Radiology

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

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Leveraging Large Language Models in Radiology Research: A Comprehensive User Guide.

Large Language Models (LLMs) such as ChatGPT have been increasingly integrated into radiology resear...

Machine learning models based on FEM simulation of hoop mode vibrations to enable ultrasonic cuffless measurement of blood pressure.

Blood pressure (BP) is one of the vital physiological parameters, and its measurement is done routin...

DIFLF: A domain-invariant features learning framework for single-source domain generalization in mammogram classification.

BACKGROUND AND OBJECTIVE: Single-source domain generalization (SSDG) aims to generalize a deep learn...

Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning.

Self-supervised learning (SSL) has gained attention in the medical field as a deep learning approach...

Clinicians' perspectives on the use of artificial intelligence to triage MRI brain scans.

Artificial intelligence (AI) tools can triage radiology scans to streamline the patient pathway and ...

Using deep learning to shorten the acquisition time of brain MRI in acute ischemic stroke: Synthetic T2W images generated from b0 images.

OBJECTIVE: This study aimed to assess the feasibility of the deep learning in generating T2 weighted...

Advances in physiological and clinical relevance of hiPSC-derived brain models for precision medicine pipelines.

Precision, or personalized, medicine aims to stratify patients based on variable pathogenic signatur...

Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases.

OBJECTIVE: To develop a machine learning-based clinical and/or radiomics model for predicting the pr...

MRI to digital medicine diagnosis: integrating deep learning into clinical decision-making for lumbar degenerative diseases.

INTRODUCTION: To develop an intelligent system based on artificial intelligence (AI) deep learning a...

Automated ultrasonography of hepatocellular carcinoma using discrete wavelet transform based deep-learning neural network.

This study introduces HCC-Net, a novel wavelet-based approach for the accurate diagnosis of hepatoce...

Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging.

Cardiovascular diseases (CVD) can be diagnosed using various diagnostic modalities. The electrocardi...

DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.

In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusi...

Machine learning-based interpretation of non-contrast feature tracking strain analysis and T1/T2 mapping for assessing myocardial viability.

Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium...

Leveraging Transformers-based models and linked data for deep phenotyping in radiology.

BACKGROUND AND OBJECTIVE: Despite significant investments in the normalization and the standardizati...

Deep learning-based pelvimetry in pelvic MRI volumes for pre-operative difficulty assessment of total mesorectal excision.

BACKGROUND: Specific pelvic bone dimensions have been identified as predictors of total mesorectal e...

Multi-institutional development and testing of attention-enhanced deep learning segmentation of thyroid nodules on ultrasound.

PURPOSE: Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS cla...

Dual-Stage AI Model for Enhanced CT Imaging: Precision Segmentation of Kidney and Tumors.

OBJECTIVES: Accurate kidney and tumor segmentation of computed tomography (CT) scans is vital for di...

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