An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Netw...
In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high perform...
OBJECTIVE: To develop a machine learning-based prediction model for incident radiographic osteoarthritis (OA) of the knee over 8 years using MRI-based cartilage biochemical composition and knee joint structure, demographics, and clinical predictors i...
Cardiotoxicity is a severe side effect for colorectal cancer (CRC) patients undergoing fluoropyrimidine-based chemotherapy. To develop and compare machine learning algorithms to predict cardiotoxicity risk among nationally representative CRC patients...
Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data inform...
In current clinical practice, tumor response assessment is usually based on tumor size change on serial computerized tomography (CT) scan images. However, evaluation of tumor response to anti-vascular endothelial growth factor therapies in metastatic...
OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning approach based on clinical factors and diffusion tensor imaging (DTI) to predict anti-seizure medication (ASM) response in focal epilepsy. We hypothesized that ASM r...
BACKGROUND: Number of involved lymph nodes (LNs) is a crucial stratification factor in staging of numerous disease sites, but has not been incorporated for endometrial cancer. We evaluated whether number of involved LNs provide improved prognostic va...
BACKGROUND: Timely recognition of hemodynamic instability in critically ill patients enables increased vigilance and early treatment opportunities. We develop the Hemodynamic Stability Index (HSI), which highlights situational awareness of possible h...