Radiology

DCE-Qnet: deep network quantification of dynamic contrast enhanced (DCE) MRI.

INTRODUCTION: Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption.

Deep learning-based computer-aided detection of ultrasound in breast cancer diagnosis: A systematic review and meta-analysis.

PURPOSE: The aim of this meta-analysis was to assess the diagnostic performance of deep learning (DL) and ultrasound in breast cancer diagnosis. Additionally, we categorized the included studies into two subgroups: B-mode ultrasound diagnostic subgroup and multimodal ultrasound diagnostic subgroup, and compared the performance differences of DL algorithms in breast cancer diagnosis using only B-mo...

[Healthcare 4.0-Medicine in transition].

Healthcare 4.0 describes the future transformation of the healthcare sector driven by the combination of digital technologies, such as artificial inte...

BrainSegFounder: Towards 3D foundation models for neuroimage segmentation.

The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to analyze and interpret neuroimaging data. Medical ...

TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis.

Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activat...

RS-Net: An end-to-end deep learning framework for rodent skull stripping in multi-center brain MRI.

Skull stripping is a crucial preprocessing step in magnetic resonance imaging (MRI), where experts manually create brain masks. This labor-intensive p...

Deep learning enables accurate soft tissue tendon deformation estimation in vivo via ultrasound imaging.

Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue ...

A Cascaded Mutliresolution Ensemble Deep Learning Framework for Large Scale Alzheimer's Disease Detection Using Brain MRIs.

Alzheimer's is progressive and irreversible type of dementia, which causes degeneration and death of cells and their connections in the brain. AD wors...

A YOLOX-Based Deep Instance Segmentation Neural Network for Cardiac Anatomical Structures in Fetal Ultrasound Images.

Echocardiography is an essential procedure for the prenatal examination of the fetus for congenital heart disease (CHD). Accurate segmentation of key ...

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics a...

Big Data Analytics on Lung Cancer Diagnosis Framework With Deep Learning.

As the segment of diseased tissue in PET images is time-consuming, laborious and low accuracy, this work proposes an automated framework for PET image...

Synthesis of higher-B CEST Z-spectra from lower-B data via deep learning and singular value decomposition.

Chemical exchange saturation transfer (CEST) MRI at 3 T suffers from low specificity due to overlapping CEST effects from multiple metabolites, while ...

Patients' Attitudes Toward the Use of Artificial Intelligence as a Diagnostic Tool in Radiology in Saudi Arabia: Cross-Sectional Study.

BACKGROUND: Artificial intelligence (AI) is widely used in various medical fields, including diagnostic radiology as a tool for greater efficiency, pr...

Contrastive machine learning reveals Parkinson's disease specific features associated with disease severity and progression.

Parkinson's disease (PD) exhibits heterogeneity in terms of symptoms and prognosis, likely due to diverse neuroanatomical alterations. This study empl...

Establishing a Classification System for Predicting Flow-Limiting Dissection After Balloon Angioplasty Using Explainable Machine-Learning Models: A Multicenter Retrospective Cohort Study.

OBJECTIVE: Percutaneous transluminal angioplasty (PTA) is the primary method for treatment in peripheral arterial disease. However, some patients expe...

Deep learning approaches for the detection of scar presence from cine cardiac magnetic resonance adding derived parametric images.

This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed from cine cardiac magnetic ...

Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks.

Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD ...

Attention-Based MultiOffset Deep Learning Reconstruction of Chemical Exchange Saturation Transfer (AMO-CEST) MRI.

One challenge of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is the long scan time due to multiple acquisitions of i...

Preoperative Prediction of Axillary Lymph Node Metastasis in Patients With Breast Cancer Through Multimodal Deep Learning Based on Ultrasound and Magnetic Resonance Imaging Images.

RATIONALE AND OBJECTIVES: Deep learning can enhance the performance of multimodal image analysis, which is known for its noninvasive attributes and co...

Enhancing the reliability of deep learning-based head and neck tumour segmentation using uncertainty estimation with multi-modal images.

Deep learning shows promise in autosegmentation of head and neck cancer (HNC) primary tumours (GTV-T) and nodal metastases (GTV-N). However, errors su...