Radiology

An AI-Based Solution for Denoising Fast-Acquisition [18F]FDG PET: Clinical Feasibility and Quantitative Assessment.

Benefits in patient comfort, efficiency, and sustainability can come from reducing positron emission tomography (PET) scan's acquisition duration. This study assesses the clinical adequacy of restoring fast-acquisition 18F-fluorodeoxyglucose ([18F]FDG) PET to its standard-of-care image quality through deep-learning-based (DL) methods. Fast and standard whole-body [18F]FDG PET acquisitions of 117 o...

Comparison of the Diagnostic Utility of Computed Tomography Angiography Head With and Without 3-Dimensional Volume-Rendered Images for Aneurysm Detection in Subarachnoid Hemorrhage Patients Versus Digital Subtraction Angiography.

BACKGROUND AND OBJECTIVES: Generating computed tomography (CT) angiography (CTA) 3-dimensional (3D) volume-rendered (3DVR) images can be time consuming without specialized technical staff or artificial intelligence solutions. However, their role in aneurysm detection in patients with subarachnoid hemorrhage is not known. Our aim was to assess the diagnostic utility of 64-detector row CTA with 3DVR...

Ultra-Low-Dose CTPA Using Sparse Sampling CT Combined with the U-Net for Deep Learning-Based Artifact Reduction: An Exploratory Study.

This retrospective study evaluates U-Net-based artifact reduction for dose-reduced sparse-sampling CT (SpSCT) in terms of image quality and diagnostic...

Robust Quantification of Affected Brain Volume from Computed Tomography Perfusion: A Hybrid Approach Combining Deep Learning and Singular Value Decomposition.

Volumetric estimation of affected brain volumes using computed tomography perfusion (CTP) is crucial in the management of acute ischemic stroke (AIS) ...

Bias in deep learning-based image quality assessments of T2-weighted imaging in prostate MRI.

OBJECTIVES: To determine whether deep learning (DL)-based image quality (IQ) assessment of T2-weighted images (T2WI) could be biased by the presence o...

Evaluating the diagnostic accuracy of AI in ischemic and hemorrhagic stroke: A comprehensive meta-analysis.

Stroke poses a significant health challenge, with ischemic and hemorrhagic subtypes requiring timely and accurate diagnosis for effective management. ...

AlzhiNet: Traversing from 2D-CNN to 3D-CNN, Towards Early Detection and Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with increasing prevalence among the ageing population, necessitating early and a...

Digital versus analogue PET in parathyroid imaging: comparison of PET metrics and machine learning-based characterisation of hyperfunctioning lesions (the DIGI-PET study).

PURPOSE: To compare PET-derived metrics between digital and analogue PET/CT in hyperparathyroidism, and to assess whether machine learning (ML) applie...

Spatial imaging features derived from SUVmax location in resectable NSCLC are associated with tumor aggressiveness.

PURPOSE: Accurate non-invasive prediction of histopathologic invasiveness and recurrence risk remains a clinical challenge in resectable non-small cel...

Attention-based deep learning network for predicting World Health Organization meningioma grade and Ki-67 expression based on magnetic resonance imaging.

OBJECTIVES: Preoperative assessment of World Health Organization (WHO) meningioma grading and Ki-67 expression is crucial for treatment strategies. We...

Artificial Intelligence in Anterior Cruciate Ligament Tear Diagnosis: A Bibliometric Analysis of the 50 Most Cited Studies.

INTRODUCTION: Since the 2000s, artificial intelligence (AI) publications in medicine have surged, particularly in orthopaedics and radiology. A key ar...

Advanced liver fibrosis detection using a two-stage deep learning approach on standard T2-weighted MRI.

OBJECTIVES: To develop and validate a deep learning model for automated detection of advanced liver fibrosis using standard T2-weighted MRI. METHODS: ...

Machine Learning in Venous Thromboembolism-Why and What Next?

Venous thromboembolism (VTE) remains a leading cause of cardiovascular morbidity and mortality, despite advances in imaging and anticoagulation. VTE a...

Automating the Referral of Bone Metastases Patients With and Without the Use of Large Language Models.

BACKGROUND AND OBJECTIVES: Bone metastases, affecting more than 4.8% of patients with cancer annually, and particularly spinal metastases require urge...

An Adaptive Multi-Stage and Adjacent-Level Feature Integration Network for Brain Tumor Image Segmentation.

The segmentation of brain tumor magnetic resonance imaging (MRI) plays a crucial role in assisting diagnosis, treatment planning, and disease progress...

Exploring Radiologists' Use of AI Chatbots for Assistance in Image Interpretation: Patterns of Use and Trust Evaluation.

This study investigated radiologists' perceptions of AI-generated, patient-friendly radiology reports across three modalities: MRI, CT, and mammogram/...

Comparative analysis of tumor and mesorectum radiomics in predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.

PURPOSE: Neoadjuvant chemoradiotherapy (CRT) is known to increase sphincter preservation rates and decrease the risk of postoperative recurrence in pa...

Non-invasive prediction of the secondary enucleation risk in uveal melanoma based on pretreatment CT and MRI prior to stereotactic radiotherapy.

PURPOSE: The aim of this study was to develop a radiomic model to non-invasively predict the risk of secondary enucleation (SE) in patients with uveal...

Development and validation of a transformer-based deep learning model for predicting distant metastasis in non-small cell lung cancer using 18FDG PET/CT images.

BACKGROUND: This study aimed to develop and validate a hybrid deep learning (DL) model that integrates convolutional neural network (CNN) and vision t...

A novel network for resolving subjective masking differences and accurate thyroid nodule diagnosis.

BACKGROUND: Over the past three decades, there has been a significant increase in the incidence of thyroid cancer. Ultrasound serves as a non-invasive...