Radiology

TA-SSM net: tri-directional attention and structured state-space model for enhanced MRI-Based diagnosis of Alzheimer's disease and mild cognitive impairment.

Early diagnosis of Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI), is c...

A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning.

This paper presents a novel transfer learning approach for segmenting brain tumors in Magnetic Reson...

A successive framework for brain tumor interpretation using Yolo variants.

Accurate identification and segmentation of brain tumors in Magnetic Resonance Imaging (MRI) images ...

Impact of large language models and vision deep learning models in predicting neoadjuvant rectal score for rectal cancer treated with neoadjuvant chemoradiation.

This study aims to explore Deep Learning methods, namely Large Language Models (LLMs) and Computer V...

Impact of AI assistance on radiologist interpretation of knee MRI.

OBJECTIVES: Knee injuries frequently require Magnetic Resonance Imaging (MRI) evaluation, increasing...

Effectiveness of Radiomics-Based Machine Learning Models in Differentiating Pancreatitis and Pancreatic Ductal Adenocarcinoma: Systematic Review and Meta-Analysis.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) and mass-forming pancreatitis (MFP) share simila...

Enhanced Detection, Using Deep Learning Technology, of Medial Meniscal Posterior Horn Ramp Lesions in Patients with ACL Injury.

BACKGROUND: Meniscal ramp lesions can impact knee stability, particularly when associated with anter...

A Trust-Guided Approach to MR Image Reconstruction with Side Information.

Reducing MRI scan times can improve patient care and lower healthcare costs. Many acceleration metho...

Technological advancements in sports injury: diagnosis and treatment.

Sports injuries are a significant concern for athletes at all levels of competition, ranging from ac...

Navigating the AI revolution: will radiology sink or soar?

The rapid acceleration of digital transformation and artificial intelligence (AI) is fundamentally r...

Effect of spatial resolution on the diagnostic performance of machine-learning radiomics model in lung adenocarcinoma: comparisons between normal- and high-spatial-resolution imaging for predicting invasiveness.

PURPOSE: To construct two machine learning radiomics (MLR) for invasive adenocarcinoma (IVA) predict...

Generative artificial intelligence for counseling of fetal malformations following ultrasound diagnosis.

OBJECTIVES: To explore the potential role of generative artificial intelligence (GenAI) in enhancing...

Radiation enteritis associated with temporal sequencing of total neoadjuvant therapy in locally advanced rectal cancer: a preliminary study.

BACKGROUND: This study aimed to develop and validate a multi-temporal magnetic resonance imaging (MR...

High-Resolution Ultrasound Data for AI-Based Segmentation in Mouse Brain Tumor.

Glioblastoma multiforme (GBM) is the most aggressive type of brain cancer, making effective treatmen...

Classification of Brain Tumors in MRI Images with Brain-CNXSAMNet: Integrating Hybrid ConvNeXt and Spatial Attention Module Networks.

Brain tumors (BT) can cause fatal outcomes by affecting body functions, making precise early detecti...

Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study.

The purpose of this study was to create and validate an ultrasound-based graph convolutional network...

Correction: Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Prospective Cohort Study.

Re: Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review.

Clinician Perspectives of a Magnetic Resonance Imaging-Based 3D Volumetric Analysis Tool for Neurofibromatosis Type 2-Related Schwannomatosis: Qualitative Pilot Study.

BACKGROUND: Accurate monitoring of tumor progression is crucial for optimizing outcomes in neurofibr...

Structural MRI-based Computer-aided Diagnosis Models for Alzheimer Disease: Insights into Misclassifications and Diagnostic Limitations.

Purpose To examine common patterns among different computer-aided diagnosis (CAD) models for Alzhei...

Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study.

Purpose To develop and optimize a federated learning (FL) framework across multiple clients for bip...