Domestic Violence

Development of a Deep Learning Model for Retinal Hemorrhage Detection on Head Computed Tomography in Young Children.

IMPORTANCE: Abusive head trauma (AHT) in children is often missed in medical encounters, and retinal hemorrhage (RH) is considered strong evidence for AHT. Although head computed tomography (CT) is obtained routinely, all but exceptionally large RHs are undetectable on CT images in children.

Large language models for structured reporting in radiology: performance of GPT-4, ChatGPT-3.5, Perplexity and Bing.

Structured reporting may improve the radiological workflow and communication among physicians. Artificial intelligence applications in medicine are growing fast. Large language models (LLMs) are recently gaining importance as valuable tools in radiology and are currently being tested for the critical task of structured reporting. We compared four LLMs models in terms of knowledge on structured rep...

Genomic and machine learning-based screening of aquaculture-associated introgression into at-risk wild North American Atlantic salmon (Salmo salar) populations.

The negative genetic impacts of gene flow from domestic to wild populations can be dependent on the degree of domestication and exacerbated by the mag...

Screening outcome for interpretation by the first and second reader in a population-based mammographic screening program with independent double reading.

BACKGROUND: Double reading of screening mammograms is associated with a higher rate of screen-detected cancer than single reading, but different strat...

A real-world evaluation of the implementation of NLP technology in abstract screening of a systematic review.

The laborious and time-consuming nature of systematic review production hinders the dissemination of up-to-date evidence synthesis. Well-performing na...

Opportunistic Screening: Scientific Expert Panel.

Radiologic tests often contain rich imaging data not relevant to the clinical indication. Opportunistic screening refers to the practice of systematic...

Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis.

Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essen...

Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images.

Deep learning technology has been used in the medical field to produce devices for clinical practice. Deep learning methods in cytology offer the pote...

Virtual Screening of Nrf2 Dietary-Derived Agonists and Safety by a New Deep-Learning Model and Verified and .

Nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is an essential regulatory target of antioxidants, but the lack of Nrf2 active site information has...

Effects of da Vinci robot versus thoracoscopic surgery on body trauma and lymphocyte subsets in lung cancer patients: A propensity score matching study.

BACKGROUND: To assess the impact of da Vinci robot versus thoracoscopic surgery on body trauma and lymphocyte subsets in lung cancer patients.

Henry gas solubility optimization double machine learning classifier for neurosurgical patients.

This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms...

Show Your Work: Responsible Model Reporting in Health Care Artificial Intelligence.

Standardized and thorough model reporting is an integral component in the development and deployment of machine learning models in health care. Model ...

Sociodemographic Variables Reporting in Human Radiology Artificial Intelligence Research.

PURPOSE: Artificial intelligence (AI) is rapidly reshaping how radiology is practiced. Its susceptibility to biases, however, is a primary concern as ...

CG02N Analyzer Accurately Measures Fibrinogen Level in Whole Blood in the Presence of Low- or High-Dose Heparin.

Rapid fibrinogen (Fbg) evaluation is important in patients with massive bleeding during severe trauma and those undergoing major surgery. However, the...

Artificial intelligence using a deep learning versus expert computed tomography human reading in calcium score and coronary artery calcium data and reporting system classification.

BACKGROUND: Artificial intelligence (AI) applied to cardiac imaging may provide improved processing, reading precision and advantages of automation. C...

The three-dimensional weakly supervised deep learning algorithm for traumatic splenic injury detection and sequential localization: an experimental study.

BACKGROUND: Splenic injury is the most common solid visceral injury in blunt abdominal trauma, and high-resolution abdominal computed tomography (CT) ...

Survey and Evaluation of Hypertension Machine Learning Research.

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in di...

Enabling Scientific Reproducibility through FAIR Data Management: An ontology-driven deep learning approach in the NeuroBridge Project.

Scientific reproducibility that effectively leverages existing study data is critical to the advancement of research in many disciplines including neu...

Artificial intelligence innovation in healthcare: Relevance of reporting guidelines for clinical translation from bench to bedside.

Artificial intelligence (AI) and digital innovation are transforming healthcare. Technologies such as machine learning in image analysis, natural lang...

Interstitial Lung Abnormalities at CT in the Korean National Lung Cancer Screening Program: Prevalence and Deep Learning-based Texture Analysis.

Background Interstitial lung abnormalities (ILAs) are associated with worse clinical outcomes, but ILA with lung cancer screening CT has not been quan...