Radiology

Metasurface-enhanced terahertz imaging for glioblastoma in orthotopic xenograft mouse model combined with neural network decision making.

Terahertz (THz) optical sensing and imaging offer significant potential in a range of biological and medical applications owing to their low-energy, non-ionizing nature, and ultra-broadband spectral information, which includes numerous molecular fingerprints. However, conventional THz imaging suffers from limited contrast and low absorption cross-section in biological tissues. Recent advances in t...

Integrating handheld ultrasound in rheumatology: A review of benefits and drawbacks.

Musculoskeletal ultrasound is a key tool in rheumatology for diagnosing and managing inflammatory arthritis. Traditional ultrasound systems, while effective, can be cumbersome and costly, limiting their use in many clinical settings. Handheld ultrasound (HHUS) devices, which are portable, affordable, and user-friendly, have emerged as a promising alternative. This review explores the role of HHUS ...

High-performance Open-source AI for Breast Cancer Detection and Localization in MRI.

Purpose To develop and evaluate an open-source deep learning model for detection and localization of breast cancer on MRI. Materials and Methods In t...

[AI-enabled clinical decision support systems: challenges and opportunities].

Clinical decision-making is inherently complex, time-sensitive, and prone to error. AI-enabled clinical decision support systems (CDSS) offer promisin...

Development of an anomaly detection system for Gibbs artifact identification in amyloid PET imaging.

The PET Imaging Site Qualification Program for amyloid positron emission tomography (PET) in Japan includes visual evaluation of the cylinder phantom....

Interventional Radiology Reporting Standards and Checklist for Artificial Intelligence Research Evaluation (iCARE).

As artificial intelligence (AI) becomes increasingly prevalent within interventional radiology (IR) research and clinical practice, steps must be take...

Novel Application of Connectomics to the Surgical Management of Pediatric Arteriovenous Malformations.

Introduction The emergence of connectomics in neurosurgery has allowed for construction of detailed maps of white matter connections, incorporating bo...

Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis.

INTRODUCTION: Systemic sclerosis (SSc) is a complex inflammatory vasculopathy with diverse symptoms and variable disease progression. Despite its know...

Efficacy of an Automated Pulmonary Embolism (PE) Detection Algorithm on Routine Contrast-Enhanced Chest CT Imaging for Non-PE Studies.

The urgency to accelerate PE management and minimize patient risk has driven the development of artificial intelligence (AI) algorithms designed to pr...

Few-Shot Learning for Prostate Cancer Detection on MRI: Comparative Analysis with Radiologists' Performance.

Deep-learning models for prostate cancer detection typically require large datasets, limiting clinical applicability across institutions due to domain...

Assessment of Robustness of MRI Radiomic Features in the Abdomen: Impact of Deep Learning Reconstruction and Accelerated Acquisition.

The objective of this study is to investigate the impact of deep learning reconstruction and accelerated acquisition on reproducibility and variabilit...

The evaluation of artificial intelligence in mammography-based breast cancer screening: Is breast-level analysis enough?

OBJECTIVES: To assess whether the diagnostic performance of a commercial artificial intelligence (AI) algorithm for mammography differs between breast...

Machine learning-based construction and validation of an radiomics model for predicting ISUP grading in prostate cancer: a multicenter radiomics study based on [68Ga]Ga-PSMA PET/CT.

PURPOSE: The International Society of Urological Pathology (ISUP) grading of prostate cancer (PCa) is a crucial factor in the management and treatment...

Refining cardiac segmentation from MRI volumes with CT labels for fine anatomy of the ascending aorta.

Magnetic resonance imaging (MRI) is time-consuming, posing challenges in capturing clear images of moving organs, such as cardiac structures, includin...

Preoperative Assessment of Lymph Node Metastasis in Rectal Cancer Using Deep Learning: Investigating the Utility of Various MRI Sequences.

PURPOSE: This study aimed to develop a deep learning (DL) model based on three-dimensional multi-parametric magnetic resonance imaging (mpMRI) for pre...

Ultrasound Displacement Tracking Techniques for Post-Stroke Myofascial Shear Strain Quantification.

OBJECTIVE: Ultrasound shear strain is a potential biomarker of myofascial dysfunction. However, the quality of estimated shear strains can be impacted...

Bedside Ultrasound Vector Doppler Imaging System with GPU Processing and Deep Learning.

Recent innovations in vector flow imaging promise to bring the modality closer to clinical application and allow for more comprehensive high-frame-rat...

AI-based large-scale screening of gastric cancer from noncontrast CT imaging.

Early detection through screening is critical for reducing gastric cancer (GC) mortality. However, in most high-prevalence regions, large-scale screen...

Artificial Intelligence Powered Real-Time Coronary Stenosis Recognition and Quantification in Angiography.

Effective and automated measurement of coronary lesions is essential for timely decision-making during interventions. However, a comprehensive, real-t...

Multimodal Deep Learning Based on Ultrasound Images and Clinical Data for Better Ovarian Cancer Diagnosis.

This study aimed to develop and validate a multimodal deep learning model that leverages 2D grayscale ultrasound (US) images alongside readily availab...