Radiology

Point-of-care ultrasound in obstetric anesthesia clinical practice.

PURPOSE OF THE REVIEW: Point-of-care ultrasound (POCUS) is increasingly recognized as a valuable tool in obstetric anesthesia. This review synthesizes key studies and reviews published within the last 2 years on its application in clinical practice with relevant supporting literature.

Advances in MRI optic nerve segmentation.

Understanding optic nerve structure and monitoring changes within it can provide insights into neurodegenerative diseases like multiple sclerosis, in which optic nerves are often damaged by inflammatory episodes of optic neuritis. Over the past decades, interest in the optic nerve has increased, particularly with advances in magnetic resonance technology and the advent of deep learning solutions. ...

Aligning, Autoencoding and Prompting Large Language Models for Novel Disease Reporting.

Given radiology images, automatic radiology report generation aims to produce informative text that reports diseases. It can benefit current clinical ...

A Multitask CNN for Near-Infrared Probe: Enhanced Real-Time Breast Cancer Imaging.

The early detection of breast cancer, particularly in dense breast tissues, faces significant challenges with traditional imaging techniques such as m...

Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges.

The intricate imaging structures, artifacts, and noise present in ultrasound images and videos pose significant challenges for accurate segmentation. ...

NeuroNasal: Advanced AI-Driven Self-Supervised Learning Approach for Enhanced Sinonasal Pathology Detection.

Sinus diseases are inflammations or infections of the sinuses that significantly impact patient quality of life. They cause nasal congestion, facial p...

A novel intelligent grade classification architecture for Patent Foramen Ovale by Contrast Transthoracic Echocardiography based on deep learning.

Patent foramen ovale (PFO) is one of the main causes of ischemic stroke. Due to the complex characteristics of contrast transthoracic echocardiography...

Use of Artificial Intelligence and Machine Learning in Critical Care Ultrasound.

This article explores the transformative potential of artificial intelligence (AI) in critical care ultrasound AI technologies, notably deep learning ...

Workload of diagnostic radiologists in the foreseeable future based on recent (2024) scientific advances: Updated growth expectations.

PURPOSE: To assess the expected impact of the 2024 medical imaging literature on the workload of diagnostic radiologists.

Prediction of Seronegative Hashimoto's thyroiditis using machine learning models based on ultrasound radiomics: a multicenter study.

BACKGROUND: Seronegative Hashimoto's thyroiditis is often underdiagnosed due to the lack of antibody markers. Combining ultrasound radiomics with mach...

Federated learning with integrated attention multiscale model for brain tumor segmentation.

Brain tumors are an extremely deadly condition and the growth of abnormal cells that have formed inside the brain causes the illness. According to stu...

Ensemble deep learning for Alzheimer's disease diagnosis using MRI: Integrating features from VGG16, MobileNet, and InceptionResNetV2 models.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain...

Development and Validation of an Algorithm for Segmentation of the Prostate and its Zones from Three-dimensional Transrectal Multiparametric Ultrasound Images.

BACKGROUND AND OBJECTIVE: Multiparametric ultrasound (mpUS) is being investigated as an alternative to magnetic resonance imaging (MRI) for detection ...

Using artificial intelligence system for assisting the classification of breast ultrasound glandular tissue components in dense breast tissue.

To investigate the potential of employing artificial intelligence (AI) -driven breast ultrasound analysis models for the classification of glandular t...

Complex-valued neural networks to speed-up MR thermometry during hyperthermia using Fourier PD and PDUNet.

Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tu...

Longitudinal study on the impact of short-term radiological interpretation training on resting-state brain network hubs.

Radiological expertise develops through extensive experience in specific imaging modalities. While previous research has focused on long-term learning...

Unsupervised brain MRI tumour segmentation via two-stage image synthesis.

Deep learning shows promise in automated brain tumour segmentation, but it depends on costly expert annotations. Recent advances in unsupervised learn...

Coronary p-Graph: Automatic classification and localization of coronary artery stenosis from Cardiac CTA using DSA-based annotations.

Coronary artery disease (CAD) is a prevalent cardiovascular condition with profound health implications. Digital subtraction angiography (DSA) remains...

FreqYOLO: A uterine disease detection network based on local and global frequency feature learning.

Leiomyomas (LM) and adenomyosis (AM) are common gynecological diseases with high incidence rates and an increasing trend of affecting younger women. A...

Implementation of a national AI technology program on cardiovascular outcomes and the health system.

Coronary artery disease (CAD) is a major cause of ill health and death worldwide. Coronary computed tomographic angiography (CCTA) is the first-line i...