PURPOSE: To examine the performance of deep-learning models that predicts the visual acuity after cataract surgery using preoperative clinical information and color fundus photography (CFP).
PURPOSE: To investigate the behavior of artificial intelligence (AI) CT-based body composition biomarkers at different virtual monoenergetic imaging (VMI) levels using dual-energy CT (DECT).
BACKGROUND: Symptomatic intracranial hemorrhage (sICH) after mechanical thrombectomy (MT) is associated with worse outcomes. We sought to develop and internally validate a machine learning (ML) model to predict sICH prior to MT in patients with anter...
Organic acids reflect the course of all important metabolic processes and the effects of diet, nutrient deficiency, lifestyle, and microbiota composition. In present work, we focused on identifying age-related changes in organic acids in urine, and c...
Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. Successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, requires large amounts of...
IEEE journal of biomedical and health informatics
Dec 5, 2024
The Otago Exercise Program (OEP) represents a crucial rehabilitation initiative tailored for older adults, aimed at enhancing balance and strength. Despite previous efforts utilizing wearable sensors for OEP recognition, existing studies have exhibit...
IEEE journal of biomedical and health informatics
Dec 5, 2024
Graph Neural Networks (GNNs) play a pivotal role in learning representations of brain networks for estimating brain age. However, the over-squashing impedes interactions between long-range nodes, hindering the ability of message-passing mechanism-bas...
BACKGROUND: Care home residents are a highly vulnerable group, but identifying care home residents in routine data is challenging. This study aimed to develop and validate Natural Language Processing (NLP) methods to identify care home residents from...
Previous models of depression outcomes have been limited by symptom heterogeneity within populations. This study conducted a retrospective analysis using latent growth mixture models to identify heterogeneous trajectories within a clinical population...
The early detection of Alzheimer's Disease (AD) is thought to be important for effective intervention and management. Here, we explore deep learning methods for the early detection of AD. We consider both genetic risk factors and functional magnetic ...