AIMC Topic: Middle Aged

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Preventing postoperative moderate- and high-risk pressure injuries with artificial intelligence-powered smart decompression mattress on in middle-aged and elderly patients: a retrospective cohort analysis.

British journal of hospital medicine (London, England : 2005)
Artificial intelligence technology has attained rapid development in recent years. The integration of artificial intelligence applications into pressure reduction mattresses, giving rise to artificial intelligence-powered pressure reduction mattress...

Identification of immune-related biomarkers for intracerebral hemorrhage diagnosis based on RNA sequencing and machine learning.

Frontiers in immunology
BACKGROUND: Intracerebral hemorrhage (ICH) is a severe stroke subtype with high morbidity, disability, and mortality rates. Currently, no biomarkers for ICH are available for use in clinical practice. We aimed to explore the roles of RNAs in ICH path...

Developmental and Validation of Machine Learning Model for Prediction Complication After Cervical Spine Metastases Surgery.

Clinical spine surgery
STUDY DESIGN: This is a retrospective cohort study utilizing machine learning to predict postoperative complications in cervical spine metastases surgery.

Application of a machine learning model for early prediction of in-hospital cardiac arrests: Retrospective observational cohort study.

Medicina intensiva
OBJECTIVE: To describe the results of the application of a Machine Learning (ML) model to predict in-hospital cardiac arrests (ICA) 24 hours in advance in the hospital wards.

A novel algorithm developed using machine learning and a J-ACCESS database can estimate defect scores from myocardial perfusion single-photon emission tomography images.

Annals of nuclear medicine
BACKGROUND: Stress myocardial perfusion single-photon emission computed tomography (SPECT) imaging (MPI) has been used to diagnose and predict the prognoses of patients with coronary artery disease (CAD). An ongoing multicenter collaboration establis...

Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentations.

International journal of mental health nursing
This research addresses the critical issue of identifying factors contributing to admissions to acute mental health (MH) wards for individuals presenting to the emergency department (ED) with MH concerns as their primary issue, notably suicidality. T...

Utilization of an artificial intelligence-enhanced, web-based application to review bile duct brushing cytologic specimens: A pilot study.

Cancer cytopathology
BACKGROUND: The authors previously developed an artificial intelligence (AI) to assist cytologists in the evaluation of digital whole-slide images (WSIs) generated from bile duct brushing specimens. The aim of this trial was to assess the efficiency ...

Deep learning to predict risk of lateral skull base cerebrospinal fluid leak or encephalocele.

International journal of computer assisted radiology and surgery
PURPOSE: Skull base features, including increased foramen ovale (FO) cross-sectional area, are associated with lateral skull base spontaneous cerebrospinal fluid (sCSF) leak and encephalocele. Manual measurement requires skill in interpreting imaging...

Construction and evaluation of a predictive model for the types of sleep respiratory events in patients with OSA based on hypoxic parameters.

Sleep & breathing = Schlaf & Atmung
OBJECTIVE: To explore the differences and associations of hypoxic parameters among distinct types of respiratory events in patients with obstructive sleep apnea (OSA) and to construct prediction models for the types of respiratory events based on hyp...

A deep learning approach for cervical cord injury severity determination through axial and sagittal magnetic resonance imaging segmentation and classification.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
STUDY DESIGN: Cross-sectional Database Study.