Radiology

Beyond benchmarks of IUGC: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos.

A significant proportion (45%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, particularly prevalent in low- and middle-income countries. Intrapartum biometry plays a crucial role in monitoring labor progress. However, the routine use of ultrasound in resource-limited settings is hindered by a shortage of trained sonographers. To tackle this issue, the Int...

HyCoSwin-PD: An explainable hybrid ConvNeXtV2-Swin transformer framework for Parkinson's disease detection from neuroimaging.

Accurate detection of Parkinson's disease (PD) from structural MRI remains a significant challenge due to the diffuse and heterogeneous nature of PD-related neuroanatomical alterations. This study introduces HyCoSwin-PD, an advanced hybrid deep learning framework that integrates ConvNeXt-V2 and Swin Transformer to jointly model fine-grained local morphology and hierarchical global context. ConvNeX...

Comparative study of different artificial intelligence (AI)-assisted compressed sensing factors in inner ear heavily T2-weighted imaging.

AIM: Artificial intelligence (AI)-assisted compressed sensing (ACS) is a cutting-edge magnetic resonance imaging (MRI) acceleration technique based on...

ROBUST-MIPS: A Combined Skeletal Pose and Instance Segmentation Dataset for Laparoscopic Surgical Instruments.

Localisation of surgical tools constitutes a foundational building block for computer-assisted interventional technologies. Works in this field typica...

Simulation of the Metabolic Response to an Interventional Study with New Healthy Beverages by Machine-Learning Regression.

The present study proposes a methodology to emulate an interventional trial by employing machine-learning (ML) models. A maqui-citrus beverage is used...

Super-resolution deep learning reconstruction enhances visualization of cerebral aneurysms on magnetic resonance angiography.

A multimodal feature disentanglement model for lymphadenopathy diagnosis based on BUS and CDFI ultrasound videos: a retrospective, prospective, multicenter study.

OBJECTIVES: This study developed and validated a deep learning model for diagnosing lymphadenopathy (LA) using B-mode ultrasound (BUS) and color Doppl...

MyoClass: A modular multimodal auto-classification system for myocardial tissue characterization.

Differentiating myocarditis from myocardial infarction (MI) using Cardiac Magnetic Resonance (CMR) imaging remains a significant clinical challenge du...

Certainty Language Use in Pediatric Radiology: A Single-Institution Analysis.

BACKGROUND: Radiologists often employ diagnostic certainty phrases (DCPs) to convey levels of confidence in imaging interpretations. Prior research in...

Dose reduction for synaptic density PET imaging in Parkinson's disease.

OBJECTIVE: Dynamic PET imaging with 11C-UCB-J enables in vivo quantification of synaptic vesicle glycoprotein 2A (SV2A), with prior reports of lower s...

Use of artificial intelligence and radiomics for diagnosis and prediction of osteoporotic fractures: a systematic review.

AIM: Osteoporosis (OP) is a prevalent metabolic bone disease causing millions of fractures annually, leading to significant healthcare and economic bu...

Epilepsy: Modern Treatment Approaches, Clinical Aspects, and Patent Status.

Epilepsy is a chronic neurological disorder causing recurrent seizures. Improved diagnosis and management, including high-resolution imaging, genetic ...

Dynamic functional connectivity changes in noise-induced hearing loss: a resting-state fMRI study with machine learning-based classification.

OBJECTIVE: Noise-induced hearing loss impacts brain health and cognition, with dynamic functional connectivity analysis offering a promising but under...

PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT Dataset with Corresponding Radiology Reports.

Publicly available, large-scale medical imaging datasets are crucial for developing and validating artificial intelligence (AI) models and conducting ...

Exploration of Deep Learning Methods for Synthetic T2-Weighted Pelvic MRI Generation from CT Scans: A Technical Feasibility Study.

Synthesizing T2-weighted MRI from CT scans presents a challenging ill-posed problem that remains underexplored in abdominopelvic imaging. We aim to de...

Prostate MRI quality improvement: a Roadmap from the ESUR Prostate MRI Working Group.

Prostate magnetic resonance imaging (MRI) has become a crucial tool in diagnosing and managing prostate cancer, mainly by helping to avoid unnecessary...

Integrating traditional statistics and explainable machine learning for risk stratification of atrial fibrillation recurrence after pulsed field ablation: a hypothesis-generating single-center study.

BACKGROUND: Predicting recurrence after pulsed field ablation (PFA) for paroxysmal atrial fibrillation (AF) remains challenging, particularly in early...

An explainable radiomics model based on multiparametric magnetic resonance for differentiating benign and malignant orbital tumors.

OBJECTIVE: To develop and internally test a multiparametric radiomics combined model for differentiating benign and malignant orbital tumors. PATIENTS...

Long Axial Field-of-View (LAFOV) PET in the Era of Multi-Parametric Imaging and Theranostics.

This review explores the revolutionary impact of long axial field-of-view (LAFOV) PET/CT imaging in modern nuclear medicine and molecular imaging. LAF...

A Multicenter Study on Deep Learning Model-Assisted Detection of Brain Metastases in MR Images.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a deep learning-based brain metastasis detection model (BMDM) in magnetic resonance...