Radiology

Nystagmus in Clinical Practice: From Diagnosis to Treatment-A Comprehensive Review.

Nystagmus, a common yet intricate ocular movement disorder, significantly contributes to visual morbidity in the paediatric and adult populations. Defined by involuntary, rhythmic, to-and-fro eye movements, this condition may manifest as an isolated anomaly or harbour more serious ocular or systemic pathologies. Its presence often provides vital diagnostic clues, emphasizing the importance of thor...

Radiomics of Dynamic Contrast-Enhanced MRI for Predicting Radiation-Induced Hepatic Toxicity After Intensity Modulated Radiotherapy for Hepatocellular Carcinoma: A Machine Learning Predictive Model Based on the SHAP Methodology.

OBJECTIVE: To develop an interpretable machine learning (ML) model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic data, dosimetric parameters, and clinical data for predicting radiation-induced hepatic toxicity (RIHT) in patients with hepatocellular carcinoma (HCC) following intensity-modulated radiation therapy (IMRT).

Cardiovascular imaging in 2024: review of current research and innovations.

Cardiovascular imaging saw significant advancements in 2024, impacting technology, pathophysiology, and clinical applications. This review provides a ...

Model-informed deep-learning photoacoustic reconstruction for low-element linear array.

Photoacoustic tomography (PAT), widely applied using linear array ultrasound transducers for clinical and preclinical imaging, faces significant chall...

What are the perceptions of AI in radiology among UK medical students and junior doctors?

BackgroundThe integration of artificial intelligence (AI) in radiology has the potential to improve diagnostic accuracy and efficiency. Medical studen...

FDG-PET intensity normalization improves radiomics- based survival prediction in oropharyngeal cancer patients: a comparison of the SUV with alternative normalization techniques.

BACKGROUND AND PURPOSE: Despite the widespread research application of radiomics, there is a knowledge gap regarding the optimal voxel intensity norma...

Lightweight hybrid transformers-based dyslexia detection using cross-modality data.

Early and precise diagnosis of dyslexia is crucial for implementing timely intervention to reduce its effects. Timely identification can improve the i...

How early can we detect diabetic retinopathy? A narrative review of imaging tools for structural assessment of the retina.

Despite current screening models, enhanced imaging modalities, and treatment regimens, diabetic retinopathy (DR) remains one of the leading causes of ...

Diagnostic challenges of carpal tunnel syndrome in patients with congenital thenar hypoplasia: a comprehensive review.

Carpal Tunnel Syndrome (CTS) is the most common entrapment neuropathy, frequently presenting with pain, numbness, and muscle weakness due to median ne...

FlowMRI-Net: A Generalizable Self-Supervised 4D Flow MRI Reconstruction Network.

BACKGROUND: Image reconstruction from highly undersampled 4D flow MRI data can be very time consuming and may result in significant underestimation of...

The imaging crisis in axial spondyloarthritis.

Imaging holds a pivotal yet contentious role in the early diagnosis of axial spondyloarthritis. Although MRI has enhanced our ability to detect early ...

Deep learning predicts HER2 status in invasive breast cancer from multimodal ultrasound and MRI.

The preoperative human epidermal growth factor receptor type 2 (HER2) status of breast cancer is typically determined by pathological examination of a...

SFPGCL: Specificity-preserving federated population graph contrastive learning for multi-site ASD identification using rs-fMRI data.

Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder that affects people's social communication and daily routine. Most existing ima...

MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI.

Accurate preoperative prediction of biochemical recurrence (BCR) in prostate cancer (PCa) is essential for treatment optimization, and demands an expl...

Optimization of Protein Extraction from Rapeseed Oil Cake by Dephenolization Process for Scale-Up Application Using Artificial Neural Networks.

Rapeseed proteins, due to their quality and wide availability, have great potential for application in human nutrition. However, their high content of...

Dynamic Predictive Models of Cardiogenic Shock in STEMI: Focus on Interventional and Critical Care Phases.

: While early risk stratification in STEMI is essential, the threat of cardiogenic shock (CS) persists after revascularization due to reperfusion inju...

Machine learning-assisted fluorescence/smartphone dual-mode platform for lead ion detection using the novel polyimide covalent organic framework.

Unintended leakage of toxic and lead ion (Pb) ions poses high harmful to human health and environment, hence its monitoring and detection is of utmost...

Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization.

Large-cohort imaging and diagnostic studies often assess cardiac function but overlook underlying biological mechanisms. Cardiac digital twins (CDTs) ...

Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images.

PCOS (Poly-Cystic Ovary Syndrome) is a multifaceted disorder that often affects the ovarian morphology of women of their reproductive age, resulting i...

MRI-derived deep learning models for predicting 1p/19q codeletion status in glioma patients: a systematic review and meta-analysis of diagnostic test accuracy studies.

PURPOSE: We conducted a systematic review and meta-analysis to evaluate the performance of magnetic resonance imaging (MRI)-derived deep learning (DL)...