Radiology

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

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Deep Learning-Based Acceleration in MRI: Current Landscape and Clinical Applications in Neuroradiology.

Magnetic resonance imaging (MRI) is a cornerstone of neuroimaging, providing unparalleled soft-tissu...

Non-Invasive Bedside Approaches for Assessing Microvascular Dysfunction.

Microvascular dysfunction is implicated in a range of acute and chronic conditions, ranging from car...

Patient Perspectives on Artificial Intelligence in Medical Imaging.

Artificial intelligence (AI) is reshaping medical imaging with the promise of improved diagnostic ac...

Advancements in lung cancer: molecular insights, innovative therapies, and future prospects.

Still among the most common and deadly cancers worldwide, lung cancer causes major morbidity and dea...

From promise to practice: a scoping review of AI applications in abdominal radiology.

AI is rapidly transforming abdominal radiology. This scoping review mapped current applications acro...

Fully automated 3D multi-modal deep learning model for preoperative T-stage prediction of colorectal cancer using F-FDG PET/CT.

PURPOSE: This study aimed to develop a fully automated 3D multi-modal deep learning model using preo...

Dosimetric evaluation of synthetic kilo-voltage CT images generated from megavoltage CT for head and neck tomotherapy using a conditional GAN network.

The lower image contrast of megavoltage computed tomography (MVCT), which corresponds to kilovoltage...

Towards trustworthy artificial intelligence in musculoskeletal medicine: A narrative review on uncertainty quantification.

INTRODUCTION: Deep learning (DL) models have achieved remarkable performance in musculoskeletal (MSK...

HIST-DIP: histogram thresholding and deep image priors assisted smartphone-based fluorescence microscopy imaging.

Portable fluorescence microscopes coupled with smartphones offer accessible and cost-effective point...

Multi-modal classification of retinal disease based on convolutional neural network.

Retinal diseases such as age-related macular degeneration and diabetic retinopathy will lead to irre...

Performance Evaluation of Artificial Intelligence Techniques in the Diagnosis of Brain Tumors: A Systematic Review and Meta-Analysis.

Brain tumors are becoming more prevalent, often leading to severe disability and high mortality rate...

Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges.

Lymphoma is a prevalent malignant tumor within the hematological system, posing significant challeng...

Connectomic stroke lesion measures provide no benefit over basic spatial lesion features in the prognosis of global stroke outcome measures.

The prediction of stroke outcome from imaging markers could be used to guide individualized therapeu...

AI-driven preclinical disease risk assessment using imaging in UK biobank.

Identifying disease risk and detecting disease before clinical symptoms appear are essential for ear...

Quantification of hepatic steatosis on post-contrast computed tomography scans using artificial intelligence tools.

PURPOSE: Early detection of steatotic liver disease (SLD) is critically important. In clinical pract...

A novel hybrid deep learning approach combining deep feature attention and statistical validation for enhanced thyroid ultrasound segmentation.

An effective diagnosis system and suitable treatment planning require the precise segmentation of th...

A novel dual-student reverse knowledge distillation method for magnetic tile defect detection.

Magnetic tiles surface defect detection is crucial in industrial production. However, it is difficul...

A review on computer-aided diagnostic system to classify the disorders of the gastrointestinal tract.

Various diseases, such as colon cancer, gastric cancer, celiac, and bleeding, pose a significant ris...

Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis.

BACKGROUND: Extrinsic adenomyosis exhibits heterogeneous clinical symptoms, with pain being more com...

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