscopic light. Historically, rosettes were primarily reported in actinic keratoses and squamous cell carcinoma. However, rosettes have also been reported in other conditions.
OBJECTIVES
The objective of this systematic review to elucidate the breadth of diagnoses exhibiting this unique dermoscopic phenomenon.
METHODS
A review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Literature searches were performed in MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials and Web of Science, as well as a manual search of the reference lists of screened articles.
RESULTS
A total of 73 articles met the inclusion criteria. Out of these, 47 distinct diagnoses with rosette were identified. Among neoplastic conditions, keratinizing neoplasms had the highest number of articles reported (N = 19). Discoid lupus was the most commonly reported diagnosis within the inflammatory category (N = 6). Molluscum contagiosum was the predominant diagnosis among infectious entities (N = 3), while acroangiodermatitis was the sole diagnosis reported in the vascular category (N = 1).
CONCLUSIONS
These findings confirm rosettes are not specific to keratinocytic growths and are observed in a wide range of conditions. Knowledge of the breadth of conditions with rosettes may aid clinicians when developing a differential diagnosis of a growth or an eruption with rosettes under dermoscopy.
PubMed: 38810026
DOI: 10.5826/dpc.1402a125 -
Dermatology Practical & Conceptual Oct 2023Dermoscopy has been showed to facilitate the non-invasive recognition of several infectious disorders (infectiouscopy) thanks to the detection of peculiar clues.... (Review)
Review
Dermoscopy has been showed to facilitate the non-invasive recognition of several infectious disorders (infectiouscopy) thanks to the detection of peculiar clues. Although most of the knowledge on this topic comes from studies involving light-skinned patients, there is growing evidence about its use also in dark phototypes. This systematic literature review summarizes published data on dermoscopy of parasitic, bacterial, viral and fungal dermatoses (dermoscopic findings, used setting, pathological correlation, and level of evidence of studies) and provides a homogeneous terminology of reported dermoscopic features according to a standardized methodology. A total of 66 papers addressing 41 different dermatoses (14 bacterial, 5 viral, 11 fungal infections, and 11 parasitoses/bites and stings) and involving a total of 1096 instances were included in the analysis. The majority of them displayed a level of evidence of V (44 single case reports and 21 case series), with only 1 study showing a level of evidence of IV (case-control analysis). Moreover, our analysis also highlighted a high variability in the terminology used in the retrieved studies. Thus, although promising, further studies designed according to a systematic and standardized approach are needed for better characterization of dermoscopy of infectious skin infections.
PubMed: 37874993
DOI: 10.5826/dpc.1304S1a309S -
Sensors (Basel, Switzerland) Oct 2023Skin cancer is considered a dangerous type of cancer with a high global mortality rate. Manual skin cancer diagnosis is a challenging and time-consuming method due to... (Review)
Review
Skin cancer is considered a dangerous type of cancer with a high global mortality rate. Manual skin cancer diagnosis is a challenging and time-consuming method due to the complexity of the disease. Recently, deep learning and transfer learning have been the most effective methods for diagnosing this deadly cancer. To aid dermatologists and other healthcare professionals in classifying images into melanoma and nonmelanoma cancer and enabling the treatment of patients at an early stage, this systematic literature review (SLR) presents various federated learning (FL) and transfer learning (TL) techniques that have been widely applied. This study explores the FL and TL classifiers by evaluating them in terms of the performance metrics reported in research studies, which include true positive rate (TPR), true negative rate (TNR), area under the curve (AUC), and accuracy (ACC). This study was assembled and systemized by reviewing well-reputed studies published in eminent fora between January 2018 and July 2023. The existing literature was compiled through a systematic search of seven well-reputed databases. A total of 86 articles were included in this SLR. This SLR contains the most recent research on FL and TL algorithms for classifying malignant skin cancer. In addition, a taxonomy is presented that summarizes the many malignant and non-malignant cancer classes. The results of this SLR highlight the limitations and challenges of recent research. Consequently, the future direction of work and opportunities for interested researchers are established that help them in the automated classification of melanoma and nonmelanoma skin cancers.
Topics: Humans; Prospective Studies; Skin Neoplasms; Melanoma; Skin; Machine Learning
PubMed: 37896548
DOI: 10.3390/s23208457