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Predicting Agitation Events in the Emergency Department Through Artificial Intelligence.

JAMA network open
IMPORTANCE: Agitation events are increasing in emergency departments (EDs), exacerbating safety risks for patients and clinicians. A wide range of clinical etiologies and behavioral patterns in the emergency setting make agitation prediction difficul...

Anatomy-derived 3D Aortic Hemodynamics Using Fluid Physics-informed Deep Learning.

Radiology
Background Four-dimensional (4D) flow MRI provides assessment of thoracic aorta hemodynamic measures that are increasingly recognized as important biomarkers for risk assessment. However, long acquisition times and cumbersome data analysis limit wide...

Machine Learning Models for Predicting Pediatric Hospitalizations Due to Air Pollution and Humidity: A Retrospective Study.

Pediatric pulmonology
BACKGROUND: Exposure to air pollution and meteorological conditions, such as humidity, has been linked to adverse respiratory health outcomes in children. This study aims to develop predictive models for pediatric hospitalizations based on both envir...

Dual-stream algorithms for dementia detection: Harnessing structured and unstructured electronic health record data, a novel approach to prevalence estimation.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Identifying individuals with dementia is crucial for prevalence estimation and service planning, but reliable, scalable methods are lacking. We developed novel set algorithms using both structured and unstructured electronic health reco...

Exploratory Analysis of Nationwide Japanese Patient Safety Reports on Suicide and Suicide Attempts Among Inpatients With Cancer Using Large Language Models.

Psycho-oncology
OBJECTIVE: Patients with cancer have a high risk of suicide. However, evidence-based preventive measures remain unclear. This study aimed to investigate suicide prevention strategies for hospitalized patients with cancer by analyzing nationwide patie...

Does artificial intelligence redefine nuclear-to-cytoplasmic ratio threshold for diagnosing high-grade urothelial carcinoma?

Cancer cytopathology
BACKGROUND: The Paris System (TPS) introduced standardized nuclear-to-cytoplasmic (N/C) ratio thresholds for urine cytology to improve high-grade urothelial carcinoma (HGUC) detection, but these criteria remain subjective. This study used AIxURO, an ...

Evaluation of an Ambient Artificial Intelligence Documentation Platform for Clinicians.

JAMA network open
IMPORTANCE: The increase of electronic health record (EHR) work negatively impacts clinician well-being. One potential solution is incorporating an ambient artificial intelligence (AI) documentation platform.

A Novel Deep Learning-based Pathomics Score for Prognostic Stratification in Pancreatic Ductal Adenocarcinoma.

Pancreas
BACKGROUND AND OBJECTIVES: Accurate survival prediction for pancreatic ductal adenocarcinoma (PDAC) is crucial for personalized treatment strategies. This study aims to construct a novel pathomics indicator using hematoxylin and eosin-stained whole s...

Evaluating Automated Tools for Lesion Detection on F Fluoroestradiol PET/CT Images and Assessment of Concordance with Standard-of-Care Imaging in Metastatic Breast Cancer.

Radiology. Imaging cancer
Purpose To evaluate two automated tools for detecting lesions on fluorine 18 (F) fluoroestradiol (FES) PET/CT images and assess concordance of F-FES PET/CT with standard diagnostic CT and/or F fluorodeoxyglucose (FDG) PET/CT in patients with breast c...

Enhancing F-FDG PET image quality and lesion diagnostic performance across different body mass index using the deep progressive learning reconstruction algorithm.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: As body mass index (BMI) increases, the quality of 2-deoxy-2-[fluorine-18]fluoro-D-glucose (F-FDG) positron emission tomography (PET) images reconstructed with ordered subset expectation maximization (OSEM) declines, negatively impacting ...