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Frontiers in Artificial Intelligence 2023Detecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used...
INTRODUCTION
Detecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used to identify and study skin cancer, but the blurred boundaries between lesions and besieging tissues can lead to incorrect identification. Artificial Intelligence (AI) models, including vision transformers, have been proposed as a solution, but variations in symptoms and underlying effects hinder their performance.
OBJECTIVE
This scoping review synthesizes and analyzes the literature that uses vision transformers for skin lesion detection.
METHODS
The review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Revise) guidelines. The review searched online repositories such as IEEE Xplore, Scopus, Google Scholar, and PubMed to retrieve relevant articles. After screening and pre-processing, 28 studies that fulfilled the inclusion criteria were included.
RESULTS AND DISCUSSIONS
The review found that the use of vision transformers for skin cancer detection has rapidly increased from 2020 to 2022 and has shown outstanding performance for skin cancer detection using dermoscopy images. Along with highlighting intrinsic visual ambiguities, irregular skin lesion shapes, and many other unwanted challenges, the review also discusses the key problems that obfuscate the trustworthiness of vision transformers in skin cancer diagnosis. This review provides new insights for practitioners and researchers to understand the current state of knowledge in this specialized research domain and outlines the best segmentation techniques to identify accurate lesion boundaries and perform melanoma diagnosis. These findings will ultimately assist practitioners and researchers in making more authentic decisions promptly.
PubMed: 37529760
DOI: 10.3389/frai.2023.1202990 -
Sensors (Basel, Switzerland) Nov 2022This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles... (Review)
Review
This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.
Topics: Galvanic Skin Response; Machine Learning; Arousal; Neural Networks, Computer; Support Vector Machine
PubMed: 36433482
DOI: 10.3390/s22228886 -
Frontiers in Oncology 2023Artificial intelligence (AI), with its potential to diagnose skin cancer, has the potential to revolutionize future medical and dermatological practices. However, the...
OBJECTIVE
Artificial intelligence (AI), with its potential to diagnose skin cancer, has the potential to revolutionize future medical and dermatological practices. However, the current knowledge regarding the utilization of AI in skin cancer diagnosis remains somewhat limited, necessitating further research. This study employs visual bibliometric analysis to consolidate and present insights into the evolution and deployment of AI in the context of skin cancer. Through this analysis, we aim to shed light on the research developments, focal areas of interest, and emerging trends within AI and its application to skin cancer diagnosis.
METHODS
On July 14, 2023, articles and reviews about the application of AI in skin cancer, spanning the years from 1900 to 2023, were selected from the Web of Science Core Collection. Co-authorship, co-citation, and co-occurrence analyses of countries, institutions, authors, references, and keywords within this field were conducted using a combination of tools, including CiteSpace V (version 6.2. R3), VOSviewer (version 1.6.18), SCImago, Microsoft Excel 2019, and R 4.2.3.
RESULTS
A total of 512 papers matching the search terms and inclusion/exclusion criteria were published between 1991 and 2023. The United States leads in publications with 149, followed by India with 61. Germany holds eight positions among the top 10 institutions, while the United States has two. The most prevalent journals cited were , the , and . The most frequently cited keywords include "skin cancer", "classification", "artificial intelligence", and "deep learning".
CONCLUSIONS
Research into the application of AI in skin cancer is rapidly expanding, and an increasing number of scholars are dedicating their efforts to this field. With the advancement of AI technology, new opportunities have arisen to enhance the accuracy of skin imaging diagnosis, treatment based on big data, and prognosis prediction. However, at present, the majority of AI research in the field of skin cancer diagnosis is still in the feasibility study stage. It has not yet made significant progress toward practical implementation in clinical settings. To make substantial strides in this field, there is a need to enhance collaboration between countries and institutions. Despite the potential benefits of AI in skin cancer research, numerous challenges remain to be addressed, including developing robust algorithms, resolving data quality issues, and enhancing results interpretability. Consequently, sustained efforts are essential to surmount these obstacles and facilitate the practical application of AI in skin cancer research.
PubMed: 37901316
DOI: 10.3389/fonc.2023.1222426 -
Disability and Rehabilitation May 2006Skin problems of the stump in lower limb amputees are relative common in daily rehabilitation practice, possibly impeding prosthetic use. This impediment may have great... (Review)
Review
PURPOSE
Skin problems of the stump in lower limb amputees are relative common in daily rehabilitation practice, possibly impeding prosthetic use. This impediment may have great impact in daily life. Our objective was to review literature systematically concerning incidence and prevalence of skin disorders of the stump in lower limb amputees.
METHOD
A literature search was performed in several medical databases (MEDLINE, CINAHL, EMBASE, RECAL) using database specific search strategies. Reference lists in the identified publications were used as threads for retrieving more publications missed in the searches. Only clinical studies and patient surveys were eligible for further assessment.
RESULTS
545 publications were initially found. After selection, 28 publications were assessed for research methodology. Only one publication fulfilled the selection criteria. The prevalence of skin problems in a series of 45 lower leg amputees of 65 years and older was 16%.
CONCLUSIONS
Prevalence and incidence of skin problems of the stump in lower limb amputees are mainly unknown.
Topics: Amputation Stumps; Artificial Limbs; Humans; Incidence; Leg; Prevalence; Skin Diseases
PubMed: 16690571
DOI: 10.1080/09638280500277032 -
Sensors (Basel, Switzerland) Jul 2023Inertial measurement units (IMUs) have become the mainstay in human motion evaluation outside of the laboratory; however, quantification of 3-dimensional upper limb... (Review)
Review
Inertial measurement units (IMUs) have become the mainstay in human motion evaluation outside of the laboratory; however, quantification of 3-dimensional upper limb motion using IMUs remains challenging. The objective of this systematic review is twofold. Firstly, to evaluate computational methods used to convert IMU data to joint angles in the upper limb, including for the scapulothoracic, humerothoracic, glenohumeral, and elbow joints; and secondly, to quantify the accuracy of these approaches when compared to optoelectronic motion analysis. Fifty-two studies were included. Maximum joint motion measurement accuracy from IMUs was achieved using Euler angle decomposition and Kalman-based filters. This resulted in differences between IMU and optoelectronic motion analysis of 4° across all degrees of freedom of humerothoracic movement. Higher accuracy has been achieved at the elbow joint with functional joint axis calibration tasks and the use of kinematic constraints on gyroscope data, resulting in RMS errors between IMU and optoelectronic motion for flexion-extension as low as 2°. For the glenohumeral joint, 3D joint motion has been described with RMS errors of 6° and higher. In contrast, scapulothoracic joint motion tracking yielded RMS errors in excess of 10° in the protraction-retraction and anterior-posterior tilt direction. The findings of this study demonstrate high-quality 3D humerothoracic and elbow joint motion measurement capability using IMUs and underscore the challenges of skin motion artifacts in scapulothoracic and glenohumeral joint motion analysis. Future studies ought to implement functional joint axis calibrations, and IMU-based scapula locators to address skin motion artifacts at the scapula, and explore the use of artificial neural networks and data-driven approaches to directly convert IMU data to joint angles.
Topics: Humans; Range of Motion, Articular; Shoulder Joint; Upper Extremity; Motion; Movement; Biomechanical Phenomena
PubMed: 37514829
DOI: 10.3390/s23146535 -
Medicina (Kaunas, Lithuania) Apr 2021: Porcine xenografts have been used successfully in partial thickness burn treatment for many years. Their disappearance from the market led to the search for effective... (Review)
Review
: Porcine xenografts have been used successfully in partial thickness burn treatment for many years. Their disappearance from the market led to the search for effective and efficient alternatives. In this article, we examine the synthetic epidermal skin substitute Suprathel as a substitute in the treatment of partial thickness burns. : A systematic review following the PRISMA guidelines has been performed. Sixteen Suprathel and 12 porcine xenograft studies could be included. Advantages and disadvantages between the treatments and the studies' primary endpoints have been investigated qualitatively and quantitatively. : Although Suprathel had a nearly six times larger TBSA in their studies ( < 0.001), it showed a significantly lower necessity for skin grafts ( < 0.001), and we found a significantly lower infection rate ( < 0.001) than in Porcine Xenografts. Nonetheless, no significant differences in the healing time ( = 0.67) and the number of dressing changes until complete wound healing ( = 0.139) could be found. Both products reduced pain to various degrees with the impression of a better performance of Suprathel on a qualitative level. Porcine xenograft was not recommended for donor sites or coverage of sheet-transplanted keratinocytes, while Suprathel was used successfully in both indications. : The investigated parameters indicate that Suprathel to be an effective replacement for porcine xenografts with even lower subsequent treatment rates. Suprathel appears to be usable in an extended range of indications compared to porcine xenograft. Data heterogeneity limited conclusions from the results.
Topics: Animals; Burns; Heterografts; Skin Transplantation; Skin, Artificial; Swine; Wound Healing
PubMed: 33946298
DOI: 10.3390/medicina57050432 -
Journal of Clinical Medicine Sep 2023Otolaryngological diagnoses, such as otitis media, are traditionally performed using endoscopy, wherein diagnostic accuracy can be subjective and vary among clinicians.... (Review)
Review
Otolaryngological diagnoses, such as otitis media, are traditionally performed using endoscopy, wherein diagnostic accuracy can be subjective and vary among clinicians. The integration of objective tools, like artificial intelligence (AI), could potentially improve the diagnostic process by minimizing the influence of subjective biases and variability. We systematically reviewed the AI techniques using medical imaging in otolaryngology. Relevant studies related to AI-assisted otitis media diagnosis were extracted from five databases: Google Scholar, PubMed, Medline, Embase, and IEEE Xplore, without date restrictions. Publications that did not relate to AI and otitis media diagnosis or did not utilize medical imaging were excluded. Of the 32identified studies, 26 used tympanic membrane images for classification, achieving an average diagnosis accuracy of 86% (range: 48.7-99.16%). Another three studies employed both segmentation and classification techniques, reporting an average diagnosis accuracy of 90.8% (range: 88.06-93.9%). These findings suggest that AI technologies hold promise for improving otitis media diagnosis, offering benefits for telemedicine and primary care settings due to their high diagnostic accuracy. However, to ensure patient safety and optimal outcomes, further improvements in diagnostic performance are necessary.
PubMed: 37762772
DOI: 10.3390/jcm12185831 -
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 -
JID Innovations : Skin Science From... Jan 2023Artificial intelligence (AI) has recently made great advances in image classification and malignancy prediction in the field of dermatology. However, understanding the... (Review)
Review
Artificial intelligence (AI) has recently made great advances in image classification and malignancy prediction in the field of dermatology. However, understanding the applicability of AI in clinical dermatology practice remains challenging owing to the variability of models, image data, database characteristics, and variable outcome metrics. This systematic review aims to provide a comprehensive overview of dermatology literature using convolutional neural networks. Furthermore, the review summarizes the current landscape of image datasets, transfer learning approaches, challenges, and limitations within current AI literature and current regulatory pathways for approval of models as clinical decision support tools.
PubMed: 36655135
DOI: 10.1016/j.xjidi.2022.100150 -
European Journal of Cancer (Oxford,... Oct 2021Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the...
BACKGROUND
Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice.
OBJECTIVE
The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians.
METHODS
PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma. The search terms skin cancer classification, deep learning, convolutional neural network (CNN), melanoma (detection), digital biomarkers, histopathology and whole slide imaging were combined. Based on the search results, only studies that considered direct comparison of AI results with clinicians and had a diagnostic classification as their main objective were included.
RESULTS
A total of 19 reader studies fulfilled the inclusion criteria. Of these, 11 CNN-based approaches addressed the classification of dermoscopic images; 6 concentrated on the classification of clinical images, whereas 2 dermatopathological studies utilised digitised histopathological whole slide images.
CONCLUSIONS
All 19 included studies demonstrated superior or at least equivalent performance of CNN-based classifiers compared with clinicians. However, almost all studies were conducted in highly artificial settings based exclusively on single images of the suspicious lesions. Moreover, test sets mainly consisted of holdout images and did not represent the full range of patient populations and melanoma subtypes encountered in clinical practice.
Topics: Automation; Biopsy; Clinical Competence; Deep Learning; Dermatologists; Dermoscopy; Diagnosis, Computer-Assisted; Humans; Image Interpretation, Computer-Assisted; Melanoma; Microscopy; Neural Networks, Computer; Pathologists; Predictive Value of Tests; Reproducibility of Results; Skin Neoplasms
PubMed: 34509059
DOI: 10.1016/j.ejca.2021.06.049