AIMC Topic: Aged

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Deep Learning Approaches to Predict Geographic Atrophy Progression Using Three-Dimensional OCT Imaging.

Translational vision science & technology
PURPOSE: To evaluate the performance of various approaches of processing three-dimensional (3D) optical coherence tomography (OCT) images for deep learning models in predicting area and future growth rate of geographic atrophy (GA) lesions caused by ...

[Establishing of mortality predictive model for elderly critically ill patients using simple bedside indicators and interpretable machine learning algorithms].

Zhonghua wei zhong bing ji jiu yi xue
OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assess...

Prediction of Prostate Cancer Grades Using Radiomic Features.

Acta medica Okayama
We developed a machine learning model for predicting prostate cancer (PCa) grades using radiomic features of magnetic resonance imaging. 112 patients diagnosed with PCa based on prostate biopsy between January 2014 and December 2021 were evaluated. L...

Predicting In-Hospital Fall Risk Using Machine Learning With Real-Time Location System and Electronic Medical Records.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: Hospital falls are the most prevalent and fatal event in healthcare, posing significant risks to patient health outcomes and institutional care quality. Real-time location system (RTLS) enables continuous tracking of patient location, pro...

Impact of Hydroxy-Methyl-Butyrate Supplementation on Malnourished Patients Assessed Using AI-Enhanced Ultrasound Imaging.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: This study aimed to evaluate the effects of an oral nutritional supplement (ONS) enriched with hydroxy-methyl-butyrate (HMB) in subjects with disease-related malnutrition (DRM) and to monitor these effects with an ultrasound Imaging Syste...

Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, but its routine clinical use is limited by cost and accessibility barriers.

A Machine Learning Approach to Build and Evaluate a Molecular Prognostic Model for Endometrial Cancer Based on Tumour Microenvironment.

Journal of cellular and molecular medicine
Endometrial cancer (EC) incidence and the associated tumour burden have increased globally. To build a molecular expression prognostic model based on the tumour microenvironment to guide personalised treatment using a machine learning approach. Two d...

Urban-rural disparities in the prevalence and trends of loneliness among Chinese older adults and their associated factors: Evidence from machine learning analysis.

Applied psychology. Health and well-being
In the context of rapid aging development, exploring the predictive factors of older adults' loneliness and its urban-rural differences is of great significance for promoting the psychological health of older adults. This study selected 30 variables ...

Arthroscopy-validated Diagnostic Performance of 7-Minute Five-Sequence Deep Learning Super-Resolution 3-T Shoulder MRI.

Radiology
Background Deep learning (DL) methods enable faster shoulder MRI than conventional methods, but arthroscopy-validated evidence of good diagnostic performance is scarce. Purpose To validate the clinical efficacy of 7-minute threefold parallel imaging ...