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Zhongguo Dang Dai Er Ke Za Zhi =... Jun 2024To assess the effectiveness and safety of prone positioning in the treatment of neonatal respiratory distress syndrome (NRDS) using invasive respiratory support. (Randomized Controlled Trial)
Randomized Controlled Trial
OBJECTIVES
To assess the effectiveness and safety of prone positioning in the treatment of neonatal respiratory distress syndrome (NRDS) using invasive respiratory support.
METHODS
A prospective study was conducted from June 2020 to September 2023 at Suining County People's Hospital, involving 77 preterm infants with gestational ages less than 35 weeks requiring invasive respiratory support for NRDS. The infants were randomly divided into a supine group (37 infants) and a prone group (40 infants). Infants in the prone group were ventilated in the prone position for 6 hours followed by 2 hours in the supine position, continuing in this cycle until weaning from the ventilator. The effectiveness and safety of the two approaches were compared.
RESULTS
At 6 hours after enrollment, the prone group showed lower arterial blood carbon dioxide levels, inspired oxygen concentration, oxygenation index, rates of tracheal intubation bacterial colonization, and Neonatal Pain, Agitation and Sedation Scale scores compared to the supine group (<0.05). There were no significant differences between the groups in terms of pH, arterial oxygen pressure, positive end-expiratory pressure, duration of mechanical ventilation, accidental extubation, ventilator-associated pneumonia, air leak syndrome, skin pressure sores, feeding intolerance, and grades II-IV intraventricular hemorrhage (>0.05).
CONCLUSIONS
Compared to supine positioning, prone ventilation effectively improves oxygenation, increases comfort, and reduces tracheal intubation bacterial colonization in neonates requiring mechanical ventilation for NRDS, without significantly increasing adverse reactions.
Topics: Humans; Prone Position; Infant, Newborn; Respiratory Distress Syndrome, Newborn; Male; Female; Prospective Studies; Respiration, Artificial
PubMed: 38926379
DOI: 10.7499/j.issn.1008-8830.2312126 -
Clinics in Dermatology Jun 2024Non-melanoma skin cancers (NMSC) cancers are among the top five most common cancers globally. NMSC is an area with great potential for novel application of diagnostic...
Non-melanoma skin cancers (NMSC) cancers are among the top five most common cancers globally. NMSC is an area with great potential for novel application of diagnostic tools including artificial intelligence (AI). In this scoping review, we aimed to describe the applications of AI in diagnosis and treatment of NMSC. Twenty-nine publications described AI applications to dermatopathology including lesion classification and margin assessment. Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training datasets. Sixteen publications described use of AI in cutaneous surgery for NMSC including use in margin assessment during excisions and Mohs surgery, as well as predicting procedural complexity. Eleven publications discussed spectroscopy, confocal microscopy, and thermography and the AI algorithms that analyze and interpret their data. Ten publications pertained to AI application for discovery and utilization of NMSC biomarkers. Eight publications discussed the use of smart phones and AI, specifically how they enable clinicians and patients to have increased access to instant dermatological assessments but with varying accuracies. Five publications discussed large language models and NMSC, including how they may facilitate or hinder patient education and medical decision-making. Three publications pertained to skin of color and AI for NMSC discussed concerns regarding limited diverse datasets for training of CNNs. AI demonstrates tremendous potential to improve diagnosis, patient and clinician education, and management of NMSC. Despite excitement regarding AI, datasets are often not transparently reported, may include low quality images, and may not include diverse skin types, limiting generalizability. AI may serve as a tool to increase access to dermatology services for patients in rural areas and save healthcare dollars. These benefits can only be achieved, however, with consideration of potential ethical costs.
PubMed: 38925444
DOI: 10.1016/j.clindermatol.2024.06.016 -
Computers in Biology and Medicine Jun 2024Skin cancer (SC) significantly impacts many individuals' health all over the globe. Hence, it is imperative to promptly identify and diagnose such conditions at their...
Skin cancer (SC) significantly impacts many individuals' health all over the globe. Hence, it is imperative to promptly identify and diagnose such conditions at their earliest stages using dermoscopic imaging. Computer-aided diagnosis (CAD) methods relying on deep learning techniques especially convolutional neural networks (CNN) can effectively address this issue with outstanding outcomes. Nevertheless, such black box methodologies lead to a deficiency in confidence as dermatologists are incapable of comprehending and verifying the predictions that were made by these models. This article presents an advanced an explainable artificial intelligence (XAI) based CAD system named "Skin-CAD" which is utilized for the classification of dermoscopic photographs of SC. The system accurately categorises the photographs into two categories: benign or malignant, and further classifies them into seven subclasses of SC. Skin-CAD employs four CNNs of different topologies and deep layers. It gathers features out of a pair of deep layers of every CNN, particularly the final pooling and fully connected layers, rather than merely depending on attributes from a single deep layer. Skin-CAD applies the principal component analysis (PCA) dimensionality reduction approach to minimise the dimensions of pooling layer features. This also reduces the complexity of the training procedure compared to using deep features from a CNN that has a substantial size. Furthermore, it combines the reduced pooling features with the fully connected features of each CNN. Additionally, Skin-CAD integrates the dual-layer features of the four CNNs instead of entirely depending on the features of a single CNN architecture. In the end, it utilizes a feature selection step to determine the most important deep attributes. This helps to decrease the general size of the feature set and streamline the classification process. Predictions are analysed in more depth using the local interpretable model-agnostic explanations (LIME) approach. This method is used to create visual interpretations that align with an already existing viewpoint and adhere to recommended standards for general clarifications. Two benchmark datasets are employed to validate the efficiency of Skin-CAD which are the Skin Cancer: Malignant vs. Benign and HAM10000 datasets. The maximum accuracy achieved using Skin-CAD is 97.2 % and 96.5 % for the Skin Cancer: Malignant vs. Benign and HAM10000 datasets respectively. The findings of Skin-CAD demonstrate its potential to assist professional dermatologists in detecting and classifying SC precisely and quickly.
PubMed: 38925085
DOI: 10.1016/j.compbiomed.2024.108798 -
Advanced Science (Weinheim,... Jun 2024Tremendous popularity is observed for multifunctional flexible electronics with appealing applications in intelligent electronic skins, human-machine interfaces, and...
Tremendous popularity is observed for multifunctional flexible electronics with appealing applications in intelligent electronic skins, human-machine interfaces, and healthcare sensing. However, the reported sensing electronics, mostly can hardly provide ultrasensitive sensing sensitivity, wider sensing range, and robust cycling stability simultaneously, and are limited of efficient heat conduction out from the contacted skin interface after wearing flexible electronics on human skin to satisfy thermal comfort of human skin. Inspired from the ultrasensitive tactile perception microstructure (epidermis/spinosum/signal transmission) of human skin, a flexible comfortably wearable ultrasensitive electronics is hereby prepared from thermal conductive boron nitride nanosheets-incorporated polyurethane elastomer matrix with MXene nanosheets-coated surface microdomes as epidermis/spinosum layers assembled with interdigitated electrode as sensing signal transmission layer. It demonstrates appealing sensing performance with ultrasensitive sensitivity (≈288.95 kPa), up to 300 kPa sensing range, and up to 20 000 sensing cycles from obvious contact area variation between microdome microstructures and the contact electrode under external compression. Furthermore, the bioinspired electronics present advanced thermal management by timely efficient thermal dissipation out from the contacted skin surface to meet human skin thermal comfort with the incorporated thermal conductive boron nitride nanosheets. Thus, it is vitally promising in wearable artificial electronic skins, intelligent human-interactive sensing, and personal health management.
PubMed: 38924313
DOI: 10.1002/advs.202401800 -
ACS Sensors Jun 2024The concept of simulating external mechanical stimuli to generate luminescence has been a long-standing aspiration in real-time dynamic visualization. However, creating...
The concept of simulating external mechanical stimuli to generate luminescence has been a long-standing aspiration in real-time dynamic visualization. However, creating self-power and self-restoring mechanoluminescent electronic skins for artificial sensors poses significant challenges. In this study, we introduce a cutting-edge triboelectric-mechanoluminescent electronic skin (TMES) that exhibits a remarkable response to multiple external stimuli. This advancement is achieved by integrating a mechanoluminescent intermediate layer within a triboelectric nanogenerator (TENG). When pressure is applied to TMES, the maximum detection voltage can reach hundreds of volts and the maximum correlation sensitivity is 11.76 V/N. Moreover, we incorporate luminescence materials into mechanoluminescence layer, and the maximum absolute sensitivity can reach 1.41%. The device can not only distinguish between external stimuli such as pressing and bending but also continuously track external mechanical stimuli. A 4 × 4 matrix and motion prediction of 8 different postures were established to further demonstrate the significant advantages of the developed device in spatial detection. The versatility and performance of the TMES hint at its vast potential in areas such as human-computer interaction and wearable electronics, paving the way for more intuitive and dynamic technological interfaces.
PubMed: 38922626
DOI: 10.1021/acssensors.4c01061 -
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 -
Clinics in Dermatology Jun 2024Dermatologists treating patients with Autoimmune Bullous Dermatoses (AIBDs), as well as the patients themselves, encounter challenges at every stage of their...
Dermatologists treating patients with Autoimmune Bullous Dermatoses (AIBDs), as well as the patients themselves, encounter challenges at every stage of their interaction, including dermatological and comorbidities assessment, diagnosis, prognosis evaluation, treatment, and follow-up monitoring. We summarize the current and potential future clinical applications of artificial intelligence (AI) in the field of AIBDs. Recent research and AI models have demonstrated their potential to enhance or may already be contributing to advancements in every phase of the comprehensive diagnosis and personalized treatment process in AIBDs, providing patients, clinicians, and administrators with valuable support. Image recognition AI systems might assist precise clinical diagnoses of various diseases, including AIBDs, and could offer consistent and reliable scoring of disease severity. Automated and standardized AI-assisted laboratory methods could improve the accuracy and decrease the time and cost of gold-standard tests such as direct and indirect immunofluorescence. The studies and tools discussed in this article, although in the early stages, might be a small precursor to a transformative shift in the way we take care of patients with chronic skin diseases, including AIBDs.
PubMed: 38914175
DOI: 10.1016/j.clindermatol.2024.06.008 -
Clinics in Dermatology Jun 2024Artificial Intelligence (AI) has evolved to become a significant force in various domains, including medicine. We explore the role of AI in pathology, with a specific...
Artificial Intelligence (AI) has evolved to become a significant force in various domains, including medicine. We explore the role of AI in pathology, with a specific focus on dermatopathology and neoplastic dermatopathology. AI, encompassing Machine Learning (ML) and Deep Learning (DL), has demonstrated its potential in tasks ranging from diagnostic applications on Whole Slide Imaging (WSI) to predictive and prognostic functions in skin pathology. In dermatopathology, studies have assessed AI's ability to identify skin lesions, classify melanomas, and improve diagnostic accuracy. Results indicate that AI, particularly Convolutional Neural Networks (CNNs), can outperform human pathologists in terms of sensitivity and specificity. Moreover, AI aids in predicting disease outcomes, identifying aggressive tumors, and differentiating between various skin conditions. Neoplastic dermatopathology showcases AI's prowess in classifying melanocytic lesions, discriminating between melanomas and nevi, and aiding dermatopathologists in making accurate diagnoses. Studies emphasize the reproducibility and diagnostic aid that AI provides, especially in challenging cases. In inflammatory and lymphoproliferative dermatopathology, limited research exists, but studies show attempts to use AI to differentiate conditions like Mycosis Fungoides and eczema. While some results are promising, further exploration is needed in these areas. We highlight the extraordinary interest AI has garnered in the scientific community and its potential to assist clinicians and pathologists. Despite the advancements, we have stress edthe importance of collaboration between medical professionals, computer scientists, bioinformaticians, and engineers to harness AI's benefits while acknowledging its limitations and risks. The integration of AI into dermatopathology holds great promise, positioning it as a valuable tool rather than as a replacement for human expertise.
PubMed: 38909860
DOI: 10.1016/j.clindermatol.2024.06.010 -
Clinics in Dermatology Jun 2024Artificial intelligence (AI) has the potential to significantly impact many aspects of dermatology. The visual nature of dermatology lends itself to innovations in this...
Artificial intelligence (AI) has the potential to significantly impact many aspects of dermatology. The visual nature of dermatology lends itself to innovations in this space. The robustness of AI algorithms depends on the quality, quantity, and variety of data it is trained and tested on. Image collections can suffer from inconsistencies in image quality, underrepresentation of various anatomic sites and skin tones, and lack of benign counterparts leading to underperformance of algorithms in contexts other than one in which it is developed. Access to care, trust, rights, control, and transparency all play roles in the willingness of patients and healthcare providers and systems to collect, provide, and share data. Opportunities to improve data participation for the development of artificial intelligence include the establishment of data hubs and public algorithms, federated learning strategies, development of renumeration ecosystems for patients and systems, and development of criteria and mechanisms for transparency.
PubMed: 38909859
DOI: 10.1016/j.clindermatol.2024.06.013 -
Clinics in Dermatology Jun 2024Artificial intelligence (AI) has been steadily integrated into dermatology with AI platforms already attempting to identify skin cancers and diagnose benign versus...
Artificial intelligence (AI) has been steadily integrated into dermatology with AI platforms already attempting to identify skin cancers and diagnose benign versus malignant lesions. While not as widely known, AI programs have also been utilized as diagnostic and prognostic tools for dermatologic conditions with systemic or extracutaneous involvement, especially for diseases with autoimmune etiologies. We have provided a primer on commonly used AI platforms and practical applicability of these algorithms in dealing with psoriasis, systemic sclerosis, and dermatomyositis as a microcosm for future directions in the field. With a rapidly changing landscape in dermatology and medicine as a whole, AI could be a versatile tool to support clinicians and enhance access to care.
PubMed: 38909858
DOI: 10.1016/j.clindermatol.2024.06.019