Radiology

Feasibility study of fast T2-weighted imaging with deep-learning reconstruction in volunteers and emergency patients with acute abdomen.

PURPOSE: To evaluate the image quality and clinical utility of DLR-enhanced single-shot fast spin-echo (SSFSE) T2-weighted imaging (T2WI) for diagnosing acute abdominal conditions, compared to standard SSFSE and Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) T2WI sequences. METHODS: This prospective single-institutional study enrolled 70 participants (35 h...

[Artificial intelligence prediction of surgical difficulty in mid-low rectal cancer: a single-center cohort study].

Objective: This study processes and analyzes rectal MRI images of patients with mid-to-low rectal cancer using deep learning technology, and integrates these data with clinical baseline information to construct a fully automated end-to-end prediction model. The model is designed to assist colorectal surgeons in preoperatively assessing surgical difficulty and selecting the optimal surgical approac...

[Magnetically Controlled Capsule Endoscopy for Upper Gastrointestinal Examination].

Esophagogastroduodenoscopy (EGD) is the standard diagnostic modality for upper gastrointestinal (UGI) diseases, but its invasive nature and the risk o...

Image-space compensation of inter-crystal scattering in PET using a neural network based filter.

BACKGROUND: In positron emission tomography (PET), gamma photons arriving at the detector ring may undergo one or more Compton scattering events, pote...

CCTA as a Screening Tool (Invited Commentary).

Coronary computed tomography angiography (CCTA) has evolved into a key non-invasive tool for evaluating coronary artery disease, offering high sensiti...

MRI-based multilevel radiomics and transformer features for predicting radiation-induced carotid artery injury after nasopharyngeal carcinoma radiotherapy: A multicenter study.

PURPOSE: To develop and validate an MRI-based fusion model (Rad-SRad-SwinT) integrating conventional radiomics (Rad), subregional radiomics (SRad), an...

Biatrial cardioneuroablation guided by robotic magnetic navigation and artificial intelligence-based mapping in vagal bradyarrhythmias: A controlled observational study.

PURPOSE: Reflex bradyarrhythmias and syncope related to excessive vagal tone may be refractory to conservative therapy and significantly impair qualit...

Machine learning-based models for preoperative prediction of pituitary adenoma consistency: a systematic review and meta-analysis.

BACKGROUND/OBJECTIVES: The consistency of pituitary adenoma (PA) significantly impacts surgical difficulty and the extent of resection. Machine learni...

Enhancing anesthesia monitoring training: a SPOC and Gagné's model hybrid personalized by artificial intelligence.

BACKGROUND: High-quality training in anesthesia nursing-particularly in intraoperative monitoring-is essential for ensuring patient safety. However, t...

Optimisation and Technological Advances in Cardiovascular Magnetic Resonance to improve Access and Screening.

Cardiovascular magnetic resonance (CMR) can comprehensively assess cardiac function and structure, with a unique capability for tissue characterisatio...

IUGC: A benchmark of landmark detection in end-to-end intrapartum ultrasound biometry.

Accurate intrapartum biometry plays a crucial role in monitoring labor progression and preventing complications. However, its clinical application is ...

Generating synthetic MRI scans for improving Alzheimer's disease diagnosis.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia. Magnetic Resonance Imaging (MRI) combined with...

Deep learning assessment of fetal brain maturation on 3D ultrasound volumes in early-onset fetal growth restriction.

OBJECTIVES: To quantify fetal brain maturation in fetuses with early-onset fetal growth restriction (FGR) by estimating gestational age (GA) based on ...

Identification and Localization of Breast Tumor Components via a Convolutional Neural Network Based on High-Frequency Ultrasound Combined With Histopathologic Registration: Prospective Study.

BACKGROUND: Given the highly heterogeneous biology of breast cancer, a more effective noninvasive diagnostic tool that unravels microscopic histopatho...

Two-Minute Deep Learning-Powered Brain Quantitative Mapping: Accelerating Clinical Imaging With Synthetic Magnetic Resonance Imaging.

BACKGROUND: Quantitative magnetic resonance imaging (MRI) is an advanced technique that can map the physical properties (T1, T2, and proton density [P...

Influence of personalized human head modeling and resolution on EEG source localization for rapid brain mapping.

OBJECTIVE: To evaluate the trade-offs among model resolution, anatomical fidelity, computational cost, and localization accuracy in EEG source imaging...

Multimodal deep learning with anatomically constrained attention for screening MRI-detectable TMJ abnormalities from panoramic images.

Early diagnosis of temporomandibular disorders is challenging. Particularly, intra-articular temporomandibular joint (TMJ) abnormalities can only be c...

Sex disparities in deep learning estimation of ejection fraction from cardiac magnetic resonance imaging.

The advent of artificial intelligence in cardiovascular imaging holds immense potential for earlier diagnoses, precision medicine, and improved diseas...

Explainable Hybrid Deep Learning Framework Integrating MobileNetV2, EfficientNetV2B0, and KNN for MRI-Based Brain Tumor Classification.

Magnetic resonance imaging (MRI) is central to noninvasive brain tumor assessment, yet clinical uptake of artificial intelligence depends on both accu...

Diagnostic Performance of Preoperative Imaging-based Radiomics Models for Predicting Liver Metastases in Colorectal Cancer: A Systematic Review and Meta-analysis.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of preoperative computed tomography (CT) and magnetic resonance imaging (MRI)-based r...