Radiology

Assessment of Deep Learning-Based Triage Application for Acute Ischemic Stroke on Brain MRI in the ER.

RATIONALE AND OBJECTIVES: To assess a deep learning application (DLA) for acute ischemic stroke (AIS) detection on brain magnetic resonance imaging (MRI) in the emergency room (ER) and the effect of T2-weighted imaging (T2WI) on its performance.

Spontaneous brain activity in patients with central retinal artery occlusion: a resting-state functional MRI study using machine learning.

Central retinal artery occlusion (CRAO) is a serious eye condition that poses a risk to vision, resulting from the blockage of the central retinal artery. Because of the anatomical connection between the ocular artery, which derives from the internal carotid artery, and the anterior middle cerebral artery, hemodynamic alterations and sudden vision loss associated with CRAO may impact brain functio...

Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study.

BACKGROUND: We compared magnetic resonance imaging (MRI) turbo spin-echo images reconstructed using a deep learning technique (TSE-DL) with standard t...

Artificial intelligence in dermatopathology: Updates, strengths, and challenges.

Artificial intelligence (AI) has evolved to become a significant force in various domains, including medicine. We explore the role of AI in pathology,...

Prospective Evaluation of Artificial Intelligence Triage of Incidental Pulmonary Emboli on Contrast-Enhanced CT Examinations of the Chest or Abdomen.

Artificial intelligence (AI) algorithms improved detection of incidental pulmonary embolism (IPE) on contrast-enhanced CT (CECT) examinations in retr...

Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detect...

Deep learning-based automated detection and segmentation of bone and traumatic bone marrow lesions from MRI following an acute ACL tear.

INTRODUCTION: Traumatic bone marrow lesions (BML) are frequently identified on knee MRI scans in patients following an acute full-thickness, complete ...

[Development of a Deep Learning Model for Judging Late Gadolinium-enhancement in Cardiac MRI].

PURPOSE: To verify the usefulness of a deep learning model for determining the presence or absence of contrast-enhanced myocardium in late gadolinium-...

Automated classification of multiple ophthalmic diseases using ultrasound images by deep learning.

BACKGROUND: Ultrasound imaging is suitable for detecting and diagnosing ophthalmic abnormalities. However, a shortage of experienced sonographers and ...

Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.

BACKGROUND: Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of...

Protocol for performing deep learning-based fundus fluorescein angiography image analysis with classification and segmentation tasks.

Fundus fluorescein angiography (FFA) examinations are widely used in the evaluation of fundus disease conditions to facilitate further treatment sugge...

Enhancing Breast Cancer Diagnosis: A Nomogram Model Integrating AI Ultrasound and Clinical Factors.

PURPOSE: A novel nomogram incorporating artificial intelligence (AI) and clinical features for enhanced ultrasound prediction of benign and malignant ...

Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology.

We present open-source tools for three-dimensional (3D) analysis of photographs of dissected slices of human brains, which are routinely acquired in b...

Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges.

This article reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes i...

A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma.

BACKGROUND: To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging-based radiomics data with...

Focused ultrasound enables selective actuation and Newton-level force output of untethered soft robots.

Untethered miniature soft robots have significant application potentials in biomedical and industrial fields due to their space accessibility and safe...

Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model.

OBJECTIVES: This study was designed to explore and validate the value of different machine learning models based on ultrasound image-omics features in...

Machine Learning Detects Symptomatic Plaques in Patients With Carotid Atherosclerosis on CT Angiography.

BACKGROUND: This study aimed to develop and validate a computed tomography angiography based machine learning model that uses plaque composition data ...

Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging.

Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac ma...

Smart scanning: automatic detection of superficially located lymph nodes using ultrasound - initial results.

Over the last few years, there has been an increasing focus on integrating artificial intelligence (AI) into existing imaging systems. This also appli...