Radiology

Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance.

Background Automation bias (the propensity for humans to favor suggestions from automated decision-m...

Artificial intelligence using a deep learning versus expert computed tomography human reading in calcium score and coronary artery calcium data and reporting system classification.

BACKGROUND: Artificial intelligence (AI) applied to cardiac imaging may provide improved processing,...

Establishment and Evaluation of Intelligent Diagnostic Model for Ophthalmic Ultrasound Images Based on Deep Learning.

OBJECTIVE: The goal of the work described here was to construct a deep learning-based intelligent di...

The potential for photon-counting computed tomography and deep learning to reduce radiation dose in paediatric radiology: reply to Nagy et al.

ChatGPT's Potential Role in Interventional Radiology.

Robotic-navigated assistance in spine surgery.

The aim of this study was to assess the accuracy of pedicle screw placement, as well as intraoperati...

Real-Time Automatic Assisted Detection of Uterine Fibroid in Ultrasound Images Using a Deep Learning Detector.

OBJECTIVE: Uterine smooth muscle hyperplasia causes a tumor called a uterine fibroid. With an incide...

Physics-informed neural networks for transcranial ultrasound wave propagation.

Transcranial ultrasound imaging has been playing an increasingly important role in the non-invasive ...

Prediction of total knee replacement using deep learning analysis of knee MRI.

Current methods for assessing knee osteoarthritis (OA) do not provide comprehensive information to m...

Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe.

Optical coherence tomography (OCT), an interferometric imaging technique, provides non-invasive, hig...

Non-invasively Discriminating the Pathological Subtypes of Non-small Cell Lung Cancer with Pretreatment F-FDG PET/CT Using Deep Learning.

RATIONALE AND OBJECTIVES: To develop an end-to-end deep learning (DL) model for non-invasively predi...

Artificial intelligence assisted whole organ pancreatic fat estimation on magnetic resonance imaging and correlation with pancreas attenuation on computed tomography.

BACKGROUND: Fatty pancreas is associated with inflammatory and neoplastic pancreatic diseases. Magne...

[Feasibility Study of the Prediction of Radiologist's Instructions with the Bi-LSTM Model Trained with Descriptions of MR Imaging Order-statement].

PURPOSE: Magnetic resonance (MR) images provide essential diagnostic information; however, it is als...

A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training.

Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field where researc...

Adrenal lesion classification with abdomen caps and the effect of ROI size.

Accurate classification of adrenal lesions on magnetic resonance (MR) images are very important for ...

LAVA HyperSense and deep-learning reconstruction for near-isotropic (3D) enhanced magnetic resonance enterography in patients with Crohn's disease: utility in noise reduction and image quality improvement.

PURPOSE: This study aimed to compare near-isotropic contrast-enhanced T1-weighted (CE-T1W) magnetic ...

Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.

. Radiation therapy for head and neck (H&N) cancer relies on accurate segmentation of the primary tu...

The potential for clinical application of automatic quantification of olfactory bulb volume in MRI scans using convolutional neural networks.

The olfactory bulbs (OBs) play a key role in olfactory processing; their volume is important for dia...

Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias.

Personalized, image-based computational heart modelling is a powerful technology that can be used to...

Deep learning for classification of thyroid nodules on ultrasound: validation on an independent dataset.

OBJECTIVES: The purpose is to apply a previously validated deep learning algorithm to a new thyroid ...

Physics-Informed Deep-Learning For Elasticity: Forward, Inverse, and Mixed Problems.

Elastography is a medical imaging technique used to measure the elasticity of tissues by comparing u...