Radiology

[Healthcare 4.0-Medicine in transition].

Healthcare 4.0 describes the future transformation of the healthcare sector driven by the combinatio...

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 m...

The Role of Preoperative Abdominal Ultrasound in the Preparation of Patients Undergoing Primary Metabolic and Bariatric Surgery: A Machine Learning Algorithm on 4418 Patients' Records.

BACKGROUND: The utility of preoperative abdominal ultrasonography (US) in evaluating patients with o...

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, i...

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 ...

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) play...

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 ...

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...

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 overlapp...

Commentary: Detection of Endoleak After Endovascular Aortic Repair Through Deep Learning Based on Non-contrast CT.

The impact of multicentric datasets for the automated tumor delineation in primary prostate cancer using convolutional neural networks on F-PSMA-1007 PET.

PURPOSE: Convolutional Neural Networks (CNNs) have emerged as transformative tools in the field of r...

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 di...

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 diagnos...

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 para...

Do machine learning methods improve prediction of ambient air pollutants with high spatial contrast? A systematic review.

BACKGROUND & OBJECTIVE: The use of machine learning for air pollution modelling is rapidly increasin...

Ensemble machine learning to predict futile recanalization after mechanical thrombectomy based on non-contrast CT imaging.

OBJECTIVES: Despite successful recanalization after Mechanical Thrombectomy (MT), approximately 25 %...

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 th...

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 ...

A Deep Learning-Based Framework for Predicting Intracerebral Hematoma Expansion Using Head Non-contrast CT Scan.

RATIONALE AND OBJECTIVES: Hematoma expansion (HE) in intracerebral hemorrhage (ICH) is a critical fa...

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, wh...

Single-detector multiplex imaging flow cytometry for cancer cell classification with deep learning.

Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged ...