Radiology

Discriminating bipolar depression from major depressive disorder using functional and microstructural signatures of inferior fronto-occipital fasciculus: Promising results from an MRI study.

BACKGROUND: Bipolar depression (BD-d) and major depressive disorder (MDD) share overlapping clinical symptoms, posing a significant diagnostic challenge. The inferior fronto-occipital fasciculus (IFOF) is a critical long-range tract that integrates cognitive and affective processes across brain networks. However, it remains unexplored whether bilateral IFOF abnormalities differ between BD-d and MD...

A large dataset of brain imaging linked to health systems data: curation and access to a whole system national cohort from NHS Scotland.

We present the design and implementation of a data curation framework to generate a large-scale clinical brain imaging dataset suitable for artificial intelligence (AI) enabled image analysis. The dataset is accessible through the Brain Health Data (BHD) initiative, which includes approximately 417,341 magnetic resonance imaging (MRI) and 846,077 computerized tomography (CT) head studies, linked e...

AI and knowledge driven computation of rock mass characteristic parameters across engineering projects.

With the continuous development of underground construction, represented by transportation tunnels, municipal utility tunnels and hydraulic tunnels, a...

The combined use of in vivo confocal microscopy and optical coherence tomography in monitoring corneal infections.

BACKGROUND/AIMS: Infectious keratitis (IK) is a major cause of blindness worldwide, requiring prompt diagnosis and management. This study evaluates th...

Deep-learning based image reconstruction enables reduced dose CT pulmonary angiography with non-inferior image quality.

To investigate whether a CT pulmonary angiography (CTPA) protocol with reduced radiation dose and deep-learning based image reconstruction (DLIR) is n...

The role of emerging technology in expanding regional anesthesia education and patient access.

PURPOSE OF REVIEW: The purpose of this article is to identify promising technologies that can enhance core regional anesthesia training, competence, a...

Artificial intelligence in inflammatory bowel disease: From current evidence, clinical translation, and the road to precision medicine.

Artificial intelligence (AI) is rapidly transforming healthcare, supporting disease management and enabling outcome prediction across multiple clinica...

Lipid MRI in plant science: principles and potential areas of application.

Magnetic resonance imaging (MRI), long established in medical diagnostics, offers powerful, non-invasive capabilities for visualizing physiological pr...

Development of a Computed Tomography Enterography Based Radiomics Model to Characterize Inflammation, Fibrosis and Smooth Muscle Thickening in Stricturing Crohn's Disease.

BACKGROUND AIMS: Computed tomography enterography (CTE) is a non-invasive cross-sectional imaging modality routinely used for diagnosis of Crohn's dis...

Hybrid-CMLP: Hybrid CNN-MLP Networks for Low-to-standard-dose PET Synthesis.

Positron Emission Tomography (PET) is a critical modality in medical imaging for detecting abnormalities and diagnosing diseases. However, the radiati...

Fully automated detection of pancreaticobiliary maljunction based on 3D magnetic resonance cholangiopancreatography using deep learning.

INTRODUCTION: Pancreaticobiliary maljunction (PBM) is closely related to biliary tract cancer. The early detection of PBM is crucial but challenging. ...

Postpartum ovarian vein thrombosis: plain CT with clinical indicators-diagnostic performance and clinical application.

BACKGROUND: Postpartum ovarian vein thrombosis (POVT) is a rare but serious condition, often presenting with nonspecific symptoms and potentially lead...

Scan-wise generalized PET denoising with contrastive adversarial learning.

Objective.Deep learning has significantly advanced low-count positron emission tomography (PET) denoising. However, models trained on specific distrib...

Developing an Advanced Deep Learning-based MR Image Framework for Brain Stroke Segmentation and Classification with Novel Activation Function.

Stroke is considered as one of the most prevalent causes of death and disability for the humans, although it is preventable and treatable. Earlier str...

Beyond 0.29±0.02 mm intrinsic spatial resolution based on monolithic crystals using convolutional neural network: a simulation study.

High spatial resolution is important for a Positron Emission Tomography (PET) system. Monolithic crystal based detectors are promising to deliver ...

Zero-shot performance of a general-purpose vision-language model for pediatric appendicitis diagnosis.

BACKGROUND: General-purpose vision-language models can analyze medical images without task-specific training, but their value for pediatric abdominal ...

The impact of a novel deep learning reconstruction algorithm on image quality in ultralow-dose CT: a quantitative phantom study.

OBJECTIVE: The aim of this study is to evaluate the performance of a novel deep learning image reconstruction (DLIR) algorithm in noise reduction, con...

Impact of middle meningeal artery embolization on neurological complications and discharge outcomes in chronic subdural hematoma: A machine learning-based comparative study.

INTRODUCTION: Chronic subdural hematoma (cSDH) predominantly affects older adults, often those with prior head trauma, anticoagulation therapy, or chr...

Deep learning-based biliary stent classification and transfer learning adaptation to an additional stent type.

OBJECTIVE: Accurate classification of previously deployed biliary stents is crucial for planning reintervention, yet conventional image interpretation...

The emerging role of coronary computed tomography for prevention of cardiovascular disease in asymptomatic individuals.

The persistent global rise in atherosclerotic cardiovascular disease (ASCVD) challenges the effectiveness of current risk factor-based algorithms in p...