Radiology

Impact of AI-based Slab Reconstruction Technology on the Diagnostic Accuracy of Screening Digital Breast Tomosynthesis.

Background Digital breast tomosynthesis (DBT) uses 1-mm slices, resulting in a larger number of images and longer interpretation times than conventional digital two-dimensional mammography. Slab reconstruction technologies address this challenge by generating thicker slices, thereby reducing the number of images requiring review, improving efficiency, and lowering storage demand. Purpose To compar...

AI-based Histologic Heterogeneity of Microvascular Obstruction at Cardiac MRI for Predicting MACEs: A Multicenter Study.

Background Microvascular obstruction (MVO) is strongly associated with adverse outcomes after ST-segment elevation myocardial infarction (STEMI). However, manual quantification of MVO is time-consuming and fails to capture the heterogeneity of microvascular injury. Purpose To evaluate an artificial intelligence (AI)-based model including automated MVO segmentation and radiomic feature extraction t...

Fractal analysis in dentistry: a bibliometric analysis (1991-2024).

OBJECTIVES: This study aims to provide a comprehensive bibliometric mapping of the scientific evolution and research trends of fractal analysis (FA) i...

Orbitofrontal cortex and amygdala as key regions for the classification of small animal phobia: A structural MRI machine learning study.

Small animal phobia (SAP) is an anxiety disorder characterized by an intense fear triggered by small animals. Existing studies on SAP have primarily u...

Artificial intelligence empowered coronary artery imaging: A review.

Cardiovascular disease is the leading cause of death worldwide, with coronary artery disease the most prevalent cause. Although artificial intelligenc...

Automated vs. manual segmentation for small renal mass imaging.

INTRODUCTION: Automated segmentation using artificial intelligence (AI) has the potential to rapidly perform three-dimensional (3D) segmentation of sm...

Radiomics-Based Machine Learning versus the MAP Score for Predicting Haematocrit Decline after Laparoscopic Partial Nephrectomy.

OBJECTIVE: To evaluate and compare the ability of the Mayo Adhesive Probability (MAP) score and radiomics-based machine learning approaches to predict...

The role of machine learning, deep learning, and MRI findings in the classification of pediatric posterior fossa tumors.

PURPOSE: Pediatric posterior fossa tumors represent a major subset of childhood central nervous system neoplasms; however, overlapping MRI features of...

Evaluation of super-resolution deep learning reconstruction on three-dimensional constructive interference in steady state for enhanced visualization of vestibular schwannomas.

We evaluated whether, compared with conventional deep learning reconstruction (DLR) and zero-filling interpolation (ZIP), super-resolution DLR (SR-DLR...

FreeTune4D: Anatomy-Aware 4D-MRI Motion Reconstruction Benchmark and Free Fine-Tuning Framework.

Deep learning and prior-image-guided cross modality motion reconstruction methods have recently en abled faster acquisition time and lower artifacts i...

Physical Parameter-Guided Deep Learning Ultrasound Localization Microscopy Framework Based on Diffusion Model.

OBJECTIVE: Deep learning based-imaging methods have demonstrated significant potential for achieving high spatiotemporal resolution in Ultrasound Loca...

A reproducible data-driven parameter optimization framework for classical skull stripping methods across heterogeneous brain MRI datasets.

Accurate skull stripping is a critical preprocessing step for reliable brain MRI analysis, yet the performance of classical algorithms remains highly ...

Differentiating Mummified Thyroid Nodules From Papillary Thyroid Carcinoma: A Machine Learning Approach Using Multi-Modal Ultrasound Radiomics.

OBJECTIVES: This study aimed to establish a machine-learning model that integrates contrast-enhanced ultrasound (CEUS) radiomics, conventional ultraso...

Toward Autonomous Histotripsy: Integrating Deep Learning Segmentation With Robotic Control for Glioblastoma.

OBJECTIVE: Glioblastoma multiforme (GBM) is an aggressive brain tumor in which incomplete margin delineation during surgery can contribute to residual...

A Preliminary Open-Label Pilot Study of a Pillow-Based ELF-ELME (Extremely Low-Frequency, Extremely Low Magnetic Environment) Intervention for Major Depressive Disorder.

BACKGROUND: Mitochondrial dysfunction has been implicated in the pathogenesis of depression. Major depressive disorder (MDD) is a prevalent condition ...

Submanifold sparse convolutional networks for automated 3D segmentation of kidneys and kidney tumours in computed tomography.

Accurate delineation of kidney tumours in Computed Tomography (CT) is essential for downstream quantitative analysis and precision oncology that could...

A Cost-Efficient Multi-Angle Fusion Deep Learning for Ultrasound Localization Microscopy.

Ultrasound localization microscopy (ULM) enables super-resolution imaging of microvascular structures by localizing microbubbles from clutter-filtered...

Prostate Targeting: Compact Robot With Harmonic Stepper Motors for MRI-Guided Needle Therapy.

The superior image quality and excellent contrast offered by Magnetic Resonance Imaging (MRI) make it an ideal tool for guiding interventional procedu...

The DeepJoint Algorithm: An Innovative Approach for Studying the Longitudinal Evolution of Quantitative Mammographic Density and Its Association With Screen-Detected Breast Cancer Risk.

High mammographic density is a well-known risk factor for breast cancer and reduces the sensitivity of mammography-based screening. While automated ma...

Breaking the Limits of Low-Field MRI: Deep Learning Approaches to Image Enhancement.

Low-field magnetic resonance imaging (LF-MRI) has emerged as a transformative technology, offering portable and cost-effective solutions for medical i...