Radiology

AI in nuclear medicine.

Artificial intelligence (AI) holds great promise for advancing diagnostics and treatment in nuclear medicine. The rapid growth of AI over the past decade largely driven by advances in hardware components such as graphics processing units (GPUs) and the introduction of Deep Learning (DL) and convolutional neural networks (CNN). The integration of AI and medical imaging has the potential to revoluti...

Machine Learning-Driven Approach to Identify Freezing of Gait in Individuals with Parkinson's Disease Using Conventional Structural MRI and Clinical Measures.

BACKGROUND AND PURPOSE: Freezing of gait (FOG) presents a significant challenge in the management of Parkinson's disease (PD). Our study explored the potential to predict PD-FOG using an unbiased machine learning (ML) approach that leverages conventional T1-weighted MRI and clinical measures. MATERIALS AND METHODS: Thirty-seven participants (16 PD-FOG, 21 PD-nFOG) underwent standard isotropic 1mm³...

Annotation-free 3D reconstruction and quantification of retinal microvasculature by RADAR.

Three-dimensional mapping of retinal microvasculature is essential for monitoring systemic vascular health. Existing methods rely heavily on manual an...

Patient perceptions and attitudes towards the use of artificial intelligence in the symptomatic breast unit.

OBJECTIVES: Artificial intelligence (AI) has been applied in a number of breast screening settings with favourable results. While there are a limited ...

Multidisciplinary artificial intelligence systems versus single-model approaches for the diagnosis and management of ileus and volvulus.

BACKGROUND AND AIMS: The accurate and timely diagnosis of ileus versus volvulus is essential in emergency care, as treatment choices directly influenc...

[Predictive value of a multimodal radiomics model for central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma based on machine learning].

OBJECTIVES: To develop and validate a multimodal radiomics model based on machine learning for predicting central lymph node metastasis (CLNM) in pati...

Machine learning using entropy-based texture features from MRI to differentiate histological subtypes of non-small cell lung cancer identified as metabolically active on PET/MRI.

Texture analysis is a foundational approach in imaging studies and demonstrates excellent diagnostic performance, with radiomic analysis being the mos...

Atypical Histology in Pulmonary Biopsies Obtained Using Robotic-Assisted Bronchoscopy: Natural History and Clinical Practice.

BACKGROUND: A recent consensus statement recommends that atypical cells are considered nondiagnostic specimens in the calculation of diagnostic yield....

VHU-Net: Variational hadamard U-Net for body MRI bias field correction.

Bias field artifacts in magnetic resonance imaging (MRI) scans introduce spatially smooth intensity inhomogeneities that degrade image quality and hin...

Artificial intelligence in emergency medicine: a narrative review.

INTRODUCTION: Artificial intelligence (AI) is increasingly embedded in healthcare, with expanding applications in emergency medicine (EM). OBJECTIVE: ...

UNISELF: A unified network with instance normalization and self-ensembled lesion fusion for multiple sclerosis lesion segmentation.

Automated segmentation of multiple sclerosis (MS) lesions using multicontrast magnetic resonance (MR) images improves efficiency and reproducibility c...

Artificial intelligence in knee osteoarthritis imaging and total knee arthroplasty: advances, challenges, and segmentation methods - A review.

BACKGROUND: Knee osteoarthritis (KOA) is one of the most prevalent degenerative joint diseases and a significant cause of disability. Total Knee Arthr...

Artificial Intelligence-Enhanced Electrocardiogram Models for Detection of Left Ventricular Dysfunction: A Comparison Study.

BACKGROUND: Several artificial intelligence-enhanced electrocardiogram (AI-ECG) models have shown promise in detecting left ventricular systolic dysfu...

[Advanced imaging diagnosis and therapeutic strategies for ischemic stroke].

In recent years, advances in imaging analysis technologies, including CT perfusion, MRI, and AI analysis, have extended the therapeutic time window fo...

Automated 3D segmentation of human vagus nerve fascicles and epineurium from micro-computed tomography images using anatomy-aware neural networks.

Objective.Precise segmentation and quantification of nerve morphology from imaging data are critical for designing effective and selective peripheral ...

[Enhancement of radiomics-based machine learning models for predicting efficacy of high-intensity focused ultrasound ablation of uterine fibroids using undersampling methods].

OBJECTIVES: To improve the accuracy of machine learning models for preoperative prediction of high-intensity focused ultrasound (HIFU) ablation effica...

[Diffusion cycle-consistent generative adversarial networks for pelvic active bone marrow segmentation].

OBJECTIVES: To establish a pelvic active bone marrow (ABM) segmentation method based on diffusion cycle-consistent generative adversarial networks for...

Structured reporting in knee MRI.

Structured reporting in knee MRI represents a transformative advancement in musculoskeletal radiology, promising enhanced clarity and consistency in e...

A clinical-radiomics nomogram based on multisequence MRI for predicting intraoperative vertebral artery injury in patients with primary cervical spine tumor: a diagnostic study.

BACKGROUND: Currently, reliable preoperative methods for predicting vertebral artery (VA) invasion are lacking. The authors develop a novel model base...

Integrated Ultrasound Device for Precision Bladder Volume Monitoring via Acoustic Focusing and Machine Learning.

Bladder volume monitoring is critical for managing lower urinary tract dysfunctions, yet existing methods remain invasive or operator-dependent and ar...