Radiology

Deep Learning-Based Reconstruction Improves Image Quality in Canine Cranial Abdominal MRI: A Prospective Pilot Study.

The application of deep learning-based reconstruction (DLR) to magnetic resonance imaging (MRI) has been recently introduced in human and veterinary medicine to improve image quality without prolonging acquisition time. We hypothesized that cranial abdominal MRI with DLR would have superior image quality than conventional MRI. This prospective comparative pilot study aimed to compare cranial abdom...

Time Distributed Classification of Alzheimer's Disease on MRI Scans.

The diagnosis of Alzheimer's disease (AD) has progressively depended on sophisticated neuroimaging methods alongside cognitive assessments. This study combines volumetric feature analysis with computational modeling techniques, focusing on spatial and temporal analysis, to categorize individuals as cognitively normal (CN), mild cognitive impairment (MCI), or AD using magnetic resonance imaging (MR...

Diagnostic Uncertainty & Improving Diagnostic Certainty in Radiology.

The Radiology Research Alliance (RRA) of the Association of Academic Radiology (AAR) convenes task forces to study trends that will shape the future o...

Urgent Need for Artificial Intelligence Readiness: Insights From a Multicenter Cross-Sectional Study on Medical Undergraduates and Radiology Trainees in Central and Western China.

RATIONALE AND OBJECTIVES: Artificial intelligence (AI) is reshaping the future of medicine, particularly influencing specialties like radiology. While...

An Enhanced Machine Learning-Based Multimodal Framework for Seizure Detection Using EEG and MRI Data.

One of the most common neurological disorders that immediately alters a person's way of life is an epileptic seizure. Accurate seizure detection remai...

An Effective LRSF-DLNN-Based Autism Spectrum Disorder Prediction Using EEG and fMRI.

In today's society, autism spectrum disorder (ASD) is a common neurological disorder that affects a person's behavior and communication. Hence, an ear...

A Deep Dual-Domain Interaction Reconstruction Framework With Adaptive Gating Fusion for Low-Field MRI.

Low-field magnetic resonance imaging (MRI) offers a cost-effective and accessible alternative to high-field systems but inherently suffers from low si...

Postdeployment Monitoring and Surveillance Methods, Guidelines, and Possibilities for AI in Radiology.

As radiology AI systems move from predeployment validation to routine radiology practice, attention is shifting toward postdeployment monitoring and p...

AI-assisted Radiomic Model for Cervical Cancer Recurrence Prediction: A Multicenter Retrospective Study with Experimental Validation.

Purpose To evaluate the value of a nomogram model incorporating clinical parameters, hematologic inflammatory biomarkers, and MRI radiomic features in...

Multi-Center Adversarial Bi-Phase Cross-Attention Network for Right Ventricular Segmentation and Functional Classification in Echocardiography.

BACKGROUND: Automated right ventricular (RV) analysis in 2D echocardiography is limited by the morphological complexity of RV segmentation and the dom...

Ultrasound-Based AI in Predicting Hormone Receptor Status in Breast Cancer: Is "Digital Biopsy" Possible.

Accurate and rapid determination of tumor histopathological features and molecular subtypes is critical for breast cancer prognosis and treatment stra...

Automatic Phase and Sequence Identification in Gd-EOB-DTPA-Enhanced Liver MRI Using Deep Convolutional and Sequential Learning.

The purpose of this study is to develop and validate a deep learning model for automatic identification of acquisition sequences (including T1-weighte...

Artificial Intelligence-Assisted Inner Ear Computed Tomography Analysis: Radiomics-Based Comparison of Affected and Unaffected Ears in Idiopathic Sudden Sensorineural Hearing Loss.

We aimed to determine whether computed tomography (CT)-based radiomic features of the inner ear can distinguish the affected side from the contralater...

Deep learning enables rabies virus detection and antibody quantification in cell culture.

Rabies remains a major neglected disease, causing tens of thousands of deaths annually in endemic regions. Cell culture-based assays are central to ra...

Radiomics-based causal machine learning for exploratory treatment-effect estimation of neoadjuvant chemotherapy cycle intensity in osteosarcoma: a proof-of-concept study.

BACKGROUND: Radiomics-based modeling has shown promise for characterizing tumor heterogeneity, but its integration with causal machine learning for tr...

Multi-omics Signature Predicts Anti-EGFR Therapy Benefit in Colorectal Cancer Liver Metastases: A Multi-center Cohort Study.

INTRODUCTION: Clinical management of RAS wild-type colorectal cancer liver metastases (CRLM) remains challenging because many patients exhibit primary...

The evolution of ultrasound in sarcopenia assessment: Global trends, technological shifts, and clinical frontiers.

BackgroundSarcopenia is a progressive skeletal muscle disorder associated with increased disability, morbidity, and mortality. Ultrasound has gained i...

Clinical applications of artificial intelligence in autosomal dominant polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic kidney disorder leading to kidney failure. Recent advancements in arti...

Artificial intelligence demonstrates comparable diagnostic accuracy to radiologists for anterior cruciate ligament tears on MRI: A systematic review and meta-analysis.

PURPOSE: Anterior cruciate ligament (ACL) tears are among the most common knee injuries, accounting for half of all knee ligament injuries. Magnetic r...

APEX-NET: automated pancreatic evaluation network using early non-contrast CT.

OBJECTIVES: To develop and validate APEX-NET for early diagnosis and severity stratification of acute pancreatitis (AP) using non-contrast CT (NCCT), ...