Radiology

Physics-constrained deep-learning framework for MRI metasurfaces.

Metasurfaces resolve limited signal-to-noise ratio (SNR) and radio frequency (RF) inhomogeneity bottlenecks in magnetic resonance imaging (MRI). However, strong unit-cell near-field coupling creates an "ill-posed"' design problem, where conventional optimization struggles with computation costs and non-unique mappings. We propose a physics-constrained bidirectional deep learning framework for MRI ...

Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data.

Coronary artery stenosis is a leading cause of cardiovascular disease, diagnosed by analyzing the coronary arteries from multiple angiography views. Although numerous deep-learning models have been proposed for stenosis detection from a single angiography view, their performance heavily relies on expensive view-level annotations, which are often not readily available in hospital systems. Moreover,...

Performance of Deep Learning in Classifying Age-Related Macular Degeneration From Images: Systematic Review and Meta-Analysis.

BACKGROUND: Age-related macular degeneration (AMD) is a leading cause of irreversible blindness worldwide. Retinal imaging and deep learning (DL) may ...

Robot-Assisted Oral Examination: Challenges and Innovations.

OBJECTIVES: To summarize and evaluate the core components, functional modules, and developmental challenges of intelligent oral examination robots, an...

Dental-dedicated magnetic resonance imaging in prosthodontics: Applications, benefits, and limitations.

STATEMENT OF PROBLEM: Dental-dedicated magnetic resonance imaging (ddMRI) represents a radiation free imaging modality that allows visualizing mineral...

Multimodal multitask deep learning for grading management system in non-small cell lung cancer.

Accurate histologic subtyping, tumor node metastasis classification (TNM) staging and prognostic assessment are central to clinical management of non-...

Multimodal fusion-based prediction of postoperative survival in gallbladder adenocarcinoma: Model development and validation.

Accurate prognostic models are essential for optimizing treatment strategies in gallbladder adenocarcinoma (GBAC). We preliminarily constructed and va...

Integrating ultrasound-CT-MR for preoperative multi-task prediction in ovarian cancer: achieving diagnostic parity with multidisciplinary team consensus.

Ovarian cancer, with high mortality, demands accurate preoperative assessment to guide individualized treatment. It typically requires ultrasound, CT,...

Non-invasive imaging diagnostic techniques in metabolic dysfunction-associated steatotic liver disease: a roadmap for clinicians.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), is the most prevalent ch...

A computational model to describe multi-regional brain architecture during neurodegeneration in Alzheimer's disease.

We previously proposed an MRI-based machine learning model to describe the mesoscopic architecture of the human brain to aid in classifying subjects a...

Depression detection from multimodalities based on LeNet with hunter-geese optimization.

Depression is considered a psychological illness, and the effects of depression are persistent sadness, loss of interest, and severe depression, which...

Investigation of the molecular network underlying PET-MPs-induced inflammatory bowel disease via integrated machine learning and molecular docking approaches.

Polyethylene terephthalate microplastics (PET-MPs), as environmental contaminants, have raised significant concerns due to their potential toxicity, l...

Experimental dipole moment prediction with a progressive 2D-3D hybrid framework and twin-pair-based diagnostic evaluation.

Accurate prediction of molecular dipole moments (μ) is essential for modeling electrostatic interactions in solvation, molecular recognition, and mate...

A Review of the Australian MRI Linac Program: From Pie in the Sky to Research Milestone.

The Australian Magnetic Resonance Imaging (MRI) Linear Accelerator program (MRI linac) was a major research project that aimed to build and test a uni...

DMRNet: a dynamic multi-scale residual network for Shamrock view and lumbar plexus segmentation.

Lumbar plexus block (LPB) is a regional anesthesia technique widely used for hip and knee surgeries. However, despite the assistance of ultrasound gui...

Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are rapidly transforming the medical field. The aim of this review was to outline t...

Zero-Shot Self-Supervised Learning of Single Breath-Hold Magnetic Resonance Cholangiopancreatography (MRCP) Reconstruction.

PURPOSE: To investigate the feasibility of zero-shot self-supervised learning reconstruction for reducing breath-hold times in magnetic resonance chol...

Exploring the use of AI-generated counterfactual chest X-rays to enhance diagnostic learning in medical education.

Accurate interpretation of chest X-rays is a critical clinical skill, yet radiology training in medical education remains limited and often fails to p...

Integration of two-dimensional microvascular flow imaging and ultrasound scoring for prediction of placenta accreta spectrum: A prospective study.

OBJECTIVE: This study evaluates the diagnostic performance of two-dimensional (2D) microvascular flow imaging combined with standardized ultrasound ma...

Association Between Amyloid Deposition and Collagen-to-Elastin Ratio in the Ligamentum Flavum: A Machine Learning Histologic Study in Matched Groups.

BACKGROUND AND OBJECTIVES: Amyloid deposition in the ligamentum flavum (LF) occurs in 25% to 45% of patients with lumbar stenosis (LS). Amyloid-positi...