Radiology

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

16,018 articles
Stay Ahead - Weekly Radiology research updates
Subscribe
Browse Specialties
Showing 1366-1386 of 16,018 articles
T1-weighted MRI-based brain tumor classification using hybrid deep learning models.

Health is fundamental to human well-being, with brain health particularly critical for cognitive fun...

Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer.

OBJECTIVE: This study explores the value of combining intratumoral and peritumoral radiomics feature...

Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.

PURPOSE: Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second...

Evaluation of MRI anatomy in machine learning predictive models to assess hydrogel spacer benefit for prostate cancer patients.

INTRODUCTION: Hydrogel spacers (HS) are designed to minimise the radiation doses to the rectum in pr...

Evaluation of AI-based nerve segmentation on ultrasound: relevance of standard metrics in the clinical setting.

BACKGROUND: In artificial intelligence for ultrasound-guided regional anaesthesia, accurate nerve id...

Post-Mortem imaging biobanks: Building data for reproducibility, standardization, and AI integration.

In recent years, post-mortem imaging has advanced with techniques such as Post-Mortem Computed Tomog...

The need for balancing 'black box' systems and explainable artificial intelligence: A necessary implementation in radiology.

Radiology is one of the medical specialties most significantly impacted by Artificial Intelligence (...

Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models.

Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in...

The radiologist as an independent "third party" to the patient and clinicians in the era of generative AI.

Radiologists are crucial in the diagnostic workflow. They must maintain an independent perspective, ...

Deep Learning for Lumbar Disc Herniation Diagnosis and Treatment Decision-Making Using Magnetic Resonance Imagings: A Retrospective Study.

BACKGROUND: Lumbar disc herniation (LDH) is a common cause of back and leg pain. Diagnosis relies on...

AI in Breast Cancer Imaging: An Update and Future Trends.

Breast cancer is one of the most common types of cancer affecting women worldwide. Artificial intell...

MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach.

The firmness of meningiomas is a critical factor that impacts the surgical approach recommended for ...

Non-Invasive Biomarkers in the Era of Big Data and Machine Learning.

Invasive diagnostic techniques, while offering critical insights into disease pathophysiology, are o...

Deep learning-based Intraoperative MRI reconstruction.

BACKGROUND: We retrospectively evaluated the quality of deep learning (DL) reconstructions of on-sca...

RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI.

Accurate identification of molecular subtypes in breast cancer is critical for personalized treatmen...

A PET/CT-based 3D deep learning model for predicting spread through air spaces in stage I lung adenocarcinoma.

PURPOSE: This study evaluates a three-dimensional (3D) deep learning (DL) model based on fluorine-18...

Browse Specialties