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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 -
NPJ Digital Medicine May 2024
PubMed: 38789723
DOI: 10.1038/s41746-024-01138-0 -
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 -
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 -
Cancers Feb 2024The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models... (Review)
Review
BACKGROUND
The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in non-invasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias.
METHODS
Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamous-cell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted.
RESULTS
A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI). The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87%. Accuracy was not measured in regard to AI based HEI interpretation.
CONCLUSION
AI algorithms exhibited an overall favorable performance in the diagnosis of nonmelanoma skin cancer via noninvasive imaging techniques. Ultimately, further research is needed to isolate pooled diagnostic accuracy for nonmelanoma skin cancers as many testing datasets also include melanoma and other pigmented lesions.
PubMed: 38339380
DOI: 10.3390/cancers16030629 -
Frontiers in Medicine 2023Skin cancer is one of the most common forms worldwide, with a significant increase in incidence over the last few decades. Early and accurate detection of this type of...
BACKGROUND
Skin cancer is one of the most common forms worldwide, with a significant increase in incidence over the last few decades. Early and accurate detection of this type of cancer can result in better prognoses and less invasive treatments for patients. With advances in Artificial Intelligence (AI), tools have emerged that can facilitate diagnosis and classify dermatological images, complementing traditional clinical assessments and being applicable where there is a shortage of specialists. Its adoption requires analysis of efficacy, safety, and ethical considerations, as well as considering the genetic and ethnic diversity of patients.
OBJECTIVE
The systematic review aims to examine research on the detection, classification, and assessment of skin cancer images in clinical settings.
METHODS
We conducted a systematic literature search on PubMed, Scopus, Embase, and Web of Science, encompassing studies published until April 4th, 2023. Study selection, data extraction, and critical appraisal were carried out by two independent reviewers. Results were subsequently presented through a narrative synthesis.
RESULTS
Through the search, 760 studies were identified in four databases, from which only 18 studies were selected, focusing on developing, implementing, and validating systems to detect, diagnose, and classify skin cancer in clinical settings. This review covers descriptive analysis, data scenarios, data processing and techniques, study results and perspectives, and physician diversity, accessibility, and participation.
CONCLUSION
The application of artificial intelligence in dermatology has the potential to revolutionize early detection of skin cancer. However, it is imperative to validate and collaborate with healthcare professionals to ensure its clinical effectiveness and safety.
PubMed: 38259845
DOI: 10.3389/fmed.2023.1305954 -
Journal of Clinical Medicine Dec 2023Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence... (Review)
Review
Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study's primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.
PubMed: 38202187
DOI: 10.3390/jcm13010180