Radiology

Evaluation of a deep learning-based reconstruction method for denoising and image enhancement of shoulder MRI in patients with shoulder pain.

OBJECTIVES: To evaluate the diagnostic performance of an automated reconstruction algorithm combinin...

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision.

Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (EC...

Contribution of Retzius-sparing robot-assisted radical prostatectomy to the mechanism of urinary continence as demonstrated by dynamic MRI.

Retzius-sparing robot-assisted radical prostatectomy (RARP) has been reported to exhibit better post...

Enhancing Nodule Biopsy Through Technology Integration.

Technology in navigating to peripheral pulmonary nodules has improved in recent years. The recent in...

Segmentation of the aorta in systolic phase from 4D flow MRI: multi-atlas vs. deep learning.

OBJECTIVE: In the management of the aortic aneurysm, 4D flow magnetic resonance Imaging provides val...

Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study.

BACKGROUND: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded ...

Predicting the prognosis of lower rectal cancer using preoperative magnetic resonance imaging with artificial intelligence.

BACKGROUND: There are various preoperative treatments that are useful for controlling local or dista...

Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study.

BACKGROUND: Post-processing and interpretation of coronary CT angiography (CCTA) imaging are time-co...

Ultrasound Volume Reconstruction From Freehand Scans Without Tracking.

Transrectal ultrasound is commonly used for guiding prostate cancer biopsy, where 3D ultrasound volu...

LN-Net: Perfusion Pattern-Guided Deep Learning for Lymph Node Metastasis Diagnosis Based on Contrast-Enhanced Ultrasound Videos.

OBJECTIVE: The blood flow in lymph nodes reflects important pathological features. However, most int...

Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging.

To establish a deep learning (DL) model in differentiating borderline ovarian tumor (BOT) from epith...

Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy.

PURPOSE: Minimally invasive treatments for renal carcinoma offer a low rate of complications and qui...

Artificial Intelligence in Radiology: Overview of Application Types, Design, and Challenges.

Artificial intelligence and fetal ultrasound biometry: Challenges and perspectives.

Cuttlefish eye-inspired artificial vision for high-quality imaging under uneven illumination conditions.

With the rise of mobile robotics, including self-driving automobiles and drones, developing artifici...

Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries.

Most artificial intelligence (AI) research and innovations have concentrated in high-income countrie...

Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy.

OBJECTIVES: The accurate prediction of post-hepatectomy early recurrence in patients with hepatocell...

Utilization of an attentive map to preserve anatomical features for training convolutional neural-network-based low-dose CT denoiser.

BACKGROUND: The purpose of a convolutional neural network (CNN)-based denoiser is to increase the di...

A Deep Learning Approach for Automated Bone Removal from Computed Tomography Angiography of the Brain.

Advanced visualization techniques such as maximum intensity projection (MIP) and volume rendering (V...

How scan parameter choice affects deep learning-based coronary artery disease assessment from computed tomography.

Recently, algorithms capable of assessing the severity of Coronary Artery Disease (CAD) in form of t...

[Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extensionDiretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI].

The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespre...