Background Deep learning (DL) methods can improve accelerated MRI but require validation against an independent reference standard to ensure robustness and accuracy. Purpose To validate the diagnostic performance of twofold-simultaneous-multislice (S...
This study aimed to develop and validate a nomogram based on lymphocyte subtyping and clinical factors for the early and rapid prediction of Intra-abdominal candidiasis (IAC) in septic patients. A prospective cohort study of 633 consecutive patients ...
Metabolic syndrome (MetS) has a significant impact on health. MetS is the umbrella term for a group of interdependent metabolic threats that contribute to the emergence of diseases that can lead to death. This study was designed to better predict th...
Quantifying pleural effusion change at chest CT is important for evaluating disease severity and treatment response. The purpose of this study was to assess the accuracy of artificial intelligence (AI)-based volume quantification of pleural effusion ...
OBJECTIVE: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI.
OBJECTIVE: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2) and a conventional T...
BACKGROUND: Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based spine-targeting quality assurance system (DL-SpiQA) for detecting treatment at the wrong anatomic lev...
Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiologic assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharm...
BACKGROUND: Caregiver burden significantly affects both patients and caregivers but is often overlooked in clinical practice. Physical and emotional strain on caregivers can compromise the quality of care. Care robots are emerging as solutions to all...
STUDY OBJECTIVES: The demand for cost-effective and accessible alternatives to polysomnography (PSG), the conventional diagnostic method for obstructive sleep apnea (OSA), has surged. In this study, we have developed and validated a deep learning mod...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.