Radiology

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

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Quantitative fat-fraction analysis of the rotator cuff muscles on clinical sagittal and coronal T1-weighted MRI using deep learning algorithms.

Increased fatty infiltration of the rotator cuff muscles is a primary prognostic factor for poor sur...

Feb 2026 41688617
Anatomy-guided visual prompt tuning for cross-modal breast cancer understanding.

Early and reliable detection of breast cancer across imaging modalities remains a long-standing chal...

Feb 2026 41688744
Evaluating the impact of artificial intelligence tools on the detection of chest injuries from medical imaging: A systematic review and meta-analysis.

BACKGROUND: There has been a growing interest in the clinical application of artificial intelligence...

Feb 2026 41705907
The application of Machine learning in predicting the outcomes of minimally invasive treatments for uterine Fibroids: A systematic review and meta-analysis.

RATIONALE AND OBJECTIVES: Uterine fibroids (UFs) are common benign tumors that impact women's health...

Feb 2026 41691731
Establishing an Artificial Intelligence Sandbox for Diagnostic Imaging in Ontario.

Ontario faces persistent diagnostic imaging (DI) challenges, which includes fragmented implementatio...

Feb 2026 41685485
Artificial Intelligence in Tuberculosis Imaging: A Global Bibliometric Analysis of Research Trends and Collaborations.

INTRODUCTION: Tuberculosis (TB), a leading infectious cause of death, remains a global health challe...

Feb 2026 41687417
Automatic field-of-view planning for magnetic resonance shoulder imaging using Deep Learning.

INTRODUCTION: Accurate prescription of oblique coronal and oblique sagittal field of views (FOV) is ...

Feb 2026 41687427
Deep Learning and Machine Learning for Differentiation Between Contrast Extravasation and Hemorrhagic Transformation in Post-Thrombectomy Stroke CT.

BACKGROUND: Mechanical thrombectomy (MT) improves outcomes in acute ischemic stroke (AIS) but often ...

Feb 2026 41687434
Reinforcement learning-guided segment anything model for MRI prostate and dominant intraprostatic lesions auto-segmentation.

Objective. Accurate segmentation of the prostate and dominant intraprostatic lesions (DILs) on magne...

Feb 2026 41643322
Large-scale evaluation of multimodal large language models for pneumothorax detection.

PURPOSE: Pneumothorax requires rapid recognition and accurate interpretation of chest X-rays (CXRs),...

Feb 2026 41674096
Radiomics-based differentiation of benign and malignant breast masses on contrast-enhanced mammography: a reproducible workflow using open-source tools.

PURPOSE: To differentiate benign and malignant breast masses by extracting radiomic features from lo...

Feb 2026 41674112
Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence.

BACKGROUND: Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the im...

Feb 2026 41674454
Foundation models in radiology: a primer for pediatric radiologists.

Foundation models (FMs) are large deep learning models pre-trained on vast heterogeneous datasets th...

Feb 2026 41677830
[Sudden cardiac death in structural heart disease : Update on prevention].

Sudden cardiac death (SCD) remains one of the leading causes of death despite major advances in card...

Feb 2026 41677836
Machine Learning for MRI Classification of Systemic Lupus Erythematous Patients with and without Neuropsychiatric Events.

To provide a useful and practical Machine Learning framework to facilitate the diagnosis of Neuropsy...

Feb 2026 41680572
Development and Evaluation of a Retrieval-Augmented Generation System for Radiology Guidelines.

Large language models (LLMs) have demonstrated remarkable capabilities in processing and generating ...

Feb 2026 41680573
Linking cortical structure and delirium in the elderly: insights from cohort study and shared genetic risk analysis.

BACKGROUND: This study aimed to assess the association between regional cortical changes measured vi...

Feb 2026 41680881
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