Radiology

Hemodynamic study of blood flow in the aorta during the interventional robot treatment using fluid-structure interaction.

An interventional robot is a means for vascular diagnosis and treatment, and it can perform dredging, releasing drug and operating. Normal hemodynamic indicators are a prerequisite for the application of interventional robots. The current hemodynamic research is limited to the absence of interventional devices or interventional devices in fixed positions. Considering the coupling effect of blood, ...

Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective.

Quantitative analysis of white matter fiber tracts from diffusion Magnetic Resonance Imaging (dMRI) data is of great significance in health and disease. For example, analysis of fiber tracts related to anatomically meaningful fiber bundles is highly demanded in pre-surgical and treatment planning, and the surgery outcome depends on accurate segmentation of the desired tracts. Currently, this proce...

Consistency of Artificial Intelligence (AI)-based Diagnostic Support Software in Short-term Digital Mammography Reimaging After Core Needle Biopsy.

To evaluate the consistency in the performance of Artificial Intelligence (AI)-based diagnostic support software in short-term digital mammography rei...

An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI.

Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been shown to positively affect patients' lives. To save ...

Automated deep learning auto-segmentation of air volumes for MRI-guided online adaptive radiation therapy of abdominal tumors.

. In the current MR-Linac online adaptive workflow, air regions on the MR images need to be manually delineated for abdominal targets, and then overri...

Bidirectional feature matching based on deep pairwise contrastive learning for multiparametric MRI image synthesis.

Multi-parametric MR image synthesis is an effective approach for several clinical applications where specific modalities may be unavailable to reach a...

Deep Learning Versus Neurologists: Functional Outcome Prediction in LVO Stroke Patients Undergoing Mechanical Thrombectomy.

BACKGROUND: Despite evolving treatments, functional recovery in patients with large vessel occlusion stroke remains variable and outcome prediction ch...

CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study.

This study aimed to develop a machine learning model for predicting brain arteriovenous malformation (bAVM) rupture using a combination of traditional...

Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models.

PURPOSE: Osteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. The...

Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography.

BACKGROUND: Invasive coronary angiography (ICA) is a primary imaging modality that visualizes the lumen area of coronary arteries for diagnosis and in...

Robot-assisted spinal augmentation procedures: is it worth the increased effort?

PURPOSE: Spinal augmentation procedures (SAP) are standard procedures for vertebral compression fractures. Often, SAPs are carried out in a minimally ...

Revolution of echocardiographic reporting: the new era of artificial intelligence and natural language processing.

Artificial intelligence (AI) has been making a significant impact on cardiovascular imaging, transforming everything from data capture to report gener...

Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears.

BACKGROUND: Accurately diagnosing supraspinatus tears based on magnetic resonance imaging (MRI) is challenging and time-combusting due to the experien...

The clinical application of neuro-robot in the resection of epileptic foci: a novel method assisting epilepsy surgery.

During surgery for foci-related epilepsy, neurosurgeons face significant difficulties in identifying and resecting MRI-negative or deep-seated epilept...

Artificial intelligence-aided optical imaging for cancer theranostics.

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-makin...

Review on deep learning fetal brain segmentation from Magnetic Resonance images.

Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal ima...

A Feasibility Study on Deep Learning Reconstruction to Improve Image Quality With PROPELLER Acquisition in the Setting of T2-Weighted Gynecologic Pelvic Magnetic Resonance Imaging.

OBJECTIVES: Evaluate deep learning (DL) to improve the image quality of the PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced R...

Effect of Deep Learning Reconstruction on Evaluating Cervical Spinal Canal Stenosis With Computed Tomography.

OBJECTIVE: Magnetic resonance imaging (MRI) is commonly used to evaluate cervical spinal canal stenosis; however, some patients are ineligible for MRI...

Evaluation of automated detection of head position on lateral cephalometric radiographs based on deep learning techniques.

BACKGROUND: Lateral cephalometric radiograph (LCR) is crucial to diagnosis and treatment planning of maxillofacial diseases, but inappropriate head po...

Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV.

PURPOSE: Hepatic steatosis (fatty liver disease) affects 25% of the world's population, particularly people with HIV (PWH). Pharmacoepidemiologic stud...