Radiology

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.

Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cance...

Development and validation of a random forest algorithm for source attribution of animal and human Typhimurium and monophasic variants of Typhimurium isolates in England and Wales utilising whole genome sequencing data.

Source attribution has traditionally involved combining epidemiological data with different pathogen...

Reproducibility of artificial intelligence-enabled plaque measurements between systolic and diastolic phases from coronary computed tomography angiography.

OBJECTIVES: Current coronary CT angiography (CTA) guidelines suggest both end-systolic and mid-diast...

Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning.

BACKGROUND: Quantitative coronary angiography (QCA) offers objective and reproducible measures of co...

Deep Learning Image Reconstruction for Transcatheter Aortic Valve Implantation Planning: Image Quality, Diagnostic Performance, Contrast volume and Radiation Dose Assessment.

RATIONALE AND OBJECTIVES: To assess image quality, contrast volume and radiation dose reduction pote...

[Reduction of Motion Artifacts in Liver MRI Using Deep Learning with High-pass Filtering].

PURPOSE: To investigate whether deep learning with high-pass filtering can be used to effectively re...

Reply to comment on predicting the risk stratification of gastrointestinal stromal tumors using machine learning‑based ultrasound radiomics.

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.

Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic ...

MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms.

BACKGROUND AND OBJECTIVE: Deep Learning models have emerged as a significant tool in generating effi...

Diagnostic performance of deep learning to exclude coronary stenosis on CT angiography in TAVI patients.

We evaluated the diagnostic performance of a deep-learning model (DLM) (CorEx®, Spimed-AI, Paris, Fr...

Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [F]FDG PET/CT predicts survival in multiple myeloma.

PURPOSE: Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient out...

Automated Spontaneous Echo Contrast Detection Using a Multisequence Attention Convolutional Neural Network.

OBJECTIVE: Spontaneous echo contrast (SEC) is a vascular ultrasound finding associated with increase...

Design and Control of the Magnetically Actuated Micro/Nanorobot Swarm toward Biomedical Applications.

Recently, magnetically actuated micro/nanorobots hold extensive promises in biomedical applications ...

Improving the detection of hypo-vascular liver metastases in multiphase contrast-enhanced CT with slice thickness less than 5 mm using DenseNet.

INTRODUCTION: Thinner slices are more susceptible in detecting small lesions but suffer from higher ...

Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective.

Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has ...

"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.

BACKGROUND: Radiotherapy (RT) is an important treatment modality for patients with brain malignancie...

Effectiveness and safety of robot-assisted versus fluoroscopy-assisted pedicle screw implantation in scoliosis surgery: a systematic review and meta-analysis.

This study aimed to assess the effectiveness and safety of robot-assisted versus fluoroscopy-assiste...

3D CNN-based Deep Learning Model-based Explanatory Prognostication in Patients  with Multiple Myeloma using Whole-body MRI.

Although magnetic resonance imaging (MRI) data of patients with multiple myeloma (MM) are used to pr...

Of editorial processes, AI models, and medical literature: the Magnetic Resonance Audiometry experiment.

The potential of artificial intelligence (AI) in the field of medical research is unquestionable. Ne...

The emperor has few clothes: a realistic appraisal of current AI in radiology.

Cine-cardiac magnetic resonance to distinguish between ischemic and non-ischemic cardiomyopathies: a machine learning approach.

OBJECTIVE: This work aimed to derive a machine learning (ML) model for the differentiation between i...