Radiology

An Automated Heart Shunt Recognition Pipeline Using Deep Neural Networks.

Automated recognition of heart shunts using saline contrast transthoracic echocardiography (SC-TTE) ...

Stop moving: MR motion correction as an opportunity for artificial intelligence.

Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously d...

Deep learning model for the detection of prostate cancer and classification of clinically significant disease using multiparametric MRI in comparison to PI-RADs score.

BACKGROUND: The Prostate Imaging Reporting and Data System (PI-RADS) is an established reporting sch...

Analgesic effect of the ultrasound-guided thoracolumbar paravertebral block in patients undergoing robot-assisted laparoscopic nephrectomy: a randomized controlled trial.

BACKGROUND: Paravertebral block has similar effect as epidural anesthesia, and has good somatic and ...

Fully-automated multi-organ segmentation tool applicable to both non-contrast and post-contrast abdominal CT: deep learning algorithm developed using dual-energy CT images.

A novel 3D nnU-Net-based of algorithm was developed for fully-automated multi-organ segmentation in ...

Auto-segmentation of Adult-Type Diffuse Gliomas: Comparison of Transfer Learning-Based Convolutional Neural Network Model vs. Radiologists.

Segmentation of glioma is crucial for quantitative brain tumor assessment, to guide therapeutic rese...

Segmentation of liver and liver lesions using deep learning.

Segmentation of organs and lesions could be employed for the express purpose of dosimetry in nuclear...

Generative pretrained transformer-4, an artificial intelligence text predictive model, has a high capability for passing novel written radiology exam questions.

PURPOSE: AI-image interpretation, through convolutional neural networks, shows increasing capability...

Automatic generation of conclusions from neuroradiology MRI reports through natural language processing.

PURPOSE: The conclusion section of a radiology report is crucial for summarizing the primary radiolo...

Magnetic soft microfiberbots for robotic embolization.

Cerebral aneurysms and brain tumors are leading life-threatening diseases worldwide. By deliberately...

Tracking and navigation of a microswarm under laser speckle contrast imaging for targeted delivery.

Micro/nanorobotic swarms consisting of numerous tiny building blocks show great potential in biomedi...

Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals?

PURPOSE: To determine how radiology, nuclear medicine, and medical imaging journals encourage and ma...

A primer and overview of the role of artificial intelligence in oral and maxillofacial radiology.

Robust EMI elimination for RF shielding-free MRI through deep learning direct MR signal prediction.

PURPOSE: To develop a new electromagnetic interference (EMI) elimination strategy for RF shielding-f...

Machine Learning and Bias in Medical Imaging: Opportunities and Challenges.

Bias in health care has been well documented and results in disparate and worsened outcomes for at-r...

Usefulness of pituitary high-resolution 3D MRI with deep-learning-based reconstruction for perioperative evaluation of pituitary adenomas.

PURPOSE: To evaluate the diagnostic value of T1-weighted 3D fast spin-echo sequence (CUBE) with deep...

Real-Time Tissue Classification Using a Novel Optical Needle Probe for Biopsy.

Core needle biopsy is a part of the histopathological process, which is required for cancerous tissu...

Greater accuracy of radiomics compared to deep learning to discriminate normal subjects from patients with dementia: a whole brain 18FDG PET analysis.

METHODS: 18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 he...

Exploring the potential of ChatGPT as an adjunct for generating diagnosis based on chief complaint and cone beam CT radiologic findings.

AIM: This study aimed to assess the performance of OpenAI's ChatGPT in generating diagnosis based on...

A model-based direct inversion network (MDIN) for dual spectral computed tomography.

. Dual spectral computed tomography (DSCT) is a very challenging problem in the field of imaging. Du...

Technical note: Minimizing CIED artifacts on a 0.35 T MRI-Linac using deep learning.

BACKGROUND: Artifacts from implantable cardioverter defibrillators (ICDs) are a challenge to magneti...