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Healthcare (Basel, Switzerland) Jun 2024The prevalence of dermatological conditions in primary care, coupled with challenges such as dermatologist shortages and rising consultation costs, highlights the need... (Review)
Review
The prevalence of dermatological conditions in primary care, coupled with challenges such as dermatologist shortages and rising consultation costs, highlights the need for innovative solutions. Artificial intelligence (AI) holds promise for improving the diagnostic analysis of skin lesion images, potentially enhancing patient care in primary settings. This systematic review following PRISMA guidelines examined primary studies (2012-2022) assessing AI algorithms' diagnostic accuracy for skin diseases in primary care. Studies were screened for eligibility based on their availability in the English language and exclusion criteria, with risk of bias evaluated using QUADAS-2. PubMed, Scopus, and Web of Science were searched. Fifteen studies (2019-2022), primarily from Europe and the USA, focusing on diagnostic accuracy were included. Sensitivity ranged from 58% to 96.1%, with accuracies varying from 0.41 to 0.93. AI applications encompassed triage and diagnostic support across diverse skin conditions in primary care settings, involving both patients and primary care professionals. While AI demonstrates potential for enhancing the accuracy of skin disease diagnostics in primary care, further research is imperative to address study heterogeneity and ensure algorithm reliability across diverse populations. Future investigations should prioritise robust dataset development and consider representative patient samples. Overall, AI may improve dermatological diagnosis in primary care, but careful consideration of algorithm limitations and implementation strategies is required.
PubMed: 38921305
DOI: 10.3390/healthcare12121192 -
Archives of Dermatological Research Jun 2024Steven Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN), grouped together under the terminology of epidermal necrolysis (EN), are a spectrum of... (Review)
Review
Steven Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN), grouped together under the terminology of epidermal necrolysis (EN), are a spectrum of life-threatening dermatologic conditions. A lack of standardization and validation for existing endpoints has been identified as a key barrier to the comparison of these therapies and development of evidenced-based treatment. Following PRISMA guidelines, we conducted a systematic review of prospective studies involving systemic or topical treatments for EN, including dressing and ocular treatments. Outcomes were separated into mortality assessment, cutaneous outcomes, non-cutaneous clinical outcomes, and mucosal outcomes. The COSMIN Risk of Bias tool was used to assess the quality of studies on reliability and measurement error of outcome measurement instruments. Outcomes across studies assessing treatment in the acute phase of EN were varied. Most data came from prospective case reports and cohort studies representing the lack of available randomized clinical trial data available in EN. Our search did not reveal any EN-specific validated measures or scoring tools used to assess disease progression and outcomes. Less than half of included studies were considered "adequate" for COSMIN risk of bias in reliability and measurement error of outcome measurement instruments. With little consensus about management and treatment of EN, consistency and validation of measured outcomes is of the upmost importance for future studies to compare outcomes across treatments and identify the most effective means of combating the disease with the highest mortality managed by dermatologists.
Topics: Humans; Stevens-Johnson Syndrome; Reproducibility of Results; Outcome Assessment, Health Care; Treatment Outcome; Bandages
PubMed: 38878166
DOI: 10.1007/s00403-024-03062-5 -
Computers in Biology and Medicine Jun 2024In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms.... (Review)
Review
In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual diagnostic methods used by dermatologists and oncologists. However, the application of such systems in clinical practice is severely limited due to a lack of generalizability and risks of potential misclassification. Successful implementation of artificial intelligence-based tools into clinicopathological practice requires a comprehensive study of the effectiveness and performance of existing models, as well as further promising areas for potential research development. The purpose of this systematic review is to investigate and evaluate the accuracy of artificial intelligence technologies for detecting malignant forms of pigmented skin lesions. For the study, 10,589 scientific research and review articles were selected from electronic scientific publishers, of which 171 articles were included in the presented systematic review. All selected scientific articles are distributed according to the proposed neural network algorithms from machine learning to multimodal intelligent architectures and are described in the corresponding sections of the manuscript. This research aims to explore automated skin cancer recognition systems, from simple machine learning algorithms to multimodal ensemble systems based on advanced encoder-decoder models, visual transformers (ViT), and generative and spiking neural networks. In addition, as a result of the analysis, future directions of research, prospects, and potential for further development of automated neural network systems for classifying pigmented skin lesions are discussed.
PubMed: 38875908
DOI: 10.1016/j.compbiomed.2024.108742 -
Clinical and Experimental Dermatology May 2024Body Dysmorphic Disorder (BDD) is a psychiatric condition involving a preoccupation with physical appearance disproportionate to physical findings, which are often...
Body Dysmorphic Disorder (BDD) is a psychiatric condition involving a preoccupation with physical appearance disproportionate to physical findings, which are often absent altogether. Previously published data has estimated its prevalence at 11.3-11.9% approximately, across various medical specialties. No recent systematic reviews strictly related to dermatology clinics and the prevalence of BDD have been published. The goal of the review was to gather a pooled prevalence of BDD in outpatient dermatology clinics around the world and further underline the importance of its recognition and appropriate treatment. Twenty-one articles tackling BDD in outpatient cosmetic and general dermatology clinics were selected. Studies were graded based on the Newcastle-Ottawa Scale (NOS) and the Statistical Package for the Social Sciences software (SPSS) was used to a calculate a mean for the pooled prevalence, yielding a weighted mean prevalence of 12.5% among general dermatology patients and 25.01% among cosmetic dermatology patients. The mean prevalence of BDD among general dermatology patients fell within previously reported numbers. It was, however, markedly higher than previously reported in cosmetic dermatology patients, which we postulate could be due to dermatologists being at the forefront of non-invasive cosmetic procedures. As such, we conclude that given the high prevalence of BDD among dermatology patients, we highlight the importance of keeping a high index of suspicion of BDD among dermatologists, ways to uncover it in a clinical setting, and additional data showcasing the importance of psychiatric treatment of these patients for better outcomes, all while avoiding unnecessary interventions.
PubMed: 38762899
DOI: 10.1093/ced/llae204 -
NPJ Digital Medicine May 2024Scientific research of artificial intelligence (AI) in dermatology has increased exponentially. The objective of this study was to perform a systematic review and... (Review)
Review
Scientific research of artificial intelligence (AI) in dermatology has increased exponentially. The objective of this study was to perform a systematic review and meta-analysis to evaluate the performance of AI algorithms for skin cancer classification in comparison to clinicians with different levels of expertise. Based on PRISMA guidelines, 3 electronic databases (PubMed, Embase, and Cochrane Library) were screened for relevant articles up to August 2022. The quality of the studies was assessed using QUADAS-2. A meta-analysis of sensitivity and specificity was performed for the accuracy of AI and clinicians. Fifty-three studies were included in the systematic review, and 19 met the inclusion criteria for the meta-analysis. Considering all studies and all subgroups of clinicians, we found a sensitivity (Sn) and specificity (Sp) of 87.0% and 77.1% for AI algorithms, respectively, and a Sn of 79.78% and Sp of 73.6% for all clinicians (overall); differences were statistically significant for both Sn and Sp. The difference between AI performance (Sn 92.5%, Sp 66.5%) vs. generalists (Sn 64.6%, Sp 72.8%), was greater, when compared with expert clinicians. Performance between AI algorithms (Sn 86.3%, Sp 78.4%) vs expert dermatologists (Sn 84.2%, Sp 74.4%) was clinically comparable. Limitations of AI algorithms in clinical practice should be considered, and future studies should focus on real-world settings, and towards AI-assistance.
PubMed: 38744955
DOI: 10.1038/s41746-024-01103-x -
International Journal of Dermatology May 2024Eosinophilic dermatosis of hematologic malignancy (EDHM) is a cutaneous manifestation seen in patients with hematoproliferative and lymphoproliferative disorders, most... (Review)
Review
Eosinophilic dermatosis of hematologic malignancy (EDHM) is a cutaneous manifestation seen in patients with hematoproliferative and lymphoproliferative disorders, most commonly chronic lymphocytic leukemia. This systematic review aimed to summarize the therapeutic interventions of EDHM. A comprehensive search yielded 71 studies, predominantly case reports and series. The most frequently reported modalities were systemic and topical corticosteroids, as well as treatment of the underlying malignancy. Responses to these treatments varied. Targeted therapies, including dupilumab and omalizumab, showed promise, as did other modalities such as montelukast, dapsone, doxycycline, and phototherapy. Higher-quality studies should be conducted to facilitate higher-quality management recommendations for EDHM.
PubMed: 38727148
DOI: 10.1111/ijd.17221 -
Dermatologic Surgery : Official... May 2024Limited access to dermatologic care may pose an obstacle to the early detection and intervention of cutaneous malignancies. The role of artificial intelligence (AI) in...
BACKGROUND
Limited access to dermatologic care may pose an obstacle to the early detection and intervention of cutaneous malignancies. The role of artificial intelligence (AI) in skin cancer diagnosis may alleviate potential care gaps.
OBJECTIVE
The aim of this systematic review was to offer an in-depth exploration of published AI algorithms trained on dermoscopic and macroscopic clinical images for the diagnosis of melanoma, basal cell carcinoma, and cutaneous squamous cell carcinoma (cSCC).
METHODS
Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a systematic review was conducted on peer-reviewed articles published between January 1, 2000, and January 26, 2023.
RESULTS AND DISCUSSION
Among the 232 studies in this review, the overall accuracy, sensitivity, and specificity of AI for tumor detection averaged 90%, 87%, and 91%, respectively. Model performance improved with time. Despite seemingly impressive performance, the paucity of external validation and limited representation of cSCC and skin of color in the data sets limits the generalizability of the current models. In addition, dermatologists coauthored only 12.9% of all studies included in the review. Moving forward, it is imperative to prioritize robustness in data reporting, inclusivity in data collection, and interdisciplinary collaboration to ensure the development of equitable and effective AI tools.
PubMed: 38722750
DOI: 10.1097/DSS.0000000000004223 -
International Journal of Psychiatry in... Apr 2024Hidradenitis suppurativa (HS) is an inflammatory disorder characterized by painful, deep follicular nodules, abscesses, sinus tracts, and scarring, most commonly... (Review)
Review
Hidradenitis suppurativa (HS) is an inflammatory disorder characterized by painful, deep follicular nodules, abscesses, sinus tracts, and scarring, most commonly presenting in the inguinal, axillary, and anogenital regions. This condition substantially decreases quality of life in affected individuals, resulting in higher rates of psychiatric disorders including depression, anxiety, suicidality, and substance use. The detrimental effects of HS are well documented by dermatologists, as individuals with HS make up a large proportion of the patients that they see daily. However, it is unclear whether psychiatrists are aware of the degree of psychosocial impairment present in HS patients. It is important that those in the field of psychiatry and behavioral medicine are aware of this condition and are comfortable managing it from a psychosocial perspective. This systematic review chronicles the existing literature on the psychosocial effects of HS and assesses the extent to which dermatology journals review these effects in comparison to psychiatry or behavioral medicine.
PubMed: 38644350
DOI: 10.1177/00912174241249215 -
NPJ Digital Medicine Apr 2024The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical... (Review)
Review
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.
PubMed: 38594408
DOI: 10.1038/s41746-024-01031-w -
European Journal of Dermatology : EJD Feb 2024Gel manicures have become part of a popular personal care service in the last two decades due to increased longevity of the polish and the added strength to the nail...
Gel manicures have become part of a popular personal care service in the last two decades due to increased longevity of the polish and the added strength to the nail plate. Prolonged exposure to nail ultraviolet (UV) lamps is required to cure the gel polish. Despite the increased use of UV nail lamps, there is limited consensus in the literature on the risk of skin malignancy associated with UV nail lamps. The objective of this article was to provide a systematic review of the risk of skin malignancy associated with the use of UV nail lamps and to synthesize evidence-based recommendations on their safe usage. A systematic review of the literature was conducted on the databases, Medline and Embase, in accordance with PRISMA guidelines. The search yielded 2,331 non-duplicate articles. Nine were ultimately included, of which three were case reports, one was a cross-sectional study, and five were experimental studies. The risk of bias per the Joanna Briggs Institute guidelines was high or unclear, likely due to the number of case reports included. Prolonged and repeated exposure to UV nail lamps may pose a low risk of skin cancer. It is important to note that the available evidence is weak, and patients should be informed about the limited data to make their own decisions. Dermatologists and other healthcare providers should be updated with the latest evidence to address patients' concerns about gel manicures and suggest practices which can effectively reduce the risk of cutaneous malignancy associated with gel manicures, such as the use of UV-blocking gloves or properly applied sunscreens.
Topics: Humans; Beauty; Cross-Sectional Studies; Skin Neoplasms; Nails; Sunscreening Agents; Ultraviolet Rays
PubMed: 38557455
DOI: 10.1684/ejd.2024.4616