Radiology

Artificial Intelligence in Ultrasound Imaging: Where Are We Now?

Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial.

Accurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor,...

Tricuspid valve flow measurement using a deep learning framework for automated valve-tracking 2D phase contrast.

PURPOSE: Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the r...

Machine learning models based on ultrasound and physical examination for airway assessment.

PURPOSE: To demonstrate the utility of machine learning models for predicting difficult airways usin...

Deep Learning-Based Segmentation and Risk Stratification for Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Imaging.

PURPOSE: To develop a deep neural network system for the automatic segmentation and risk stratificat...

Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations.

The field of veterinary diagnostic imaging is undergoing significant transformation with the integra...

Deep learning-based magnetic resonance imaging analysis for chronic cerebral hypoperfusion risk.

BACKGROUND: Chronic cerebral hypoperfusion (CCH) is a frequently encountered clinical condition that...

The utility of artificial intelligence in identifying radiological evidence of lung cancer and pulmonary tuberculosis in a high-burden tuberculosis setting.

BACKGROUND: Artificial intelligence (AI), using deep learning (DL) systems, can be utilised to detec...

Investigation on ultrasound images for detection of fetal congenital heart defects.

Congenital heart defects (CHD) are one of the serious problems that arise during pregnancy. Early CH...

DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images.

Breast cancer remains a critical global concern, underscoring the urgent need for early detection an...

Diagnostic performance of ultrasound-based artificial intelligence for predicting key molecular markers in breast cancer: A systematic review and meta-analysis.

BACKGROUND: Breast cancer (BC) diagnosis and treatment rely heavily on molecular markers such as HER...

Robotic Guided Interventional Radiology Set to Break Boundaries.

Transformer-CNN hybrid network for improving PET time of flight prediction.

In positron emission tomography (PET) reconstruction, the integration of time-of-flight (TOF) inform...

Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture.

The most common and aggressive tumor is brain malignancy, which has a short life span in the fourth ...

Thin-slice 2D MR Imaging of the Shoulder Joint Using Denoising Deep Learning Reconstruction Provides Higher Image Quality Than 3D MR Imaging.

PURPOSE: This study was conducted to evaluate whether thin-slice 2D fat-saturated proton density-wei...

Deep-learning-based motion correction using multichannel MRI data: a study using simulated artifacts in the fastMRI dataset.

Deep learning presents a generalizable solution for motion correction requiring no pulse sequence mo...

Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.

PURPOSE: This study aims to evaluate the clinical performance of a deep learning (DL)-enhanced two-f...

Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis.

OBJECTIVE: This study seeks to construct a machine learning model that merges clinical characteristi...

Thermally Drawn-Based Microtubule Soft Continuum Robot for Cardiovascular Intervention.

Cardiovascular disease is becoming the leading cause of human mortality. In order to address this, f...

Artificial Intelligence Quantification of Enhanced Synovium Throughout the Entire Hand in Rheumatoid Arthritis on Dynamic Contrast-Enhanced MRI.

BACKGROUND: Challenges persist in achieving automatic and efficient inflammation quantification usin...

Patient-specific cerebral 3D vessel model reconstruction using deep learning.

Three-dimensional vessel model reconstruction from patient-specific magnetic resonance angiography (...