OBJECTIVES: To investigate the usefulness of machine learning (ML) models using pretreatment F-FDG-PET-based radiomic features for predicting adverse clinical events (ACEs) in patients with cardiac sarcoidosis (CS).
PURPOSE: To investigate attitudes and perspectives on the use of artificial intelligence (AI) in the assessment of screening mammograms among women invited to BreastScreen Norway.
OBJECTIVE: Identifying participants who will progress to advanced stage in knee osteoarthritis (KOA) trials remains a significant challenge. Current tools, relying on total knee replacements (TKR), fall short in reliability due to the extraneous fact...
The American journal of emergency medicine
Mar 16, 2024
OBJECTIVE: To develop and externally validate models based on neural networks and natural language processing (NLP) to identify suspected serious infections in emergency department (ED) patients afebrile at initial presentation.
OBJECTIVE: Deep learning algorithms have commonly been used for the differential diagnosis between benign and malignant thyroid nodules. The aim of the study described here was to develop an integrated system that combines a deep learning model and a...
BACKGROUND: Accurately predicting survival in patients with cancer is crucial for both clinical decision-making and patient counseling. The primary aim of this study was to generate the first machine-learning algorithm to predict the risk of mortalit...
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Mar 15, 2024
PURPOSE: We present a novel algorithm for the automated detection of cerebral microbleeds (CMBs) on 2D gradient-recalled echo T2* weighted images (T2*WIs). This approach combines a morphology filter bank with a convolutional neural network (CNN) to i...
Journal of imaging informatics in medicine
Mar 15, 2024
Our study aims to evaluate the potential of a deep learning (DL) algorithm for differentiating the signal intensity of bone marrow between osteomyelitis (OM), Charcot neuropathic osteoarthropathy (CNO), and trauma (TR). The local ethics committee app...
OBJECTIVES: We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automati...
BACKGROUND: Artificial intelligence (AI)-enabled sinus rhythm (SR) electrocardiogram (ECG) interpretation can aid in identifying undiagnosed paroxysmal atrial fibrillation (AF) in patients with embolic stroke of undetermined source (ESUS).
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.