Radiology

Latest AI and machine learning research in radiology for healthcare professionals.

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Self-Propelled Janus Nanocatalytic Robots Guided by Magnetic Resonance Imaging for Enhanced Tumor Penetration and Therapy.

Biomedical micro/nanorobots as active delivery systems with the features of self-propulsion and cont...

Coronary Computed Tomography Angiography with Deep Learning Image Reconstruction: A Preliminary Study to Evaluate Radiation Exposure Reduction.

Coronary computed tomography angiography (CCTA) is a medical imaging technique that produces detaile...

Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.

Modern medicine is reliant on various medical imaging technologies for non-invasively observing pati...

ChatGPT from radiologists' perspective.

ChatGPT is a newly developed technology created by the OpenAI company. It is an artificial-intellige...

Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer.

BACKGROUND: Radiomic diagnosis models generally consider only a single dimension of information, lea...

A Deep Learning Pipeline Using Prior Knowledge for Automatic Evaluation of Placenta Accreta Spectrum Disorders With MRI.

BACKGROUND: The diagnosis of prenatal placenta accreta spectrum (PAS) with magnetic resonance imagin...

Deep learning-based reconstruction and 3D hybrid profile order technique for MRCP at 3T: evaluation of image quality and acquisition time.

OBJECTIVES: To evaluate the image quality of the 3D hybrid profile order technique and deep-learning...

Emerging uses of artificial intelligence in breast and axillary ultrasound.

Breast ultrasound is a valuable adjunctive tool to mammography in detecting breast cancer, especiall...

Revenue and Cost Analysis of a System Utilizing Natural Language Processing and a Nurse Coordinator for Radiology Follow-up Recommendations.

Radiology reports often contain recommendations for follow-up imaging, Provider adherence to these r...

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.

OBJECTIVES: Machine learning (ML) for medical imaging is emerging for several organs and image modal...

Robot-assisted ultrasound reconstruction for spine surgery: from bench-top to pre-clinical study.

PURPOSE: Robot-assisted ultrasound (rUS) systems have already been used to provide non-radiative thr...

Diagnosis of Developmental Dysplasia of the Hip by Ultrasound Imaging Using Deep Learning.

BACKGROUND: A timely diagnosis of developmental dysplasia of the hip (DDH) is important for satisfac...

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features.

One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells ins...

Classification of normal and abnormal fetal heart ultrasound images and identification of ventricular septal defects based on deep learning.

OBJECTIVES: Congenital heart defects (CHDs) are the most common birth defects. Recently, artificial ...

Eliminating the need for manual segmentation to determine size and volume from MRI. A proof of concept on segmenting the lateral ventricles.

Manual segmentation, which is tedious, time-consuming, and operator-dependent, is currently used as ...

Automated identification of piglet brain tissue from MRI images using Region-based Convolutional Neural Networks.

Magnetic resonance imaging is an important tool for characterizing volumetric changes of the piglet ...

Emerging Roles for Artificial Intelligence in Heart Failure Imaging.

Artificial intelligence (AI) applications are expanding in cardiac imaging. AI research has shown pr...

Boosting multiple sclerosis lesion segmentation through attention mechanism.

Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and moni...

Benign vs malignant vertebral compression fractures with MRI: a comparison between automatic deep learning network and radiologist's assessment.

OBJECTIVE: To test the diagnostic performance of a deep-learning Two-Stream Compare and Contrast Net...

Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network.

Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely pla...

Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach.

OBJECTIVE: Breast arterial calcifications (BAC) are a sex-specific cardiovascular disease biomarker ...

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