Radiology

AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.

The integration of artificial intelligence (AI) into clinical practice, particularly within radiolog...

Integrative multimodal ultrasound and radiomics for early prediction of neoadjuvant therapy response in breast cancer: a clinical study.

PURPOSE: This study aimed to develop an early predictive model for neoadjuvant therapy (NAT) respons...

Evolution of CT perfusion software in stroke imaging: from deconvolution to artificial intelligence.

Computed tomography perfusion (CTP) represents one of the main determinants in the decision-making s...

Differentiated thyroid cancer and positron emission computed tomography: when, how and why?

INTRODUCTION: Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) has b...

Multiparameter MRI-based automatic segmentation and diagnostic models for the differentiation of intracranial solitary fibrous tumors and meningiomas.

BACKGROUND: Intracranial solitary fibrous tumors (SFTs) and meningiomas are meningeal tumors with di...

OMT and tensor SVD-based deep learning model for segmentation and predicting genetic markers of glioma: A multicenter study.

Glioma is the most common primary malignant brain tumor and preoperative genetic profiling is essent...

Corrigendum to "Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review" [Clin Imaging 118 (2025) 110369].

Post-hoc eXplainable AI methods for analyzing medical images of gliomas (- A review for clinical applications).

Deep learning (DL) has shown promise in glioma imaging tasks using magnetic resonance imaging (MRI) ...

Deep supervised transformer-based noise-aware network for low-dose PET denoising across varying count levels.

BACKGROUND: Reducing radiation dose from PET imaging is essential to minimize cancer risks; however,...

Contemporary Concise Review 2024: New Techniques in Interventional Pulmonology.

Navigational bronchoscopy and cone beam computed tomography (CBCT) guided bronchoscopy show comparab...

Inter-AI Agreement in Measuring Cine MRI-Derived Cardiac Function and Motion Patterns: A Pilot Study.

Manually analyzing a series of MRI images to obtain information about the heart's motion is a time-c...

Assessment of T2-weighted MRI-derived synthetic CT for the detection of suspected lumbar facet arthritis: a comparative analysis with conventional CT.

PURPOSE: We evaluated sCT generated from T2-weighted imaging (T2WI) using deep learning techniques t...

Robust Bi-CBMSegNet framework for advancing breast mass segmentation in mammography with a dual module encoder-decoder approach.

Breast cancer is a prevalent disease affecting millions of women worldwide, and early screening can ...

Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.

Advanced metabolic-dysfunction-associated steatotic liver disease (MASLD) fibrosis (F3-4) predicts l...

Integrating radiomic texture analysis and deep learning for automated myocardial infarction detection in cine-MRI.

Robust differentiation between infarcted and normal myocardial tissue is essential for improving dia...

Confidence-Driven Deep Learning Framework for Early Detection of Knee Osteoarthritis.

Knee Osteoarthritis (KOA) is a prevalent musculoskeletal disorder that severely impacts mobility and...

Deep Learning Approach for Biomedical Image Classification.

Biomedical image classification is of paramount importance in enhancing diagnostic precision and imp...

Development of a deep learning model for predicting skeletal muscle density from ultrasound data: a proof-of-concept study.

Reduced muscle mass and function are associated with increased morbidity, and mortality. Ultrasound,...

A novel UNet-SegNet and vision transformer architectures for efficient segmentation and classification in medical imaging.

Medical imaging has become an essential tool in the diagnosis and treatment of various diseases, and...

MTMedFormer: multi-task vision transformer for medical imaging with federated learning.

Deep learning has revolutionized medical imaging, improving tasks like image segmentation, detection...

Deep learning 3D super-resolution radiomics model based on Gd-enhanced MRI for improving preoperative prediction of HCC pathological grading.

PURPOSE: The histological grade of hepatocellular carcinoma (HCC) is an important factor associated ...