Radiology

Percutaneous Robotics in Interventional Radiology.

The accuracy of the robotic device not only relies on a reproducible needle advancement, but also on the possibility to correct target movement at chosen checkpoints and to deviate from a linear to a nonlinear trajectory. We report our experience in using the robotic device for the insertion of trocar needles in CT guided procedures. The majority of procedures were targeted organ biopsies in the c...

Development of a novel combined nomogram model integrating deep learning radiomics to diagnose IgA nephropathy clinically.

This study aimed to develop and validate a combined nomogram model based on superb microvascular imaging (SMI)-based deep learning (DL), radiomics characteristics, and clinical factors for noninvasive differentiation between immunoglobulin A nephropathy (IgAN) and non-IgAN.We prospectively enrolled patients with chronic kidney disease who underwent renal biopsy from May 2022 to December 2022 and p...

Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast-Enhanced MRI.

BACKGROUND: Assessment of lymphovascular invasion (LVI) in breast cancer (BC) primarily relies on preoperative needle biopsy. There is an urgent need ...

Editorial for "Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast-Enhanced MRI".

Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis.

PURPOSE: This study aims to review deep learning applications for detecting head and neck cancer (HNC) using magnetic resonance imaging (MRI) and radi...

Retina Oculomics in Neurodegenerative Disease.

Ophthalmic biomarkers have long played a critical role in diagnosing and managing ocular diseases. Oculomics has emerged as a field that utilizes ocul...

Novel Solution for Using Neural Networks for Kidney Boundary Extraction in 2D Ultrasound Data.

: Kidney ultrasound (US) imaging is a significant imaging modality for evaluating kidney health and is essential for diagnosis, treatment, surgical in...

Radiology Residents' Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study.

BACKGROUND: Artificial intelligence (AI) is transforming various fields, with health care, especially diagnostic specialties such as radiology, being ...

Reduced versus standard dose contrast volume for contrast-enhanced abdominal CT in overweight and obese patients using photon counting detector technology vs. second-generation dual-source energy integrating detector CT.

PURPOSE: To compare image quality of contrast-enhanced abdominal-CT using 1st-generation Dual Source Photon-Counting Detector CT (DS-PCD-CT) versus 2n...

Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging.

Deep learning (DL) reconstruction techniques to improve MR image quality are becoming commercially available with the hope that they will be applicabl...

Pressure support ventilation in intensive care patients receiving prolonged invasive ventilation.

To our knowledge, the use and management of pressure support ventilation (PSV) in patients receiving prolonged (≥ 7 days) invasive mechanical ventila...

Deep Learning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation.

RATIONALE AND OBJECTIVES: Prostate MRI quality is essential in guiding prostate biopsies. However, assessment of MRI quality is subjective with variat...

Deep-learning segmentation method for optical coherence tomography angiography in ophthalmology.

PURPOSE: The optic disc and the macular are two major anatomical structures in the human eye. Optic discs are associated with the optic nerve. Macular...

Deep learning based diagnosis of Alzheimer's disease using FDG-PET images.

PURPOSE: The aim of this study is to develop a deep neural network to diagnosis Alzheimer's disease and categorize the stages of the disease using FDG...

Super-resolution biomedical imaging via reference-free statistical implicit neural representation.

Supervised deep learning for image super-resolution (SR) has limitations in biomedical imaging due to the lack of large amounts of low- and high-resol...

A multi-stage neural network approach for coronary 3D reconstruction from uncalibrated X-ray angiography images.

We present a multi-stage neural network approach for 3D reconstruction of coronary artery trees from uncalibrated 2D X-ray angiography images. This me...

Update on ethical aspects in clinical research: Addressing concerns in the development of new AI tools in radiology.

The analysis of ethical aspects in clinical research has always been a challenge and has required constant updates. In short, research ethics is the s...

Impact of deep learning on radiologists and radiology residents in detecting breast cancer on CT: a cross-vendor test study.

AIM: To investigate the effect of deep learning on the diagnostic performance of radiologists and radiology residents in detecting breast cancers on c...

The utility of automatic segmentation of kidney MRI in chronic kidney disease using a 3D convolutional neural network.

We developed a 3D convolutional neural network (CNN)-based automatic kidney segmentation method for patients with chronic kidney disease (CKD) using M...

Exploring the performance of implicit neural representations for brain image registration.

Pairwise image registration is a necessary prerequisite for brain image comparison and data integration in neuroscience and radiology. In this work, w...