Radiology

Artificial intelligence-powered coronary artery disease diagnosis from SPECT myocardial perfusion imaging: a comprehensive deep learning study.

BACKGROUND: Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is a well-established modality for noninvasive diagnostic assessment of coronary artery disease (CAD). However, the time-consuming and experience-dependent visual interpretation of SPECT images remains a limitation in the clinic.

Automated Coronary Artery Segmentation with 3D PSPNET using Global Processing and Patch Based Methods on CCTA Images.

The prevalence of coronary artery disease (CAD) has become the major cause of death across the world in recent years. The accurate segmentation of coronary artery is important in clinical diagnosis and treatment of coronary artery disease (CAD) such as stenosis detection and plaque analysis. Deep learning techniques have been shown to assist medical experts in diagnosing diseases using biomedical ...

T2-weighted imaging of rectal cancer using a 3D fast spin echo sequence with and without deep learning reconstruction: A reader study.

PURPOSE: To compare image quality and clinical utility of a T2-weighted (T2W) 3-dimensional (3D) fast spin echo (FSE) sequence using deep learning rec...

TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound.

PURPOSE: While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspiciou...

Intuitive Human-Artificial Intelligence Theranostic Complementarity.

Deep learning artificial intelligence (AI) algorithms are poised to subsume diagnostic imaging specialists in radiology and nuclear medicine, where ra...

A multi-task self-supervised approach for mass detection in automated breast ultrasound using double attention recurrent residual U-Net.

Breast cancer is the most common and lethal cancer among women worldwide. Early detection using medical imaging technologies can significantly improve...

Robust Myocardial Perfusion MRI Quantification With DeepFermi.

Stress perfusion cardiac magnetic resonance is an important technique for examining and assessing the blood supply of the myocardium. Currently, the m...

Development of a Machine-Learning Algorithm to Identify Cauda Equina Compression on Magnetic Resonance Imaging Scans.

OBJECTIVE: Cauda equina syndrome (CES) poses significant neurological risks if untreated. Diagnosis relies on clinical and radiological features. As t...

Assessment of breast composition in MRI using artificial intelligence - A systematic review.

INTRODUCTION: Magnetic Resonance Imaging (MRI) performs a critical role in breast cancer diagnosis, especially for high-risk populations e.g. family h...

Artificial intelligence versus neurologists: A comparative study on multiple sclerosis expertise.

INTRODUCTION: Multiple sclerosis (MS) is an autoimmune neurodegenerative disease affecting the central nervous system. MS diagnosis is complex, requir...

Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features.

PURPOSE: The International Federation of Gynecology and Obstetric (FIGO) stage is critical to guiding the treatments of ovarian cancer (OC). We tried ...

Intracranial stenosis prediction using a small set of risk factors in the Tromsø Study.

Intracranial atherosclerotic stenosis (ICAS) refers to a narrowing of intracranial arteries due to plaque buildup on the inside of the vessel walls re...

Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [F]PSMA-1007 PET/CT and multiparametric MRI.

PURPOSE: Accurate prediction of adverse pathology (AP) in prostate cancer (PCa) patients is crucial for formulating effective treatment strategies. Th...

Robust and generalizable artificial intelligence for multi-organ segmentation in ultra-low-dose total-body PET imaging: a multi-center and cross-tracer study.

PURPOSE: Positron Emission Tomography (PET) is a powerful molecular imaging tool that visualizes radiotracer distribution to reveal physiological proc...

SegFormer3D: Improving the Robustness of Deep Learning Model-Based Image Segmentation in Ultrasound Volumes of the Pediatric Hip.

Developmental dysplasia of the hip (DDH) is a painful orthopedic malformation diagnosed at birth in 1-3% of all newborns. Left untreated, DDH can lead...

Diagnosis of Acute Appendicitis with Machine Learning-Based Computer Tomography: Diagnostic Reliability and Role in Clinical Management.

Acute appendicitis (AA) is a common surgical emergency affecting 7-8% of the population. Timely diagnosis and treatment are crucial for preventing se...

Multimodal deep learning fusion of ultrafast-DCE MRI and clinical information for breast lesion classification.

BACKGROUND: Breast cancer is the most common cancer worldwide, and magnetic resonance imaging (MRI) constitutes a very sensitive technique for invasiv...

A novel generative model for brain tumor detection using magnetic resonance imaging.

Brain tumors are a disease that kills thousands of people worldwide each year. Early identification through diagnosis is essential for monitoring and ...

Comparison of surgical approaches to the hippocampal formation with artificial intelligence.

The relatively complex functional anatomy of the mediobasal temporal region makes surgical approaches to this area challenging. Several studies descri...

Ensemble fuzzy deep learning for brain tumor detection.

This research presents a novel ensemble fuzzy deep learning approach for brain Magnetic Resonance Imaging (MRI) analysis, aiming to improve the segmen...