Radiology

Diagnostic Radiology

Latest AI and machine learning research in diagnostic radiology for healthcare professionals.

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Natural Language Processing Techniques for Extracting and Categorizing Finding Measurements in Narrative Radiology Reports.

BACKGROUND: Accumulating quantitative outcome parameters may contribute to constructing a healthcare...

Pattern recognition for cache management in distributed medical imaging environments.

PURPOSE: Traditionally, medical imaging repositories have been supported by indoor infrastructures w...

A Bayesian approach to distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging.

The tongue is a critical organ for a variety of functions, including swallowing, respiration, and sp...

Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging.

BACKGROUND: The purpose of this study was to apply an artificial neural network (ANN) in patients wi...

Automatic abstraction of imaging observations with their characteristics from mammography reports.

BACKGROUND: Radiology reports are usually narrative, unstructured text, a format which hinders the a...

Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model.

Identifying early-onset schizophrenia spectrum disorders (SSD) at a very early stage remains challen...

Visual-language foundation models in medical imaging: A systematic review and meta-analysis of diagnostic and analytical applications.

BACKGROUND AND OBJECTIVE: Visual-language foundation models (VLMs) have garnered attention for their...

Digital twins in radiology: A systematic review of applications, challenges, and future perspectives.

BACKGROUND: Digital twins (DTs) represent a transformative advancement in radiology, integrating mul...

Causal insights from clinical information in radiology: Enhancing future multimodal AI development.

PURPOSE: This study investigates the causal mechanisms underlying radiology report generation by ana...

Large models in medical imaging: Advances and prospects.

Recent advances in large models demonstrate significant prospects for transforming the field of medi...

Artificial Intelligence-Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multimodality.

The integration of artificial intelligence (AI) in cancer research has significantly advanced radiol...

Ultimate focus: applications of the Churchill Method in radiology.

The Churchill Method evolved as an approach to shooting sporting clays; essentially, successfully sh...

A review of explainable AI techniques and their evaluation in mammography for breast cancer screening.

Explainable AI (XAI) methods are gaining prominence in medical imaging, addressing the critical need...

The Evolution of Radiology Image Annotation in the Era of Large Language Models.

Although there are relatively few diverse, high-quality medical imaging datasets on which to train c...

[Analysis of the global competitive landscape in artificial intelligence medical device research].

The objective of this study is to map the global scientific competitive landscape in the field of ar...

Exploring therapeutic and diagnostic potential of cysteine cathepsin as targets for cancer therapy with nanomedicine.

Cysteine cathepsins have been discovered to be substantially expressed in multiple types of cancer. ...

Exploring interpretable echo analysis using self-supervised parcels.

The application of AI for predicting critical heart failure endpoints using echocardiography is a pr...

Multimodal Diagnostic Approach for Osteosarcoma and Bone Callus Using Hyperspectral Imaging and Deep Learning.

Distinguishing osteosarcoma from bone callus remains a clinical challenge due to their morphological...

Cardiac imaging for the detection of ischemia: current status and future perspectives.

INTRODUCTION: Coronary artery disease is the main cause of mortality worldwide mandating early detec...

DeepValve: The first automatic detection pipeline for the mitral valve in Cardiac Magnetic Resonance imaging.

Mitral valve (MV) assessment is key to diagnosing valvular disease and to addressing its serious dow...

Deep learning-driven multi-class classification of brain strokes using computed tomography: A step towards enhanced diagnostic precision.

OBJECTIVE: To develop and validate deep learning models leveraging CT imaging for the prediction and...

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