Radiology

Latest AI and machine learning research in radiology for healthcare professionals.

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Synergistic fusion: An integrated pipeline of CLAHE, YOLO models, and advanced super-resolution for enhanced thermal eye detection.

Accurate eye detection in thermal images is essential for diverse applications, including biometrics...

Characterizing structure-function coupling in subjective memory complaints of preclinical Alzheimer's disease.

BackgroundSubjective cognitive decline (SCD) is recognized as an early phase in the progression of A...

The application of super-resolution ultrasound radiomics models in predicting the failure of conservative treatment for ectopic pregnancy.

BACKGROUND: Conservative treatment remains a viable option for selected patients with ectopic pregna...

2D-3D deformable image registration of histology slide and micro-CT with DISA-based initialization.

Recent developments in the registration of histology and micro-computed tomography (µCT) have broade...

Task based evaluation of sparse view CT reconstruction techniques for intracranial hemorrhage diagnosis using an AI observer model.

Sparse-view computed tomography (CT) holds promise for reducing radiation exposure and enabling nove...

An AI method to predict pregnancy loss by extracting biological indicators from embryo ultrasound recordings in early pregnancy.

B-ultrasound results are widely used in early pregnancy loss (EPL) prediction, but there are inevita...

Deep learning models for deriving optimised measures of fat and muscle mass from MRI.

Fat and muscle mass are potential biomarkers of wellbeing and disease in oncology, but clinical meas...

Documenting Disclosure: Limited Reporting of Generative AI Usage in Radiology Research Manuscripts.

RATIONALE AND OBJECTIVES: Large language models (LLMs) show promise in radiology through various cli...

FSS-ULivR: a clinically-inspired few-shot segmentation framework for liver imaging using unified representations and attention mechanisms.

Precise liver segmentation is critical for accurate diagnosis and effective treatment planning, serv...

Comparative study of 2D vs. 3D AI-enhanced ultrasound for fetal crown-rump length evaluation in the first trimester.

BACKGROUND: Accurate fetal growth evaluation is crucial for monitoring fetal health, with crown-rump...

Super-resolution deep learning in pediatric CTA for congenital heart disease: enhancing intracardiac visualization under free-breathing conditions.

OBJECTIVE: This study assesses the effectiveness of super-resolution deep learning reconstruction (S...

Image quality and radiation dose of reduced-dose abdominopelvic computed tomography (CT) with silver filter and deep learning reconstruction.

To assess the image quality and radiation dose between reduced-dose CT with deep learning reconstruc...

Developing an explainable machine learning and fog computing-based visual rating scale for the prediction of dementia progression.

Recently, dementia research has primarily concentrated on using Magnetic Resonance Imaging (MRI) to ...

Multi-scale machine learning model predicts muscle and functional disease progression.

Facioscapulohumeral muscular dystrophy (FSHD) is a genetic neuromuscular disorder characterized by p...

Automatic segmentation of liver structures in multi-phase MRI using variants of nnU-Net and Swin UNETR.

Accurate segmentation of the liver parenchyma, portal veins, hepatic veins, and lesions from MRI is ...

Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm.

In this research, we predict the output signal generated by iron oxide-based nanoparticles in Magnet...

Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos.

Embryo development is driven by the spatiotemporal dynamics of complex gene regulatory networks. Unc...

Evaluating Artificial Intelligence-Assisted Prostate Biparametric MRI Interpretation: An International Multireader Study.

Variability in prostate biparametric MRI (bpMRI) interpretation limits diagnostic reliability for p...

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