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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 -
The British Journal of Dermatology Nov 2019Sunscreen use can prevent skin cancer, but there are concerns that it may increase the risk of vitamin D deficiency.
BACKGROUND
Sunscreen use can prevent skin cancer, but there are concerns that it may increase the risk of vitamin D deficiency.
OBJECTIVES
We aimed to review the literature to investigate associations between sunscreen use and vitamin D or 25 hydroxyvitamin D [25(OH)D] concentration.
METHODS
We systematically reviewed the literature following the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines. We identified manuscripts published in English between 1970 and 21 November 2017. Eligible studies were experimental [using an artificial ultraviolet radiation (UVR) source], field trials or observational studies. The results of each of the experimental studies and field trials are described in detail. Two authors extracted information from observational studies, and applied quality scoring criteria that were developed specifically for this question. These have been synthesized qualitatively.
RESULTS
We included four experimental studies, three field trials (two were randomized controlled trials) and 69 observational studies. In the experimental studies sunscreen use considerably abrogated the vitamin D or 25(OH)D production induced by exposure to artificially generated UVR. The randomized controlled field trials found no effect of daily sunscreen application, but the sunscreens used had moderate protection [sun protection factor SPF) ~16]. The observational studies mostly found no association or that self-reported sunscreen use was associated with higher 25(OH)D concentration.
CONCLUSIONS
There is little evidence that sunscreen decreases 25(OH)D concentration when used in real-life settings, suggesting that concerns about vitamin D should not negate skin cancer prevention advice. However, there have been no trials of the high-SPF sunscreens that are now widely recommended. What's already known about this topic? Previous experimental studies suggest that sunscreen can block vitamin D production in the skin but use artificially generated ultraviolet radiation with a spectral output unlike that seen in terrestrial sunlight. Nonsystematic reviews of observational studies suggest that use in real life does not cause vitamin D deficiency. What does this study add? This study systematically reviewed all experimental studies, field trials and observational studies for the first time. While the experimental studies support the theoretical risk that sunscreen use may affect vitamin D, the weight of evidence from field trials and observational studies suggests that the risk is low. We highlight the lack of adequate evidence regarding use of the very high sun protection factor sunscreens that are now recommended and widely used.
Topics: Administration, Cutaneous; Humans; Observational Studies as Topic; Randomized Controlled Trials as Topic; Risk Assessment; Self Report; Skin; Skin Neoplasms; Sun Protection Factor; Sunlight; Sunscreening Agents; Ultraviolet Rays; Vitamin D; Vitamin D Deficiency
PubMed: 30945275
DOI: 10.1111/bjd.17980 -
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 Dec 2021Cutaneous myiasis in patients with malignant wounds or skin cancer is a rare and undesirable event with limited epidemiological data. A subregister of reports, lack of...
BACKGROUND
Cutaneous myiasis in patients with malignant wounds or skin cancer is a rare and undesirable event with limited epidemiological data. A subregister of reports, lack of education in the population, inadequate empirical treatments, and medical underestimation are components of a public health problem that threatens patients' lives.
METHODS
We conducted a systematic review of the literature of cutaneous myiasis associated with malignant wounds and skin cancer, characterizing sociodemographic variables, risk factors, clinical and histological features, and treatment. Additionally, we present a demonstrative case with the adequate taxonomic evaluation.
DISCUSSION
Cutaneous myiasis is an underestimated and poorly managed infestation, which can generate severe complications in oncological patients. This is the first systematic review in the literature about this clinical scenario, which provides information to the physician and clinical researcher about the epidemiological gaps and what has been published so far.
CONCLUSIONS
Findings from the current review have helped to display the sociodemographic, epidemiological, and clinical behavior of myiasis in skin cancer and malignant wounds. Its contribution to the greater tumor tissue destruction is clear; however, more studies are required. The therapeutic management in these patients is equally clarified.
Topics: Humans; Myiasis; Risk Factors; Skin Neoplasms
PubMed: 34363696
DOI: 10.1111/ijd.15672 -
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 British Journal of Dermatology Sep 2009Dermoscopy improves diagnostic accuracy of the unaided eye for melanoma, and digital dermoscopy with artificial intelligence or computer diagnosis has also been shown... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Dermoscopy improves diagnostic accuracy of the unaided eye for melanoma, and digital dermoscopy with artificial intelligence or computer diagnosis has also been shown useful for the diagnosis of melanoma. At present there is no clear evidence regarding the diagnostic accuracy of dermoscopy compared with artificial intelligence.
OBJECTIVES
To evaluate the diagnostic accuracy of dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis and to compare the diagnostic accuracy of the different dermoscopic algorithms with each other and with digital dermoscopy/artificial intelligence for the detection of melanoma.
METHODS
A literature search on dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis was performed using several databases. Titles and abstracts of the retrieved articles were screened using a literature evaluation form. A quality assessment form was developed to assess the quality of the included studies. Heterogeneity among the studies was assessed. Pooled data were analysed using meta-analytical methods and comparisons between different algorithms were performed.
RESULTS
Of 765 articles retrieved, 30 studies were eligible for meta-analysis. Pooled sensitivity for artificial intelligence was slightly higher than for dermoscopy (91% vs. 88%; P = 0.076). Pooled specificity for dermoscopy was significantly better than artificial intelligence (86% vs. 79%; P < 0.001). Pooled diagnostic odds ratio was 51.5 for dermoscopy and 57.8 for artificial intelligence, which were not significantly different (P = 0.783). There were no significance differences in diagnostic odds ratio among the different dermoscopic diagnostic algorithms.
CONCLUSIONS
Dermoscopy and artificial intelligence performed equally well for diagnosis of melanocytic skin lesions. There was no significant difference in the diagnostic performance of various dermoscopy algorithms. The three-point checklist, the seven-point checklist and Menzies score had better diagnostic odds ratios than the others; however, these results need to be confirmed by a large-scale high-quality population-based study.
Topics: Algorithms; Dermoscopy; Humans; Image Processing, Computer-Assisted; Melanoma; Sensitivity and Specificity; Skin Neoplasms
PubMed: 19302072
DOI: 10.1111/j.1365-2133.2009.09093.x -
Journal of Cosmetic Dermatology Nov 2022Melanoma accounts for the majority of skin cancer deaths. Artificial intelligence has been applied in many types of cancers, and in melanoma in recent years. However, no...
BACKGROUND
Melanoma accounts for the majority of skin cancer deaths. Artificial intelligence has been applied in many types of cancers, and in melanoma in recent years. However, no systematic review summarized the application of artificial intelligence in melanoma.
AIMS
This study aims to systematically review previously published articles to explore the application of artificial intelligence in melanoma.
MATERIALS & METHODS
PubMed database was used to search the eligible publications on August 1, 2020. The query term was "artificial intelligence" and "melanoma."
RESULTS
A total of 51 articles were included in this review. Artificial intelligence technique is mainly used in the evaluation of dermoscopic images, other image segmentation and processing, and artificial intelligence diagnosis system.
DISCUSSION
Artificial intelligence is also applied in metastasis prediction, drug response prediction, and prognosis of melanoma. Besides, patients' perspectives of artificial intelligence and collaboration of human and artificial intelligence in melanoma also attracted attention. The query term might not include all articles, and we could not examine the algorithms that were built without publication.
CONCLUSION
The performance of artificial intelligence in melanoma is satisfactory and the future for potential applications is enormous.
Topics: Humans; Sensitivity and Specificity; Melanoma; Artificial Intelligence; Skin Neoplasms; Intelligence
PubMed: 36001057
DOI: 10.1111/jocd.15323 -
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 -
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 -
Wound Repair and Regeneration :... Jul 2016Skin substitutes are increasingly used in the treatment of various types of acute and chronic wounds. The aim of this study was to perform a systematic review and... (Meta-Analysis)
Meta-Analysis Review
Skin substitutes are increasingly used in the treatment of various types of acute and chronic wounds. The aim of this study was to perform a systematic review and meta-analysis to evaluate the effectiveness of skin substitutes on ulcer healing and limb salvage in the treatment of diabetic foot ulcers. Randomized clinical trials were searched and assessed following the methodology of The Cochrane Collaboration. We included 17 trials, totaling 1655 randomized participants. Risk of bias was variable among included trials. Thirteen trials compared the skin substitutes with standard care. The pooled results showed that that skin substitutes can, in addition to standard care, increase the likelihood of achieving complete ulcer closure compared with standard care alone after 6-16 weeks (risk ratio 1.55, 95% confidence interval [CI] 1.30-1.85). Four of the included trials compared two types of skin substitutes but no particular product showed a superior effect over another. Two trials reported on total incidence of lower limb amputations. Pooling the results of these two trials yielded a statistically significantly lower amputation rate among patients treated with skin substitutes (risk ratio 0.43, 95% CI 0.23-0.81), although the absolute risk difference was small (-0.06, 95% CI -0.10 to -0.01). This systematic review provides evidence that skin substitutes can, in addition to standard care, increase the likelihood of achieving complete ulcer closure compared with standard care alone in the treatment of diabetic foot ulcers. However, effectiveness on the long term, including lower limb salvage and recurrence, is currently lacking and cost-effectiveness is unclear.
Topics: Amputation, Surgical; Diabetic Foot; Humans; Limb Salvage; Randomized Controlled Trials as Topic; Skin, Artificial; Treatment Outcome; Wound Healing
PubMed: 27062201
DOI: 10.1111/wrr.12434