Identifying and Reporting Dependent Adult abuse

Toward a rapid, sensitive, user-friendly, field-deployable artificial intelligence tool for enhancing African swine fever diagnosis and reporting.

OBJECTIVE: African swine fever (ASF) is a lethal and highly contagious transboundary animal disease with the potential for rapid international spread. Lateral flow assays (LFAs) are sometimes hard to read by the inexperienced user, mainly due to the LFA sensitivity and reading ambiguities. Our objective was to develop and implement an AI-powered tool to enhance the accuracy of LFA reading, thereby...

Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study.

BACKGROUND: The surge in artificial intelligence (AI) interventions in primary care trials lacks a study on reporting quality.

A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality.

BACKGROUND: An accurate prognostic tool is essential to aid clinical decision-making (e.g., patient triage) and to advance personalized medicine. Howe...

Machine learning or traditional statistical methods for predictive modelling in perioperative medicine: A narrative review.

Prediction of outcomes in perioperative medicine is key to decision-making and various prediction models have been created to help quantify and commun...

AI for glaucoma, Are we reporting well? a systematic literature review of DECIDE-AI checklist adherence.

BACKGROUND/OBJECTIVES: This systematic literature review examines the quality of early clinical evaluation of artificial intelligence (AI) decision su...

EMBANet: A flexible efficient multi-branch attention network.

Recent advances in the design of convolutional neural networks have shown that performance can be enhanced by improving the ability to represent multi...

Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography.

Developments in ambulatory electrocardiogram (ECG) technology have led to vast amounts of ECG data that currently need to be interpreted by human tech...

ADR-DQPU: A Novel ADR Signal Detection Using Deep Reinforcement and Positive-Unlabeled Learning.

The medical community has grappled with the challenge of analysis and early detection of severe and unknown adverse drug reactions (ADRs) from Spontan...

Spatial heterogeneity effect of built environment on traffic safety using geographically weighted atrous convolutions neural network.

The built environment exerts a significant influence on the frequency and severity of traffic accidents. Spatially uniform assumptions on the impacts ...

Network Analysis and Machine Learning for Signal Detection and Prioritization Using Electronic Healthcare Records and Administrative Databases: A Proof of Concept in Drug-Induced Acute Myocardial Infarction.

BACKGROUND: Safety signals for potential drug-induced adverse events (AEs) typically emerge from multiple data sources, primarily spontaneous reportin...

Around the EQUATOR With Clin-STAR: AI-Based Randomized Controlled Trial Challenges and Opportunities in Aging Research.

The CONSORT 2010 statement is a guideline that provides an evidence-based checklist of minimum reporting standards for randomized trials. With the rap...

FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training.

Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, parti...

Deep learning powered single-cell clustering framework with enhanced accuracy and stability.

Single-cell RNA sequencing (scRNA-seq) has revolutionized the field of cellular diversity research. Unsupervised clustering, a key technique in this e...

Pan-sharpening via Symmetric Multi-Scale Correction-Enhancement Transformers.

Pan-sharpening is a widely employed technique for enhancing the quality and accuracy of remote sensing images, particularly in high-resolution image d...

Detecting living microalgae in ship ballast water based on stained microscopic images and deep learning.

Motivated by the need of rapid detection of living microalgae cells in ship ballast water, this study is intended to determine the activities of micro...

Segmentation of coronary artery and calcification using prior knowledge based deep learning framework.

BACKGROUND: Computed tomography angiography (CTA) is used to screen for coronary artery calcification. As the coronary artery has complicated structur...

Multi-Branch CNN-LSTM Fusion Network-Driven System With BERT Semantic Evaluator for Radiology Reporting in Emergency Head CTs.

The high volume of emergency room patients often necessitates head CT examinations to rule out ischemic, hemorrhagic, or other organic pathologies. A ...

Empowering PET imaging reporting with retrieval-augmented large language models and reading reports database: a pilot single center study.

PURPOSE: The potential of Large Language Models (LLMs) in enhancing a variety of natural language tasks in clinical fields includes medical imaging re...

UNAGI: Unified neighbor-aware graph neural network for multi-view clustering.

Multi-view graph refining-based clustering (MGRC) methods aim to facilitate the clustering of data via Graph Neural Networks (GNNs) by learning optima...

MyEcoReporter: a prototype for artificial intelligence-facilitated pollution reporting.

BACKGROUND: Many chemical releases are first noticed by community members, but reporting these concerns often involves considerable hurdles. Artificia...