Radiology

Detection of focal cortical dysplasia: Development and multicentric evaluation of artificial intelligence models.

OBJECTIVE: Focal cortical dysplasia (FCD) is a common cause of drug-resistant focal epilepsy but can be challenging to detect visually on magnetic resonance imaging. Three artificial intelligence models for automated FCD detection are publicly available (MAP18, deepFCD, MELD) but have only been compared on single-center data. Our first objective is to compare them on independent multicenter test d...

Computerized classification method for significant coronary artery stenosis on whole-heart coronary MRA using 3D convolutional neural networks with attention mechanisms.

This study aims to develop a computerized classification method for significant coronary artery stenosis on whole-heart coronary magnetic resonance angiography (WHCMRA) images using a 3D convolutional neural network (3D-CNN) with attention mechanisms. The dataset included 951 segments from WHCMRA images of 75 patients who underwent both WHCMRA and invasive coronary angiography (ICA). Forty-two seg...

Healthy core: Harmonizing brain MRI for supporting multicenter migraine classification studies.

Multicenter and multi-scanner imaging studies may be necessary to ensure sufficiently large sample sizes for developing accurate predictive models. Ho...

Radiologists' perceptions and readiness for integrating artificial intelligence in diagnostic imaging: A survey-based study.

Artificial intelligence (AI) is revolutionizing diagnostic imaging, enhancing precision, speed and efficiency. This study explored radiologists' perce...

Quantitative imaging for early detection and risk stratification of cardiovascular disease using 4D flow MRI and arterial spin labelling.

Heart failure (HF) significantly burdens global healthcare, necessitating early detection and precise risk stratification. Advanced imaging techniques...

AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication.

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the United States, largely due to its poor five-year su...

Multi-scale multi-object semi-supervised consistency learning for ultrasound image segmentation.

Manual annotation of ultrasound images relies on expert knowledge and requires significant time and financial resources. Semi-supervised learning (SSL...

Noninvasive machine-learning models for the detection of lesion-specific ischemia in patients with stable angina with intermediate stenosis severity on coronary CT angiography.

This study proposed noninvasive machine-learning models for the detection of lesion-specific ischemia (LSI) in patients with stable angina with interm...

Sub-millimeter fiberscopic robot with integrated maneuvering, imaging, and biomedical operation abilities.

Small-scale continuum robots hold promise for interventional diagnosis and treatment, yet existing models struggle to achieve small size, precise stee...

Deep-learning-based extraction of circle of Willis topology with anatomical priors.

The circle of Willis (CoW) is a circular arrangement of arteries in the human brain, exhibiting significant anatomical variability. The CoW is extensi...

A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks.

Multiple sclerosis (MS) is a severe brain disease that permanently destroys brain cells, impacting vision, balance, muscle control, and daily activity...

Generation of deep learning based virtual non-contrast CT using dual-layer dual-energy CT and its application to planning CT for radiotherapy.

This paper presents a novel approach for generating virtual non-contrast planning computed tomography (VNC-pCT) images from contrast-enhanced planning...

Problems of magnetic resonance diagnosis for gastric-type mucin-positive cervical lesions of the uterus and its solutions using artificial intelligence.

PURPOSE: To reveal problems of magnetic resonance imaging (MRI) for diagnosing gastric-type mucin-positive (GMPLs) and gastric-type mucin-negative (GM...

Leveraging domain knowledge for synthetic ultrasound image generation: a novel approach to rare disease AI detection.

PURPOSE: This study explores the use of deep generative models to create synthetic ultrasound images for the detection of hemarthrosis in hemophilia p...

Machine learning-based prognostic modeling in gallbladder cancer using clinical data and pre-treatment [F]-FDG-PET-radiomic features.

OBJECTIVES: This study evaluates the effectiveness of machine learning (ML) models that incorporate clinical and 2-deoxy-2-[F]fluoro-D-glucose ([F]-FD...

Attention incorporated network for sharing low-rank, image and k-space information during MR image reconstruction to achieve single breath-hold cardiac Cine imaging.

Cardiac Cine Magnetic Resonance Imaging (MRI) provides an accurate assessment of heart morphology and function in clinical practice. However, MRI requ...

Detecting the articular disk in magnetic resonance images of the temporomandibular joint using YOLO series.

The purpose of this study was to construct an artificial intelligence object detection model to detect the articular disk from temporomandibular joint...

Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.

BACKGROUND: Meningioma, the most common primary brain tumor, presents significant challenges in MRI-based diagnosis and treatment planning due to its ...

A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.

Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of...

SHIVA-CMB: a deep-learning-based robust cerebral microbleed segmentation tool trained on multi-source T2*GRE- and susceptibility-weighted MRI.

Cerebral microbleeds (CMB) represent a feature of cerebral small vessel disease (cSVD), a prominent vascular contributor to age-related cognitive decl...