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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 -
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 -
NPJ Digital Medicine May 2024
PubMed: 38789723
DOI: 10.1038/s41746-024-01138-0 -
American Journal of Reproductive... May 2024Seminal plasma hypersensitivity (SPH) is a rare and often misdiagnosed condition characterized by local and/or systemic reactions to seminal plasma proteins following... (Review)
Review
INTRODUCTION
Seminal plasma hypersensitivity (SPH) is a rare and often misdiagnosed condition characterized by local and/or systemic reactions to seminal plasma proteins following exposure to semen. We aimed to summarize key symptomatology, diagnostic features, and management options for SPH.
METHODS
The databases PubMed, EMBASE, Web of Science, Google Scholar, and Cochrane Review were searched with key words "seminal plasma hypersensitivity" and "seminal fluid allergy" through September 2023. Exclusion criteria included non-English articles, in vitro studies, publication before 1990, duplicates, and articles with no clinical relevance to SPH in women.
RESULTS
The search yielded 53 articles for review. Of these, 60.5% described systemic SPH and 39.5% described localized.
CONCLUSION
Diagnosis of SPH relies on a thorough patient history and confirmatory skin prick testing. The use of IgE assays is controversial and less accurate for cases of localized SPH. Knowledge of disease immunopathology, systemic versus localized symptom presentation, patient preference, and desire to conceive should guide management options. Artificial insemination has the potential for severe adverse reactions in systemic SPH so necessitates extra procedural precautions. SPH does not appear to impair fertility. Additional research on specific allergens implicated in SPH can aid in the development of more targeted immunotherapy approaches with improved safety and efficacy.
Topics: Humans; Male; Allergens; Hypersensitivity; Immunoglobulin E; Insemination, Artificial; Semen; Seminal Plasma Proteins; Skin Tests; Female
PubMed: 38775338
DOI: 10.1111/aji.13865 -
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 -
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 -
Cancers Apr 2024Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial... (Review)
Review
BACKGROUND
Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report the performance metrics of commercially available convolutional neural networks (CNNs) tasked with detecting MM.
METHODS
A systematic literature search was performed using CINAHL, Medline, Scopus, ScienceDirect and Web of Science databases.
RESULTS
A total of 16 articles reporting MM were included in this review. The combined number of melanomas detected was 1160, and non-melanoma lesions were 33,010. The performance of market-approved technology and clinician performance for classifying melanoma was highly heterogeneous, with sensitivity ranging from 16.4 to 100.0%, specificity between 40.0 and 98.3% and accuracy between 44.0 and 92.0%. Less heterogeneity was observed when clinicians worked in unison with AI, with sensitivity ranging between 83.3 and 100.0%, specificity between 83.7 and 87.3%, and accuracy between 86.4 and 86.9%.
CONCLUSION
Instead of focusing on the performance of AI versus clinicians for classifying melanoma, more consistent performance has been obtained when clinicians' work is supported by AI, facilitating management decisions and improving health outcomes.
PubMed: 38611119
DOI: 10.3390/cancers16071443 -
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 -
Cell Journal Feb 2024Exposure to phosgene, a colourless poisonous gas, can lead to various health issues including eye irritation, a dry and burning throat, vomiting, coughing, the...
Exposure to phosgene, a colourless poisonous gas, can lead to various health issues including eye irritation, a dry and burning throat, vomiting, coughing, the production of foamy sputum, difficulty in breathing, and chest pain. This systematic review aims to provide a comprehensive overview of the clinical manifestations and treatment of phosgene toxicity by systematically analyzing available literature. The search was carried out on various scientific online databases to include related studies based on inclusion and exclusion criteria with the use of PRISMA guidelines. The quality of the studies was assessed using the Mixed Methods Appraisal Tool (MMAT). Thirteen articles were included in this study after the screening process. Inhalation was found to be the primary health problem of phosgene exposure with respiratory symptoms such as coughing and dyspnea. Chest pain and pulmonary oedema were also observed in some cases. Furthermore, pulmonary crackle was the most common reported physical examination. Beyond respiratory tract health issues, other organs involvements such as cardiac, skin, eye, and renal were also reported in some studies. The symptoms can occur within minutes to hours after exposure, and the severity of symptoms depends on the amount of inhaled phosgene. The findings showed that bronchodilators can alleviate symptoms of bronchoconstriction caused by phosgene. Oxygen therapy is essential for restoring oxygen levels and improving respiratory function in cases of hypoxemia. In severe cases, endotracheal intubation and invasive mechanical ventilation are used for artificial respiration, along with the removal of tracheal secretions and pulmonary oedema fluid through suctioning as crucial components of supportive therapy.
PubMed: 38459726
DOI: 10.22074/cellj.2024.2011864.1405 -
BMC Medicine Feb 2024Allergic diseases impose a significant global disease burden, however, the influence of light at night exposure on these diseases in humans has not been comprehensively... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Allergic diseases impose a significant global disease burden, however, the influence of light at night exposure on these diseases in humans has not been comprehensively assessed. We aimed to summarize available evidence considering the association between light at night exposure and major allergic diseases through a systematic review and meta-analysis.
METHODS
We completed a search of six databases, two registries, and Google Scholar from inception until December 15, 2023, and included studies that investigated the influence of artificial light at night (ALAN, high vs. low exposure), chronotype (evening vs. morning chronotype), or shift work (night vs. day shift work) on allergic disease outcomes (asthma, allergic rhinitis, and skin allergies). We performed inverse-variance random-effects meta-analyses to examine the association between the exposures (ALAN exposure, chronotype, or shiftwork) and these allergic outcomes. Stratification analyses were conducted by exposure type, disease type, participant age, and geographical location along with sensitivity analyses to assess publication bias.
RESULTS
We included 12 publications in our review. We found that exposure to light at night was associated with higher odds of allergic diseases, with the strongest association observed for ALAN exposure (OR: 1.88; 95% CI: 1.04 to 3.39), followed by evening chronotype (OR: 1.35; 95% CI: 0.98 to 1.87) and exposure to night shift work (OR: 1.33; 95% CI: 1.06 to 1.67). When analyses were stratified by disease types, light at night exposure was significantly associated with asthma (OR: 1.62; 95% CI: 1.19 to 2.20), allergic rhinitis (OR: 1.89; 95% CI: 1.60 to 2.24), and skin allergies (OR: 1.11; 95% CI: 1.09 to 1.91). We also found that the association between light at night exposure and allergic diseases was more profound in youth (OR: 1.63; 95% CI: 1.07 to 2.48) than adults (OR: 1.30; 95% CI: 1.03 to 1.63). Additionally, we observed significant geographical variations in the association between light at night exposure and allergic diseases.
CONCLUSIONS
Light at night exposure was associated with a higher prevalence of allergic diseases, both in youth and adults. More long-term epidemiological and mechanistic research is required to understand the possible interactions between light at night and allergic diseases.
Topics: Adult; Humans; Adolescent; Circadian Rhythm; Shift Work Schedule; Asthma; Rhinitis, Allergic; Prevalence
PubMed: 38355588
DOI: 10.1186/s12916-024-03291-5