Radiology

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

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Role of VATS-US in identifying and characterizing pulmonary nodules: a narrative review.

The aim of this study was to show the efficacy described in the scientific literature of lung ultras...

Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images.

Intracerebral haemorrhage (ICH) is a common form of stroke that affects millions of people worldwide...

Unveiling the invisible: How cutting-edge neuroimaging transforms adolescent depression diagnosis.

Yu 's study has advanced the understanding of the neural mechanisms underlying major depressive diso...

Association of [F]-FDG PET/CT-Derived Radiomic Features with Clinical Outcomes and Genomic Profiles in Patients with Chronic Lymphocytic Leukemia.

The role of PET/CT imaging in chronic lymphoproliferative syndromes (CLL) is debated. This study ex...

Diagnosis of early idiopathic pulmonary fibrosis: current status and future perspective.

The standard approach to diagnosing idiopathic pulmonary fibrosis (IPF) includes identifying the usu...

Development and validation of ultrasound-based radiomics deep learning model to identify bone erosion in rheumatoid arthritis.

OBJECTIVE: To develop and validate a deep learning radiomics fusion model (DLR) based on ultrasound ...

Semiautomated segmentation of breast tumor on automatic breast ultrasound image using a large-scale model with customized modules.

To verify the capability of the Segment Anything Model for medical images in 3D (SAM-Med3D), tailore...

Accuracy of segment anything model for classification of vascular stenosis in digital subtraction angiography.

BACKGROUND: This retrospective study evaluates the diagnostic performance of an optimized comprehens...

Thymoma habitat segmentation and risk prediction model using CT imaging and K-means clustering.

BACKGROUND: Thymomas, though rare, present a wide range of clinical behaviors, from indolent to aggr...

Transformer model based on Sonazoid contrast-enhanced ultrasound for microvascular invasion prediction in hepatocellular carcinoma.

BACKGROUND: Microvascular invasion (MVI) is strongly associated with the prognosis of patients with ...

Functional MRI Analysis of Cortical Regions to Distinguish Lewy Body Dementia From Alzheimer's Disease.

OBJECTIVE: Cortical regions such as parietal area H (PH) and the fundus of the superior temporal sul...

Feasibility study of a general model for synthetic CT generation in MRI-guided extracranial radiotherapy.

This study aims to investigate the feasibility of a single general model to synthesize CT images acr...

Evaluating Second-Generation Deep Learning Technique for Noise Reduction in Myocardial T1-Mapping Magnetic Resonance Imaging.

BACKGROUND: T1 mapping has become a valuable technique in cardiac magnetic resonance imaging (CMR) f...

Artificial Intelligence as a Potential Tool for Predicting Surgical Margin Status in Early Breast Cancer Using Mammographic Specimen Images.

: Breast cancer is the most common malignancy among women globally, with an increasing incidence, pa...

Fair ultrasound diagnosis via adversarial protected attribute aware perturbations on latent embeddings.

Deep learning techniques have significantly enhanced the convenience and precision of ultrasound ima...

Development of a deep-learning algorithm for etiological classification of subarachnoid hemorrhage using non-contrast CT scans.

OBJECTIVES: This study aims to develop a deep learning algorithm for differentiating aneurysmal suba...

MRI-based radiomics for differentiating high-grade from low-grade clear cell renal cell carcinoma: a systematic review and meta-analysis.

PURPOSE: High-grade clear cell renal cell carcinoma (ccRCC) is linked to lower survival rates and mo...

Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer's disease.

The recent approval of anti-amyloid pharmaceuticals for the treatment of Alzheimer's disease (AD) ha...

AI in motion: the impact of data augmentation strategies on mitigating MRI motion artifacts.

OBJECTIVES: Artifacts in clinical MRI can compromise the performance of AI models. This study evalua...

Cardiovascular imaging in 2024: review of current research and innovations.

Cardiovascular imaging saw significant advancements in 2024, impacting technology, pathophysiology, ...

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