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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 -
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 -
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 -
The Lancet. Public Health Jun 2024Vitiligo is a chronic autoimmune disease characterised by depigmented skin patches, which can pose substantial psychosocial challenges particularly in individuals with...
BACKGROUND
Vitiligo is a chronic autoimmune disease characterised by depigmented skin patches, which can pose substantial psychosocial challenges particularly in individuals with dark skin tones. Despite its impact on quality of life, there is an absence of standardised global epidemiological data. We sought to address this gap with the present study.
METHODS
In this study we did a systematic review and modelling analysis to estimate the global, regional, and national prevalence and incidence of vitiligo. We did a comprehensive search of nine digital libraries (PubMed, Embase, Web of Science, Scientific Electronic Library Online, KCI Korean Journal Database, Russian Science Citation Index, Western Pacific Region Index Medicus, Informit, and Health Research and Development Information Network) from inception up to May 25, 2023. We included cross-sectional or cohort studies reporting the incidence rate or prevalence of vitiligo, or data from which incidence rate or prevalence could be calculated, in the general population of a country or area of a country. Summary estimate data were extracted. A main outcome was to estimate the worldwide, regional, and country-specific lifetime prevalence of vitiligo diagnosed by physicians or dermatologists among the general population and in adults and children (as per age groups defined in included studies). We used a Bayesian hierarchical linear mixed model to estimate prevalence, and calculated number of affected individuals using the UN population structure in 2022. In estimating lifetime prevalence, studies reporting point or period prevalence were excluded. Our other main outcome was to estimate incidence rates of vitiligo, but due to a small number of studies, the data on incidence were presented in a descriptive summary. This study was registered on PROSPERO, CRD42023390433.
FINDINGS
Our search identified 22 192 records, of which 90 studies met our inclusion criteria. Of these studies, six focused on the incidence of vitiligo, 79 reported on the prevalence of vitiligo, and five provided data on both incidence and prevalence. 71 studies reported on lifetime prevalence. In the most recent years studied, incidence rates in the general population ranged from 24·7 cases (95% CI 24·3-25·2) per 100 000 person-years in South Korea in 2019, to 61·0 cases (60·6-61·4) in the USA in 2017. In individual studies, incidence rates showed an increasing trend over the periods studied. The global lifetime prevalence of vitiligo diagnosed by a physician or dermatologist was estimated at 0·36% (95% credible interval [CrI] 0·24-0·54) in the general population (28·5 million people [95% CrI 18·9-42·6]), 0·67% (0·43-1·07) in the adult population (37·1 million adults [23·9-58·9]), and 0·24% (0·16-0·37) in the child population (5·8 million children [3·8-8·9]). Vitiligo prevalence was higher in adults than in children across all regions. Central Europe and south Asia reported the highest prevalence (0·52% [0·28-1·07] and 0·52% [0·33-0·82], respectively, in the general population).
INTERPRETATION
This study highlights the need for standardised epidemiological data collection globally to inform public health policies and improve vitiligo diagnosis and management. Emphasis on the impact on individuals with darker skin tones is crucial to reducing stigma and improving quality of life. Furthermore, our study highlights the need to conduct more research in regions and populations that have been historically under-represented, to effectively address the worldwide burden of vitiligo.
FUNDING
None.
Topics: Humans; Cost of Illness; Global Health; Incidence; Prevalence; Vitiligo; Child; Adult
PubMed: 38552651
DOI: 10.1016/S2468-2667(24)00026-4 -
Anais Brasileiros de Dermatologia 2024Molecularly targeted therapies, such as monoclonal antibodies (mAbs) and Janus Kinase inhibitors (JAKis), have emerged as essential tools in the treatment of...
BACKGROUND
Molecularly targeted therapies, such as monoclonal antibodies (mAbs) and Janus Kinase inhibitors (JAKis), have emerged as essential tools in the treatment of dermatological diseases. These therapies modulate the immune system through specific signaling pathways, providing effective alternatives to traditional systemic immunosuppressive agents. This review aims to provide an updated summary of targeted immune therapies for inflammatory skin diseases, considering their pathophysiology, efficacy, dosage, and safety profiles.
METHODS
The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. A systematic search was conducted on PubMed over the past 10 years, focusing on randomized clinical trials, case reports, and case series related to targeted immune therapies in dermatology. Eligibility criteria were applied, and data were extracted from each study, including citation data, study design, and results.
RESULTS
We identified 1360 non-duplicate articles with the initial search strategy. Title and abstract review excluded 1150, while a full-text review excluded an additional 50 articles. The review included 143 studies published between 2012 and 2022, highlighting 39 drugs currently under investigation or in use for managing inflammatory skin diseases.
STUDY LIMITATIONS
The heterogeneity of summarized information limits this review. Some recommendations originated from data from clinical trials, while others relied on retrospective analyses and small case series. Recommendations will likely be updated as new results emerge.
CONCLUSION
Targeted therapies have revolutionized the treatment of chronic skin diseases, offering new options for patients unresponsive to standard treatments. Paradoxical reactions are rarely observed. Further studies are needed to fully understand the mechanisms and nature of these therapies. Overall, targeted immune therapies in dermatology represent a promising development, significantly improving the quality of life for patients with chronic inflammatory skin diseases.
Topics: Humans; Antibodies, Monoclonal; Dermatologists; Janus Kinase Inhibitors; Molecular Targeted Therapy; Skin Diseases
PubMed: 38521706
DOI: 10.1016/j.abd.2023.10.002 -
JMIR Dermatology Dec 2023Dermatological conditions, especially when severe, can lead to sleep disturbances that affect a patient's quality of life. However, limited research exists on the... (Review)
Review
BACKGROUND
Dermatological conditions, especially when severe, can lead to sleep disturbances that affect a patient's quality of life. However, limited research exists on the efficacy of treatments for improving sleep parameters in skin conditions.
OBJECTIVE
The objective was to perform a systematic review of the literature on dermatological conditions and the treatments available for improving sleep parameters.
METHODS
A literature review was performed using the PubMed, Ovid MEDLINE, Embase, Cochrane, and ClinicalTrials.gov databases from 1945 to 2021. After filtering based on our exclusion criteria, studies were graded using the SORT (Strength of Recommendation Taxonomy) algorithm, and only those receiving a grade of "2" or better were included.
RESULTS
In total, 25 treatment studies (n=11,025) assessing sleep parameters related to dermatological conditions were found. Dupilumab appeared to be the best-supported and most effective treatment for improving sleep in atopic dermatitis (AD) but had frequent adverse effects. Topical treatments for AD were mostly ineffective, but procedural treatments showed some promise. Treatments for other conditions appeared efficacious.
CONCLUSIONS
The evaluation of sleep parameter changes in dermatological treatments is predominantly restricted to AD. Systemic interventions such as dupilumab and procedural interventions were the most efficacious. Sleep changes in other dermatoses were limited by a paucity of available studies. The inclusion of a sleep assessment component to a broader range of dermatological treatment studies is warranted.
PubMed: 38090791
DOI: 10.2196/48713 -
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 -
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 -
Health Science Reports Sep 2023Metabolic syndrome (MetS) is a well-known noncommunicable disease that plays a significant role in emerging other chronic disorders and following complications. MetS is...
BACKGROUND AND AIM
Metabolic syndrome (MetS) is a well-known noncommunicable disease that plays a significant role in emerging other chronic disorders and following complications. MetS is also involved in the pathophysiology of numerous dermatological diseases. We aim to evaluate the association of MetS with the most prevalent dermatological diseases.
METHODS
A systematic search was carried out on PubMed, Science Direct, Web of Science, Cochrane, as well as the Google Scholar search engine. Only English case-control studies regarding MetS and any skin disease from the beginning of 2010 up to November 15, 2022, were selected. The study was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA).
RESULTS
A total of 37 studies (13,830 participants) met the inclusion criteria. According to our result, patients with psoriasis, hidradenitis suppurativa (HS), vitiligo, androgenetic alopecia (AGA), and lichen planus (LP) have a higher chance of having MetS compared to the general population. Furthermore, people with seborrheic dermatitis (SED) and rosacea are more prone to insulin resistance, high blood pressure (BP), and higher blood lipids. After pooling data, the meta-analysis revealed a significant association between MetS and skin diseases (pooled odds ratio [OR]: 3.28, 95% confidence interval: 2.62-4.10). Concerning the type of disease, MetS has been correlated with AGA (OR: 11.86), HS (OR: 4.46), LP (OR: 3.79), and SED (OR: 2.45). Psoriasis also showed a significant association but with high heterogeneity (OR: 2.89). Moreover, skin diseases and MetS are strongly associated in Spain (OR: 5.25) and Thailand (OR: 11.86). Regarding the metaregression model, the effect size was reduced with increasing age (OR: 0.965), while the size increased with AGA (OR: 3.064).
CONCLUSIONS
MetS is closely associated with skin complications. Dermatologists and other multidisciplinary teams should be cautious while treating these patients to prevent severe complications resulting from MetS.
PubMed: 37752973
DOI: 10.1002/hsr2.1576