Identifying and Reporting Dependent Adult abuse

Diagnostic Accuracy of On-Premise Automated Coronary CT Angiography Analysis Based on Coronary Artery Disease Reporting and Data System 2.0.

Background Chest pain is a leading cause of outpatient and emergency department visits; advancements...

Evaluation of minor labial salivary gland focus score in Sjögren's disease using deep learning: a tool for more efficient diagnosis and future tissue biomarker discovery.

BACKGROUND: Sjögren's Disease (SjD) is histopathologically characterized by focal sialadenitis in mi...

Robotic task specific training for upper limb neurorehabilitation: a mixed methods feasibility trial reporting achievable dose.

PURPOSE: Robotic devices for upper-limb neurorehabilitation allow an increase in intensity of practi...

Enhanced CT and MRI Focal Bone Tumor Classification with Machine Learning-based Stratification: A Multicenter Retrospective Study.

Background Standardized bone tumor reporting is crucial for consistent, risk-aligned patient managem...

Requirements for AI Development and Reporting for MRI Prostate Cancer Detection in Biopsy-Naive Men: PI-RADS Steering Committee, Version 1.0.

This document defines the key considerations for developing and reporting an artificial intelligence...

Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.

Background Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, requiring swift ...

Accuracy of Large Language Model-based Automatic Calculation of Ovarian-Adnexal Reporting and Data System MRI Scores from Pelvic MRI Reports.

Background Ovarian-Adnexal Reporting and Data System (O-RADS) for MRI helps assign malignancy risk, ...

Value of Using a Generative AI Model in Chest Radiography Reporting: A Reader Study.

Background Multimodal generative artificial intelligence (AI) technologies can produce preliminary r...

Optimizing functional brain network analysis by incorporating nonlinear factors and frequency band selection with machine learning models.

The accurate assessment of the brain's functional network is seen as crucial for the understanding o...

Ovarian-adnexal reporting and data system MRI scoring: diagnostic accuracy, interobserver agreement, and applicability to machine learning.

OBJECTIVES: To evaluate the interobserver agreement and diagnostic accuracy of ovarian-adnexal repor...

Automatic large-scale political bias detection of news outlets.

Political bias is an inescapable characteristic in news and media reporting, and understanding what ...

[Risk prediction of Reduning Injection batches by near-infrared spectroscopy combined with multiple machine learning algorithms].

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial pr...

Machine learning-enabled risk prediction of self-neglect among community-dwelling older adults in China.

BACKGROUND: Elder self-neglect (ESN) is usually ignored as a private problem and impairs the health ...

[Interpretation of the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis based on regression or machine learning methods (TRIPOD+AI)].

TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns.

MOTIVATION: T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing a...

New horizons in prediction modelling using machine learning in older people's healthcare research.

Machine learning (ML) and prediction modelling have become increasingly influential in healthcare, p...

Reporting standards to support cost-effectiveness evaluations of AI-driven health care.

Towards a Reporting Guideline for Studies on Information Extraction from Clinical Texts.

BACKGROUND: The rapid technical progress in the domain of clinical Natural Language Processing and i...

Harnessing the Power of Machine Learning and Electronic Health Records to Support Child Abuse and Neglect Identification in Emergency Department Settings.

Emergency departments (EDs) are pivotal in detecting child abuse and neglect, but this task is often...

Using AI to Identify Unremarkable Chest Radiographs for Automatic Reporting.

Background Radiology practices have a high volume of unremarkable chest radiographs and artificial i...

CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution.

Predicting the strength of promoters and guiding their directed evolution is a crucial task in synth...