Radiology

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

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AI and early diagnostics: mapping fetal facial expressions through development, evolution, and 4D ultrasound.

The development of facial musculature and expressions in the human fetus represents a critical inter...

Ontology-guided machine learning outperforms zero-shot foundation models for cardiac ultrasound text reports.

Big data can revolutionize research and quality improvement for cardiac ultrasound. Text reports are...

Reducing inference cost of Alzheimer's disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners.

While multi-modal deep learning approaches trained using magnetic resonance imaging (MRI) and fluoro...

Deep learning on pre-procedural computed tomography and clinical data predicts outcome following stroke thrombectomy.

BACKGROUND: Deep learning using clinical and imaging data may improve pre-treatment prognostication ...

Hybrid-RViT: Hybridizing ResNet-50 and Vision Transformer for Enhanced Alzheimer's disease detection.

Alzheimer's disease (AD) is a leading cause of disability worldwide. Early detection is critical for...

Deep transfer learning based hierarchical CAD system designs for SFM images.

Present work involves rigorous experimentation for classification of mammographic masses by employin...

RADEX: a rule-based clinical and radiology data extraction tool demonstrated on thyroid ultrasound reports.

OBJECTIVES: Radiology reports contain valuable information for research and audits, but relevant det...

Improved segmentation of hepatic vascular networks in ultrasound volumes using 3D U-Net with intensity transformation-based data augmentation.

Accurate three-dimensional (3D) segmentation of hepatic vascular networks is crucial for supporting ...

Automated pediatric TMJ articular disk identification and displacement classification in MRI with machine learning.

OBJECTIVE: To evaluate the performance of an automated two-step model interpreting pediatric temporo...

Large Language Models-Supported Thrombectomy Decision-Making in Acute Ischemic Stroke Based on Radiology Reports: Feasibility Qualitative Study.

BACKGROUND: The latest advancement of artificial intelligence (AI) is generative pretrained transfor...

Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT.

PURPOSE: This study aims to explore the potential of non-contrast abdominal CT radiomics and deep le...

Diagnosis and treatment of a rare bilateral primary thyroid cancer: a case report.

Preoperative ultrasound examination of thyroid nodules is the most economical and effective screenin...

Image quality and diagnostic performance of deep learning reconstruction for diffusion- weighted imaging in 3 T breast MRI.

PURPOSE: This study aimed to assess the image quality and the diagnostic value of deep learning reco...

Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography.

PURPOSE: To determine how automation bias (inclination of humans to overly trust-automated decision-...

Artificial Intelligence Model for Detection of Colorectal Cancer on Routine Abdominopelvic CT Examinations: A Training and External-Testing Study.

Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT exami...

Deep learning-based quick MLC sequencing for MRI-guided online adaptive radiotherapy: a feasibility study for pancreatic cancer patients.

One bottleneck of magnetic resonance imaging (MRI)-guided online adaptive radiotherapy is the time-c...

Diffusion-driven multi-modality medical image fusion.

Multi-modality medical image fusion (MMIF) technology utilizes the complementarity of different moda...

Eliminating the second CT scan of dual-tracer total-body PET/CT via deep learning-based image synthesis and registration.

PURPOSE: This study aims to develop and validate a deep learning framework designed to eliminate the...

[Transformation of free-text radiology reports into structured data].

BACKGROUND: The rapid development of large language models (LLMs) opens up new possibilities for the...

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