Background Due to conflicting findings in the literature, there are concerns about a lack of objectivity in grading knee osteoarthritis (KOA) on radiographs. Purpose To examine how artificial intelligence (AI) assistance affects the performance and i...
AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals, collected within 24 h of ICU admission, for traumatic brain injury (TBI) patient outcomes.
Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES
Jul 1, 2024
BACKGROUND: This study aimed to develop and validate an artificial intelligence model using machine learning (ML) to predict hospital mortality in patients with acute mesenteric ischemia (AMI).
AIM: This study aimed to explore the mechanisms of transient receptor potential (TRP) channels on the immune microenvironment and develop a TRP-related signature for predicting prognosis, immunotherapy response, and drug sensitivity in gliomas.
BACKGROUND: Kidney transplantation is a complex process requiring extensive preparation and ongoing monitoring. Artificial intelligence (AI)-powered chatbots hold potential for providing accessible health information, but our understanding of their r...
PURPOSE: This study was conducted to develop a convolutional neural network (CNN) algorithm that can diagnose cervical foraminal stenosis using oblique radiographs and evaluate its accuracy.
BACKGROUND: The myocardial-ischaemic-injury-index (MI) is a novel machine learning algorithm for the early diagnosis of type 1 non-ST-segment elevation myocardial infarction (NSTEMI). The performance of MI, both when using early serial blood draws (e...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.