Radiology

Radiology Synthetic Confusion: How Generative Artifical Intelligence Amplifies Misunderstandings of Radiologists and Technologists in Patient-Facing Media.

Artificial intelligence (AI) tools, particularly generative models, are increasingly used to depict clinical roles in healthcare. This study evaluates whether generative AI systems accurately differentiate between radiologists and medical radiation technologists (MRTs), 2 roles often confused by patients and providers. We assessed 1380 images and videos generated by 8 text-to-image/video AI mode...

Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.

No robust biomarkers have been identified to predict the efficacy of programmed cell death protein 1 (PD-1) inhibitors in patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). We aimed to develop radiomic models using pre-immunotherapy MRI to predict the response to PD-1 inhibitors and the patient prognosis. This study included 246 LANPC patients (training cohort, = 117; exter...

GPT-4o and Specialized AI in Breast Ultrasound Imaging: A comparative Study on Accuracy, Agreement, Limitations, and Diagnostic Potential.

OBJECTIVES: This study aimed to evaluate the ability of ChatGPT and Breast Ultrasound Helper, a special ChatGPT-based subprogram trained on ultrasound...

Clinical benefits of deep learning-assisted ultrasound in predicting lymph node metastasis in pancreatic cancer patients.

AIM: This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) derived from ultrasound images to improve predictive accuracy ...

Evaluation of deep learning reconstruction in accelerated knee MRI: comparison of visual and diagnostic performance metrics.

OBJECTIVE: To investigate the clinical value of deep learning reconstruction (DLR) in accelerated magnetic resonance imaging (MRI) of the knee and com...

BrainNet-GAN: Generative Adversarial Graph Convolutional Network for Functional Brain Network Synthesis from Routine Clinical Brain Structural T1-Weighted Sequence.

Functional brain network (FBN) derived from functional Magnetic Resonance Imaging (fMRI) has promising prospects in clinical research, but fMRI is not...

Self-Supervised Optimization of RF Data Coherence for Improving Breast Reflection UCT Reconstruction.

Reflection Ultrasound Computed Tomography (UCT) is gaining prominence as an essential instrument for breast cancer screening. However, reflection UCT ...

Exploring Neural Dynamics in the Auditory Telencephalon of Crows using Functional Ultrasound Imaging.

Crows, renowned for advanced cognitive abilities and vocal communication, rely on intricate auditory systems. While the neuroanatomy of corvid auditor...

Prospective Registry Study on Thermal Liver Ablation of Primary and Secondary Liver Tumours Named the A-IMAGIO Study.

PURPOSE: The long-term objective of the Ablation-IMaging and Advanced Guidance for workflow optimization in Interventional Oncology (A-IMAGIO) project...

Fine-tuned large language model for classifying CT-guided interventional radiology reports.

BackgroundManual data curation was necessary to extract radiology reports due to the ambiguities of natural language.PurposeTo develop a fine-tuned la...

Machine learning-based prediction of amyloid positivity using early-phase F-18 flutemetamol PET.

BackgroundPrevious studies have suggested that early-phase imaging of amyloid positron emission tomography (PET) may offer information for predicting ...

Ensemble-based Convolutional Neural Networks for brain tumor classification in MRI: Enhancing accuracy and interpretability using explainable AI.

BACKGROUND: Accurate and efficient classification of brain tumors, including gliomas, meningiomas, and pituitary adenomas, is critical for early diagn...

Enhancing Lung Cancer Diagnosis: An Optimization-Driven Deep Learning Approach with CT Imaging.

Lung cancer (LC) remains a leading cause of mortality worldwide, affecting individuals across all genders and age groups. Early and accurate diagnosis...

Axial Skeletal Assessment in Osteoporosis Using Radiofrequency Echographic Multi-spectrometry: Diagnostic Performance, Clinical Utility, and Future Directions.

Osteoporosis, a prevalent skeletal disorder, necessitates accurate and accessible diagnostic tools for effective disease management and fracture preve...

Large models in medical imaging: Advances and prospects.

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large languag...

MVKD-Trans: A Multi-View Knowledge Distillation Vision Transformer Architecture for Breast Cancer Classification Based on Ultrasound Images.

Breast cancer is the leading cancer threatening women's health. In recent years, deep neural networks have outperformed traditional methods in terms o...

Emergency radiology: roadmap for radiology departments.

Emergency radiology has evolved into a significant subspecialty over the past 2 decades, facing unique challenges including escalating imaging volumes...

Artificial intelligence-based tumor size measurement on mammography: agreement with pathology and comparison with human readers' assessments across multiple imaging modalities.

PURPOSE: To evaluate the agreement between artificial intelligence (AI)-based tumor size measurements of breast cancer and the final pathology and com...

Automatic Multi-Task Segmentation and Vulnerability Assessment of Carotid Plaque on Contrast-Enhanced Ultrasound Images and Videos via Deep Learning.

Intraplaque neovascularization (IPN) within carotid plaque is a crucial indicator of plaque vulnerability. Contrast-enhanced ultrasound (CEUS) is a va...

The value of multimodal neuroimaging in the diagnosis and treatment of post-traumatic stress disorder: a narrative review.

Post-traumatic stress disorder (PTSD) is a delayed-onset or prolonged persistent psychiatric disorder caused by individuals experiencing an unusually ...