-
The Australasian Journal of Dermatology Nov 2022Dupilumab-associated ocular surface disease (DAOSD) is of particular relevance in patients with atopic dermatitis (AD). Guidance on DAOSD assessment and management in... (Review)
Review
BACKGROUND/OBJECTIVES
Dupilumab-associated ocular surface disease (DAOSD) is of particular relevance in patients with atopic dermatitis (AD). Guidance on DAOSD assessment and management in the Australian setting is needed to reduce its impact and minimise disruption to treatment.
METHODS
A systematic review of the literature was undertaken to identify data pertaining to the incidence, pathophysiology, risk factors and management of DAOSD. A critical review of this literature was used to inform a decision framework for dupilumab-prescribers and develop a graded severity scoring tool to guide appropriate management options.
RESULTS
DAOSD typically emerges within 4 months of commencing dupilumab and the occurrence of new events diminishes over time. The reported incidence varies widely depending on the nature and source of the data: 8.6-22.1% (clinical trials programme), 0.5-70% (real-world data; differences in study size, duration of follow-up, ophthalmologist intervention, use of prophylaxis). Occurrence increases with AD severity and in patients with prior history of ocular disease; pathophysiology is still to be fully characterised. Management options have evolved over time and include lubricants/artificial tears, corticosteroids, calcineurin inhibitors, antihistamines, anti-inflammatory agents and antimicrobial agents. Current therapies aim to resolve symptoms or reduce severity to levels sufficiently tolerable to enable continuation of dupilumab therapy.
CONCLUSIONS
Recommendations for DAOSD assessment and management include identification of high-risk patients, vigilance for red flags (keratoconus, herpetic and bacterial keratitis), regular assessment of symptom severity (before and during dupilumab therapy), conservative management of mild DAOSD by the prescribing physician and ophthalmologist referral for collaborative care of moderate-severe DAOSD and high-risk patients.
Topics: Humans; Australia; Dermatitis, Atopic; Eye Diseases; Severity of Illness Index; Treatment Outcome
PubMed: 36125089
DOI: 10.1111/ajd.13924 -
The Cochrane Database of Systematic... Jul 2014Securing the endotracheal tube is a common procedure in the neonatal intensive care unit. Adequate fixation of the tube is essential to ensure effective ventilation of... (Review)
Review
BACKGROUND
Securing the endotracheal tube is a common procedure in the neonatal intensive care unit. Adequate fixation of the tube is essential to ensure effective ventilation of the infant whilst minimising potential complications secondary to the intervention. Methods used to secure the endotracheal tube often vary between units and sometimes even between healthcare providers in the same nursery.
OBJECTIVES
To compare the different methods of securing the endotracheal tube in the ventilated neonate and their effects on the risk of accidental extubation and other potential complications that can result from an unstable endotracheal tube.
SEARCH METHODS
A literature search of MEDLINE (from 1966 to June 2013), CINAHL (from 1982 to June 2013) and CENTRAL in The Cochrane Library was conducted to identify relevant trials to be analysed.
SELECTION CRITERIA
All randomised and quasi-randomised controlled trials of infants who were intubated for mechanical ventilation in a neonatal intensive care nursery where methods of stabilising the endotracheal tube were being compared.
DATA COLLECTION AND ANALYSIS
Data were collected from individual studies to determine the methods being compared, the methodology of the trial, and whether there were areas of bias that could significantly affect the results of the studies. In particular, studies were assessed for blinding of randomisation and allocation, blinding of the intervention, completeness of follow up, blinding of outcome assessments and selective reporting.
MAIN RESULTS
Five randomised controlled trials were identified and included for review. Accidental extubation was the most common outcome measured (five studies). None of the studies reported on the need for re-intubation or the rate of tube malposition, however one study did report on endotracheal tube slippage. A variety of other adverse effects were reported including mortality, incidence of perioral skin trauma and tube re-taping. All five studies were of poor methodological quality, small size, contained significant risks of bias and compared methods of securing the endotracheal tube that were too dissimilar for the data to be collated or included in a meta-analysis. We have not reported these further.
AUTHORS' CONCLUSIONS
This review highlighted the need for further well designed and completed studies to be conducted for this common neonatal procedure. Evidence is lacking to determine the most effective and safe method to stabilise the endotracheal tube in the ventilated neonate.
Topics: Equipment Safety; Humans; Infant, Newborn; Infant, Premature; Intensive Care, Neonatal; Intubation, Intratracheal; Randomized Controlled Trials as Topic; Respiration, Artificial
PubMed: 25079665
DOI: 10.1002/14651858.CD007805.pub2 -
Sensors (Basel, Switzerland) Jan 2022Due to its increasing incidence, skin cancer, and especially melanoma, is a serious health disease today. The high mortality rate associated with melanoma makes it... (Review)
Review
Due to its increasing incidence, skin cancer, and especially melanoma, is a serious health disease today. The high mortality rate associated with melanoma makes it necessary to detect the early stages to be treated urgently and properly. This is the reason why many researchers in this domain wanted to obtain accurate computer-aided diagnosis systems to assist in the early detection and diagnosis of such diseases. The paper presents a systematic review of recent advances in an area of increased interest for cancer prediction, with a focus on a comparative perspective of melanoma detection using artificial intelligence, especially neural network-based systems. Such structures can be considered intelligent support systems for dermatologists. Theoretical and applied contributions were investigated in the new development trends of multiple neural network architecture, based on decision fusion. The most representative articles covering the area of melanoma detection based on neural networks, published in journals and impact conferences, were investigated between 2015 and 2021, focusing on the interval 2018-2021 as new trends. Additionally presented are the main databases and trends in their use in teaching neural networks to detect melanomas. Finally, a research agenda was highlighted to advance the field towards the new trends.
Topics: Artificial Intelligence; Deep Learning; Humans; Melanoma; Neural Networks, Computer; Skin Neoplasms
PubMed: 35062458
DOI: 10.3390/s22020496 -
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 -
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 -
Dermatology (Basel, Switzerland) 2023While skin cancers are less prevalent in people with skin of color, they are more often diagnosed at later stages and have a poorer prognosis. The use of artificial...
BACKGROUND
While skin cancers are less prevalent in people with skin of color, they are more often diagnosed at later stages and have a poorer prognosis. The use of artificial intelligence (AI) models can potentially improve early detection of skin cancers; however, the lack of skin color diversity in training datasets may only widen the pre-existing racial discrepancies in dermatology.
OBJECTIVE
The aim of this study was to systematically review the technique, quality, accuracy, and implications of studies using AI models trained or tested in populations with skin of color for classification of pigmented skin lesions.
METHODS
PubMed was used to identify any studies describing AI models for classification of pigmented skin lesions. Only studies that used training datasets with at least 10% of images from people with skin of color were eligible. Outcomes on study population, design of AI model, accuracy, and quality of the studies were reviewed.
RESULTS
Twenty-two eligible articles were identified. The majority of studies were trained on datasets obtained from Chinese (7/22), Korean (5/22), and Japanese populations (3/22). Seven studies used diverse datasets containing Fitzpatrick skin type I-III in combination with at least 10% from black Americans, Native Americans, Pacific Islanders, or Fitzpatrick IV-VI. AI models producing binary outcomes (e.g., benign vs. malignant) reported an accuracy ranging from 70% to 99.7%. Accuracy of AI models reporting multiclass outcomes (e.g., specific lesion diagnosis) was lower, ranging from 43% to 93%. Reader studies, where dermatologists' classification is compared with AI model outcomes, reported similar accuracy in one study, higher AI accuracy in three studies, and higher clinician accuracy in two studies. A quality review revealed that dataset description and variety, benchmarking, public evaluation, and healthcare application were frequently not addressed.
CONCLUSIONS
While this review provides promising evidence of accurate AI models in populations with skin of color, the majority of the studies reviewed were obtained from East Asian populations and therefore provide insufficient evidence to comment on the overall accuracy of AI models for darker skin types. Large discrepancies remain in the number of AI models developed in populations with skin of color (particularly Fitzpatrick type IV-VI) compared with those of largely European ancestry. A lack of publicly available datasets from diverse populations is likely a contributing factor, as is the inadequate reporting of patient-level metadata relating to skin color in training datasets.
Topics: Humans; Artificial Intelligence; Melanoma; Sensitivity and Specificity; Skin Neoplasms; Skin Pigmentation; Racial Groups
PubMed: 36944317
DOI: 10.1159/000530225 -
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