AIMC Topic: Adult

Clear Filters Showing 13391 to 13400 of 15606 articles

Multi-class brain malignant tumor diagnosis in magnetic resonance imaging using convolutional neural networks.

Brain research bulletin
Glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) are common malignant brain tumors with similar radiological features, while the accurate and non-invasive dialgnosis is essential for selecting appropriate...

Generating Synthetic T2*-Weighted Gradient Echo Images of the Knee with an Open-source Deep Learning Model.

Academic radiology
RATIONALE AND OBJECTIVES: Routine knee MRI protocols for 1.5 T and 3 T scanners, do not include T2*-w gradient echo (T2*W) images, which are useful in several clinical scenarios such as the assessment of cartilage, synovial blooming (deposition of he...

Predicting 5-Year EDSS in Multiple Sclerosis with LSTM Networks: A Deep Learning Approach to Disease Progression.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKROUNDS: Multiple Sclerosis (MS) is a neurodegerative disease that is common worldwide, has no definitive cure yet, and negatively affects the individual's quality of life due to disease-related disability. Predicting disability in MS is difficult...

MVMD-TCCA: A method for gesture classification based on surface electromyographic signals.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Gesture recognition plays a fundamental role in enabling nonverbal communication and interaction, as well as assisting individuals with motor impairments in performing daily tasks. Surface electromyographic (sEMG) signals, which can effectively detec...

IdenBAT: Disentangled representation learning for identity-preserved brain age transformation.

Artificial intelligence in medicine
Brain age transformation aims to convert reference brain images into synthesized images that accurately reflect the age-specific features of a target age group. The primary objective of this task is to modify only the age-related attributes of the re...

Predicting hepatocellular carcinoma response to TACE: A machine learning study based on 2.5D CT imaging and deep features analysis.

European journal of radiology
OBJECTIVES: Prior to the commencement of treatment, it is essential to establish an objective method for accurately predicting the prognosis of patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE). In this st...

Evaluating the National Early Warning Score (NEWS) in triage: A machine learning perspective.

International emergency nursing
BACKGROUND: The National Early Warning Score is widely used in Emergency Departments for triage, primarily to predict mortality. However, its effectiveness in assessing additional clinical outcomes relevant to triage, such as patient urgency and seve...

Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Building upon our previous work on predicting chronic opioid use using electronic health records (EHR) and wearable data, this study leveraged the Health Equity Across the AI Lifecycle (HEAAL) framework to (a) fine tune the previously buil...

Artificial intelligence (AI) use for personal protective equipment training, remediation, and education in health care.

American journal of infection control
BACKGROUND: Personal protective equipment (PPE) is a first-line transmission-based precaution for reducing the spread of nosocomial infections between health care workers (HCWs), patients, and staff. The COVID-19 pandemic highlighted a problematic sk...