Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review.

Vitiligo, alopecia areata, atopic, and stasis dermatitis are common skin conditions that pose diagnostic and assessment challenges. Skin image analysis is a promising noninvasive approach for objective and automated detection as well as quantitative assessment of skin diseases. This review provides a systematic literature search regarding the analysis of computer vision techniques applied to these benign skin conditions, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Deep learning for cardiac imaging: focus on myocardial diseases, a narrative review.

The integration of computational technologies into cardiology has significantly advanced the diagnosis and management of cardiovascular diseases. Computational cardiology, particularly, through cardiovascular imaging and informatics, enables a precise diagnosis of myocardial diseases utilizing techniques such as echocardiography, cardiac magnetic resonance imaging, and computed tomography. Early-stage disease classification, especially in asymptomatic patients, benefits from these advancements, potentially altering disease progression and improving patient outcomes.

Efficiency paradox: Introducing secure messaging in outpatient care.

This study explores the uptake of secure messaging technology and the influence it has on the work of three interconnected occupational roles. It was executed in an outpatient clinic, where the administrative staff adopted the new secure messaging tool, expecting it to improve efficiency. The study found that the efficiency-enhancing tool over time introduced new inefficiencies into the work of administrative assistants, nurses, and physicians, and this phenomenon is noted as the efficiency paradox, which is examined and explained throughout the study.

An explainable deep learning approach for stock market trend prediction.

Given the intricate nature of stock forecasting as well as the inherent risks and uncertainties, analysis of market trends is necessary to capitalize on optimal investment opportunities for profit maximization and timely disinvestment for loss minimization. In this work, we propose a deep learning model for predicting five distinct stock market trends: upward, downward, double top, rounded bottom, and rounded top. The proposed model surpasses common benchmarks, including support vector machine, random forest, and logistic regression, achieving an average accuracy of 94.

Establishing the Foundations of Emotional Intelligence in Care Companion Robots to Mitigate Agitation Among High-Risk Patients With Dementia: Protocol for an Empathetic Patient-Robot Interaction Study.

Background: An estimated 6.7 million persons are living with dementia in the United States, a number expected to double by 2060. Persons experiencing moderate to severe dementia are 4 to 5 times more likely to fall than those without dementia, due to agitation and unsteady gait.

Multimodal automatic assessment of acute pain through facial videos and heart rate signals utilizing transformer-based architectures.

Accurate and objective pain evaluation is crucial in developing effective pain management protocols, aiming to alleviate distress and prevent patients from experiencing decreased functionality. A multimodal automatic assessment framework for acute pain utilizing video and heart rate signals is introduced in this study. The proposed framework comprises four pivotal modules: the , responsible for extracting embeddings from videos; the , tasked with mapping heart rate signals into a higher dimensional space; the , designed to create learning-based augmentations in the latent space; and the , which utilizes the extracted video and heart rate embeddings for the final assessment.

Impacts of Climate Change on the Biogeography and Ecological Structure of Hand.-Mazz. in China.

This study utilized the platform for ensemble forecasting of species distributions, biomod2, to predict and quantitatively analyze the distribution changes of Hand.-Mazz. under different climate scenarios (SSP1-2.

SCANeXt: Enhancing 3D medical image segmentation with dual attention network and depth-wise convolution.

Existing approaches to 3D medical image segmentation can be generally categorized into convolution-based or transformer-based methods. While convolutional neural networks (CNNs) demonstrate proficiency in extracting local features, they encounter challenges in capturing global representations. In contrast, the consecutive self-attention modules present in vision transformers excel at capturing long-range dependencies and achieving an expanded receptive field.

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.

Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers.

Acute NO Stress Shortens the Median Survival Period of 'Elizabeth Angus' by Disrupting Tissue Structure and Photosynthetic Response Centers.

The air pollutant NO is one of the major constraints on plant growth, and the ecological value of the ornamental plant can be weakened by NO. In this study, an indoor 4 μL·L NO simulated fumigation test was conducted with three treatments, CK (normal growth with clean air), T1 (4 μL·L NO + 8 h/d), and T2 (4 μL·L NO + 24 h/d), which were set up with considerations for time and concentration. The results demonstrated that most of the morphological parameters of 'Elizabeth Angus', except for the floral organs, were decreased in the root, stem, leaf, and bract.

Exogenous Salicylic Acid Alleviates NO Damage by Maintaining Cell Stability and Physiological Metabolism in 'Miss Manila' Seedlings.

Exogenous substances can alleviate plant damage under adverse conditions. In order to explore whether different concentrations of salicylic acid (SA) can play a role in the resistance of × 'Miss Manila' to nitrogen dioxide (NO) stress and the relevant mechanisms of their effects, different concentrations of SA were applied locally under the control experiment condition of 4.0 μL·L NO, and the role of SA in alleviating injury was studied.

Combined Transcriptome and Proteome Analysis Provides Insights into Petaloidy in Pomegranate.

Petaloidy leads to a plump floral pattern and increases the landscape value of ornamental pomegranates; however, research on the mechanism of petaloidy in ornamental pomegranates is limited. In this study, we aimed to screen candidate genes related to petaloidy. We performed transcriptomic and proteomic sequencing of the stamens and petals of single-petal and double-petal flowers of ornamental pomegranates.

Enablers and inhibitors of digital startup evolution: a multi-case study of Swedish business incubators.

Global advances in digital technology are facilitating corresponding rises in digital entrepreneurship and its startup manifestation. There are many uncertainties on the road to digital startup evolution, some of which may be successfully navigated with the assistance of business incubators. While these organisations provide valuable guidance and support to the startup community, their efforts are at least partly constrained by the lack of a consistent, coherent roadmap to guide both them and their incubatees.

From 'making up' professionals to epistemic colonialism: Digital health platforms in the Global South.

Platforms have been studied in terms of their impact on knowledge production and generation of social value. Little however is known about the significance of the knowledge they transfer to the recipient communities-often in faraway countries of the Global South-or its potential perceived colonizing effects. Our study explores the question around digital epistemic colonialism in the context of health platforms involved in knowledge transfer.

Research on Smart Tourism Oriented Sensor Network Construction and Information Service Mode.

Smart tourism is the latest achievement of tourism development at home and abroad. It is also an essential part of the smart city. Promoting the application of computer and sensor technology in smart tourism is conducive to improving the efficiency of public tourism services and guiding the innovation of the tourism public service mode.

China's Gridded Manufacturing Dataset.

The growth of the manufacturing industry is the engine of rapid economic growth in developing regions. Characterizing the geographical distribution of manufacturing firms is critically important for scientists and policymakers. However, data on the manufacturing industry used in previous studies either have a low spatial resolution (or fuzzy classification) or high-resolution information is lacking.

How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow? A technography study in 2021.

Objectives: How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow?

Methods: We systematically analyze 393 AI applications developed for supporting diagnostic radiology workflow. We collected qualitative and quantitative data by analyzing around 1250 pages of documents retrieved from companies' websites and legal documents. Five investigators read and interpreted collected data, extracted the features and functionalities of the AI applications, and finally entered them into an excel file for identifying the patterns.

Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE).

Proteomics studies have shown differential expression of numerous proteins in dementias but have rarely led to novel biomarker tests for clinical use. The Marie Curie MIRIADE project is designed to experimentally evaluate development strategies to accelerate the validation and ultimate implementation of novel biomarkers in clinical practice, using proteomics-based biomarker development for main dementias as experimental case studies. We address several knowledge gaps that have been identified in the field.

FDG PET/CT radiomics as a tool to differentiate between reactive axillary lymphadenopathy following COVID-19 vaccination and metastatic breast cancer axillary lymphadenopathy: a pilot study.

Objectives: To evaluate if radiomics with machine learning can differentiate between F-18-fluorodeoxyglucose (FDG)-avid breast cancer metastatic lymphadenopathy and FDG-avid COVID-19 mRNA vaccine-related axillary lymphadenopathy.

Materials And Methods: We retrospectively analyzed FDG-positive, pathology-proven, metastatic axillary lymph nodes in 53 breast cancer patients who had PET/CT for follow-up or staging, and FDG-positive axillary lymph nodes in 46 patients who were vaccinated with the COVID-19 mRNA vaccine. Radiomics features (110 features classified into 7 groups) were extracted from all segmented lymph nodes.

Brains exposed: How new imaging technology reconfigures expertise coordination in neurosurgery.

This study explores the consequences of the uptake of novel medical technology for expertise coordination among specialists. Building on a 40-month field study, we show how the introduction of iMRI triggered a significant change in temporal, interactional and role dimensions of coordination. We trace the origin of those changes to the novel role the images started to play in surgery: iMRI generated new forms of visibility of the brain (offering real-time, more precise imaging) and new forms of ambiguity for clinical action (representing the brain undergoing manipulation).

Radiologists in the loop: the roles of radiologists in the development of AI applications.

Objectives: To examine the various roles of radiologists in different steps of developing artificial intelligence (AI) applications.

Materials And Methods: Through the case study of eight companies active in developing AI applications for radiology, in different regions (Europe, Asia, and North America), we conducted 17 semi-structured interviews and collected data from documents. Based on systematic thematic analysis, we identified various roles of radiologists.

Training opportunities of artificial intelligence (AI) in radiology: a systematic review.

Objectives: The aim is to offer an overview of the existing training programs and critically examine them and suggest avenues for further development of AI training programs for radiologists.

Methods: Deductive thematic analysis of 100 training programs offered in 2019 and 2020 (until June 30). We analyze the public data about the training programs based on their "contents," "target audience," "instructors and offering agents," and "legitimization strategies.