Radiology

Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy.

BACKGROUND: Lenvatinib plus PD-1 inhibitors and interventional (LPI) therapy have demonstrated promi...

Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI.

Meniscal injury is a common cause of knee joint pain and a precursor to knee osteoarthritis (KOA). T...

Multi-parameter MRI-Based Machine Learning Model to Evaluate the Efficacy of STA-MCA Bypass Surgery for Moyamoya Disease: A Pilot Study.

Superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery represents the primary t...

Diagnostic Accuracy of Ultra-Low Dose CT Compared to Standard Dose CT for Identification of Fresh Rib Fractures by Deep Learning Algorithm.

The present study aimed to evaluate the diagnostic accuracy of ultra-low dose computed tomography (U...

Achieving high accuracy in meniscus tear detection using advanced deep learning models with a relatively small data set.

PURPOSE: This study aims to evaluate the effectiveness of advanced deep learning models, specificall...

Deep learning reconstruction for zero echo time lung magnetic resonance imaging: impact on image quality and lesion detection.

AIMS: This study aimed to examine the impact of deep-learning reconstruction (DLR) on zero echo time...

Prospective and multi-reader evaluation of deep learning reconstruction-based accelerated rectal MRI: image quality, diagnostic performance, and reading time.

OBJECTIVES: To evaluate deep learning reconstruction (DLR)-based accelerated rectal magnetic resonan...

Enhancing Prostate Cancer Diagnosis: Artificial Intelligence-Driven Virtual Biopsy for Optimal Magnetic Resonance Imaging-Targeted Biopsy Approach and Gleason Grading Strategy.

An optimal approach to magnetic resonance imaging fusion targeted prostate biopsy (PBx) remains uncl...

Fully automated segmentation and volumetric measurement of ocular adnexal lymphoma by deep learning-based self-configuring nnU-net on multi-sequence MRI: a multi-center study.

PURPOSE: To evaluate nnU-net's performance in automatically segmenting and volumetrically measuring ...

Accelerated cardiac magnetic resonance imaging using deep learning for volumetric assessment in children.

BACKGROUND: Ventricular volumetry using a short-axis stack of two-dimensional (D) cine balanced stea...

Editorial Comment: Artificial Intelligence Triage Varies by Radiology Practice.

XAI-Based Assessment of the AMURA Model for Detecting Amyloid-β and Tau Microstructural Signatures in Alzheimer's Disease.

Brain microstructural changes already occur in the earliest phases of Alzheimer's disease (AD) as ev...

Streak artefact removal in x-ray dark-field computed tomography using a convolutional neural network.

BACKGROUND: Computed tomography (CT) relies on the attenuation of x-rays, and is, hence, of limited ...

An analytical review on the use of artificial intelligence and machine learning in diagnosis, prediction, and risk factor analysis of multiple sclerosis.

Medical research offers potential for disease prediction, like Multiple Sclerosis (MS). This neurolo...

[OCT biomarkers in diabetic maculopathy and artificial intelligence].

Diabetes mellitus is a chronic disease the microvascular complications of which include diabetic ret...

Current status and future directions in artificial intelligence for nuclear cardiology.

INTRODUCTION: Myocardial perfusion imaging (MPI) is one of the most commonly ordered cardiac imaging...

Automatically Detecting Pancreatic Cysts in Autosomal Dominant Polycystic Kidney Disease on MRI Using Deep Learning.

BACKGROUND: Pancreatic cysts in autosomal dominant polycystic kidney disease (ADPKD) correlate with ...

Explainable machine learning on baseline MRI predicts multiple sclerosis trajectory descriptors.

Multiple sclerosis (MS) is a multifaceted neurological condition characterized by challenges in time...

Association between myosteatosis and impaired glucose metabolism: A deep learning whole-body magnetic resonance imaging population phenotyping approach.

BACKGROUND: There is increasing evidence that myosteatosis, which is currently not assessed in clini...

A deep learning anthropomorphic model observer for a detection task in PET.

BACKGROUND: Lesion detection is one of the most important clinical tasks in positron emission tomogr...

Unsupervised shape-and-texture-based generative adversarial tuning of pre-trained networks for carotid segmentation from 3D ultrasound images.

BACKGROUND: Vessel-wall volume and localized three-dimensional ultrasound (3DUS) metrics are sensiti...