IMPORTANCE: Meticulous postoperative flap monitoring is essential for preventing flap failure and achieving optimal results in free flap operations, for which physical examination has remained the criterion standard. Despite the high reliability of p...
PURPOSE: To examine whether there is a significant difference in image quality between the deep learning reconstruction (DLR [AiCE, Advanced Intelligent Clear-IQ Engine]) and hybrid iterative reconstruction (HIR [AIDR 3D, adaptive iterative dose redu...
. This study examines the value of ventricular repolarization using QT dynamicity for two different types of atrial fibrillation (AF) prediction.. We studied the importance of QT-dynamicity (1) in the detection and (2) the onset prediction (i.e. fore...
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Jul 1, 2024
Transthyretin cardiac amyloidosis (ATTR CA) is increasingly recognized as a cause of heart failure in older patients, with Tc-pyrophosphate imaging frequently used to establish the diagnosis. Visual interpretation of SPECT images is the gold standard...
International journal of medical informatics
Jun 30, 2024
OBJECTIVES: This study aims to evaluate the fairness performance metrics of Machine Learning (ML) models to predict hospitalization and emergency department (ED) visits in heart failure patients receiving home healthcare. We analyze biases, assess pe...
BACKGROUND: Morphological properties such as length and tortuosity of the large intestine segments play important roles, especially in interventional procedures like colonoscopy.
PURPOSE: To evaluate the image quality of ultra-high-resolution CT (U-HRCT) images reconstructed using an improved deep-learning-reconstruction (DLR) method. Additionally, we assessed the utility of U-HRCT in visualizing gastric wall structure, detec...
PURPOSE: This study aims to develop and validate a prediction model for delirium in elderly ICU patients and help clinicians identify high-risk patients at the early stage.
OBJECTIVE: To develop a whole-body low-dose CT (WBLDCT) deep learning model and determine its accuracy in predicting the presence of cytogenetic abnormalities in multiple myeloma (MM).