INTRODUCTION: Intermediate-high-risk pulmonary embolism (PE) patients face elevated risks of sudden clinical deterioration in early hours after symptoms onset. We performed a hierarchical cluster analysis among intermediate-high risk PE patients to i...
Despite the remarkable achievements of deep learning networks in analyzing neuroimaging data for various tasks linked to brain functions and disorders, the opaque nature of these models and their interpretability challenges pose significant barriers ...
BACKGROUND: Robotic central pancreatectomy is increasingly used for pre- or low-grade malignant tumors in the pancreatic body balancing preservation of pancreatic function while removing the target lesion. Today, there is no established reconstructio...
PURPOSE: To investigate the image quality of deep learning-reconstructed T2-weighted half-Fourier single-shot turbo spin echo (DL T2 HASTE) and contrast-enhanced T1-weighted volumetric interpolated breath-hold examination (DL T1 VIBE) of magnetic res...
UNLABELLED: Lung adenocarcinoma (LUAD) is a heterogeneous disease with substantial genomic differences between individuals of Chinese and European ancestries. Deciphering the timing of driver mutations may lead to insights into tumor evolution that c...
Nomograms are commonly used in oncology to assist clinicians in individualized decision-making processes, such as considering sentinel node biopsy (SNB) for melanoma patients. Concurrently, artificial intelligence (AI) is increasingly being utilized ...
OBJECTIVE: To evaluate the value of combining American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) with the Demetics ultrasound diagnostic system in reducing the rate of fine-needle aspiration (FNA) biopsies for thy...
OBJECTIVE: The aim of this investigation is to assess the clinical usefulness of a machine learning model using contrast-enhanced ultrasound (CEUS) radiomics in discriminating clear cell renal cell carcinoma (ccRCC) from non-ccRCC.
BACKGROUND: Central line--associated bloodstream infections (CLABSI) are major causes of morbidity and mortality in intensive care units. This study aimed to develop an artificial intelligence-driven predictive model for CLABSI within 2 calendar days...
OBJECTIVE: To determine whether a machine learning model of voxel level [f]fluorodeoxyglucose positron emission tomography (PET) data could predict progressive supranuclear palsy (PSP) pathology, as well as outperform currently available biomarkers.
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.