Radiology

GAN-based Denoising for Scan Time Reduction and Motion Correction of 18F FP-CIT PET/CT: A Multicenter External Validation Study.

PURPOSE: AI-driven scan time reduction is rapidly transforming medical imaging with benefits such as improved patient comfort and enhanced efficiency. A Dual Contrastive Learning Generative Adversarial Network (DCLGAN) was developed to predict full-time PET scans from shorter, noisier scans, improving challenges in imaging patients with movement disorders.

F-FDG dose reduction using deep learning-based PET reconstruction.

BACKGROUND: A deep learning-based image reconstruction (DLR) algorithm that can reduce the statistical noise has been developed for PET/CT imaging. It may reduce the administered dose of F-FDG and minimize radiation exposure while maintaining diagnostic quality. This retrospective study evaluated whether the injected F-FDG dose could be reduced by applying DLR to PET images. To this aim, we compar...

Patient radiation safety in the intensive care unit.

The aim of this commentary review was to summarize the main research evidences on radiation exposure and to underline the best clinical and radiologic...

Magnetic resonance imaging of cruciate ligament disorders: current updates.

While conventional structural magnetic resonance imaging (MRI) can detect cruciate ligament anatomy and injuries, it has inherent limitations. Recentl...

ARTIFICIAL INTELLIGENCE ENHANCES DIAGNOSTIC ACCURACY OF CONTRAST ENEMAS IN HIRSCHSPRUNG DISEASE COMPARED TO CLINICAL EXPERTS.

Introduction Contrast enema (CE) is widely used in the evaluation of suspected Hirschsprung disease (HD). Deep learning is a promising tool to standar...

Ultrasound-based classification of follicular thyroid Cancer using deep convolutional neural networks with transfer learning.

This study aimed to develop and validate convolutional neural network (CNN) models for distinguishing follicular thyroid carcinoma (FTC) from follicul...

Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distribution.

Anterior Cruciate Ligament (ACL) tears are common in sports and can provide noteworthy health issues. Therefore, accurately diagnosing of tears is imp...

Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features.

To develop and validate a machine learning-based prediction model to predict axillary lymph node (ALN) metastasis in triple negative breast cancer (TN...

FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI.

Ankylosing Spondylitis (AS), commonly known as Bechterew's disease, is a complex, potentially disabling disease that develops slowly over time and pro...

Predictive value of subacromial motion metrics for the effectiveness of ultrasound-guided dual-target injection: a longitudinal follow-up cohort trial.

OBJECTIVE: Subacromial impingement syndrome (SIS) frequently causes shoulder pain. This study aimed to (1) assess the predictive utility of quantitati...

Developments in MRI radiomics research for vascular cognitive impairment.

Vascular cognitive impairment (VCI) is an umbrella term for diseases associated with cognitive decline induced by substantive brain damage following p...

Knowledge mapping of ultrasound technology and triple-negative breast cancer: a visual and bibliometric analysis.

OBJECTIVE: This study aims to explore the application of ultrasound technology in triple-negative breast cancer (TNBC) using bibliometric methods. It ...

Multi-modal and Multi-view Cervical Spondylosis Imaging Dataset.

Multi-modal and multi-view imaging is essential for diagnosis and assessment of cervical spondylosis. Deep learning has increasingly been developed to...

An adaptive deep learning approach based on InBNFus and CNNDen-GRU networks for breast cancer and maternal fetal classification using ultrasound images.

Convolutional Neural Networks (CNNs), a sophisticated deep learning technique, have proven highly effective in identifying and classifying abnormaliti...

Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase F-Florbetaben PET and clinical features.

This study developed machine learning models to predict Aβ positivity in Alzheimer's disease by integrating early-phase F-Florbetaben PET and clinical...

Generative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images.

Segmenting abnormalities is a leading problem in medical imaging. Using machine learning for segmentation generally requires manually annotated segmen...

Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare.

Brain tumor causes life-threatening consequences due to which its timely detection and accurate classification are critical for determining appropriat...

A superpixel based self-attention network for uterine fibroid segmentation in high intensity focused ultrasound guidance images.

Ultrasound guidance images are widely used for high intensity focused ultrasound (HIFU) therapy; however, the speckles, acoustic shadows, and signal a...

Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification.

Brain tumors are a significant contributor to cancer-related deaths worldwide. Accurate and prompt detection is crucial to reduce mortality rates and ...

A deep convolutional neural network-based novel class balancing for imbalance data segmentation.

Retinal fundus images provide valuable insights into the human eye's interior structure and crucial features, such as blood vessels, optic disk, macul...