Radiology

Blinded, randomized trial of sonographer versus AI cardiac function assessment.

Artificial intelligence (AI) has been developed for echocardiography, although it has not yet been tested with blinding and randomization. Here we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no outside funding) of AI versus sonographer initial assessment of left ventricular ejection fraction (LVEF) to evaluate the impact of AI in the interpret...

[Update: Small bowel diseases in computed tomography and magnetic resonance imaging].

CLINICAL/METHODICAL ISSUE: Radiological procedures play a crucial role in the diagnosis of small bowel disease. Due to a broad and quite nonspecific spectrum of symptoms, clinical evaluation is often difficult, and endoscopic procedures require significant manpower, are time-consuming and expensive. In contrast, radiologic imaging can provide important information about morphologic and functional ...

A Survey on Brain Effective Connectivity Network Learning.

Human brain effective connectivity characterizes the causal effects of neural activities among different brain regions. Studies of brain effective con...

A study on the optimal condition of ground truth area for liver tumor detection in ultrasound images using deep learning.

PURPOSE: In recent years, efforts to apply artificial intelligence (AI) to the medical field have been growing. In general, a vast amount of high-qual...

Investigating the robustness of a deep learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions.

Quantitative phase retrieval (QPR) in propagation-based x-ray phase contrast imaging of heterogeneous and structurally complicated objects is challeng...

High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning.

. Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing unit (GPU...

Robotics in interventional cardiology: a new era of safe and efficient procedures.

Review of Robot-Assisted HIFU Therapy.

This paper provides an overview of current robot-assisted high-intensity focused ultrasound (HIFU) systems for image-guided therapies. HIFU is a minim...

MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion.

Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of such images. As a method to mitigate such artifacts, denois...

Phase Contrast Image Restoration by Formulating Its Imaging Principle and Reversing the Formulation With Deep Neural Networks.

Phase contrast microscopy, as a noninvasive imaging technique, has been widely used to monitor the behavior of transparent cells without staining or a...

MLF-IOSC: Multi-Level Fusion Network With Independent Operation Search Cell for Low-Dose CT Denoising.

Computed tomography (CT) is widely used in clinical medicine, and low-dose CT (LDCT) has become popular to reduce potential patient harm during CT acq...

Attention Deficit Hyperactivity Disorder Classification Based on Deep Learning.

Attention Deficit Hyperactivity Disorder (ADHD) is a type of mental health disorder that can be seen from children to adults and affects patients' nor...

A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging.

PURPOSE: The purpose of this study was to develop and evaluate a deep learning model to detect bone marrow edema (BME) in sacroiliac joints and predic...

A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI.

BACKGROUND: Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regional lung ventilation with exceptional detail with...

Current State of Robotics in Interventional Radiology.

As a relatively new specialty with a minimally invasive nature, the field of interventional radiology is rapidly growing. Although the application of ...

BiTNet: Hybrid deep convolutional model for ultrasound image analysis of human biliary tract and its applications.

Certain life-threatening abnormalities, such as cholangiocarcinoma, in the human biliary tract are curable if detected at an early stage, and ultrason...

Aggregated micropatch-based deep learning neural network for ultrasonic diagnosis of cirrhosis.

Despite the advancements in the diagnosis of early-stage cirrhosis, the accuracy in the diagnosis using ultrasound is still challenging owing to the p...

Artificial Intelligence for Neuroimaging and Musculoskeletal Radiology: Overview of Current Commercial Algorithms.

There is a rapidly increasing number of artificial intelligence (AI) products cleared by the Food and Drug Administration (FDA) for quantification, id...

Emerging trends and research foci of deep learning in spine: bibliometric and visualization study.

As the cognition of spine develops, deep learning (DL) emerges as a powerful tool with tremendous potential for advancing research in this field. To p...

Dental image enhancement network for early diagnosis of oral dental disease.

Intelligent robotics and expert system applications in dentistry suffer from identification and detection problems due to the non-uniform brightness a...