AIMC Topic: Aged, 80 and over

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Development of machine learning models for prediction of current and future dementia.

PloS one
Dementia is among the most distressing and burdensome health challenges in aging populations. Treatment efficacy is limited; however, early diagnosis can delay or prevent disease progression. Previous machine learning-based prediction models have lim...

Construction and validation of the prediction model for kinesiophobia in older adults with chronic low back pain.

BMC geriatrics
BACKGROUND: Low back pain imposes a substantial burden on global healthcare systems. Kinesiophobia is highly prevalent among older adults with chronic low back pain, severely hindering effective intervention and treatment. However, current assessment...

Mammo-AGE: deep learning estimation of breast age from mammograms.

Nature communications
Biological age is an important indicator of organ functions and health. Although mammograms are widely used in breast cancer screening, the potential of mammogram-based biological age predictors remains underexplored. Here, we propose a deep learning...

Association of the endothelial activation and stress index with cognitive function in older adults: a cross-sectional study with machine learning.

European journal of medical research
BACKGROUND: Age-associated memory impairment (AAMI) is a predementia state linked to endothelial dysfunction. The endothelial activation and stress index (EASIX) quantifies endothelial injury, yet its association with cognitive function remains unval...

Machine Learning-Based Prediction of In-Hospital Falls in Adult Inpatients: Retrospective Observational Multicenter Study.

JMIR medical informatics
BACKGROUND: Falls among hospitalized patients are a critical issue that often leads to prolonged hospital stays and increased health care costs. Traditional fall risk assessments typically rely on standardized scoring systems; however, these may fail...

Both Infarcted and Noninfarcted Brain Regions Contribute to Deep Learning-Based MRI Prediction of Acute Stroke Outcome.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Predicting long-term clinical outcomes based on early acute ischemic stroke (AIS) information would be useful for many reasons, including patient counseling and clinical trial execution. This study investigates how different r...

Artificial Intelligence-Driven Detection of Large Vessel Occlusions on NCCT: A Multi-Institutional Study.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Imaging triage of stroke patients is primarily based on perfusion imaging. Simplified triage based on non-contrast CT are limited (NCCT). To evaluate the predictive capability of a deep learning algorithm, "Triage Stroke" (Bra...

Temporal shifts in prognostic factors for 90- and 180-day outcomes after stroke thrombolysis: A machine learning analysis.

PloS one
INTRODUCTION: Prognostication at 90 and 180 days after thrombolysis for acute ischemic stroke (AIS) is critical, yet the temporal evolution of key predictors remains inadequately understood. The utility of machine learning for systematically comparin...

Integrating a Large Language Model Into a Socially Assistive Robot in a Hospital Geriatric Unit: Two-Wave Comparative Study on Performance, Engagement, and User Perceptions.

JMIR human factors
BACKGROUND: Addressing the complex medical and psychosocial needs of older adults is increasingly difficult in resource-limited care settings. In this context, socially assistive robots (SARs) provide support and practical functions such as orientati...

[Tc]Tc-Sestamibi/[Tc]NaTcO Subtraction SPECT of Parathyroid Glands Using Analysis of Principal Components.

Journal of nuclear medicine technology
The aim of the study was to validate a new method for semiautomatic subtraction of [Tc]Tc-sestamibi and [Tc]NaTcO SPECT 3-dimensional datasets using principal component analysis (PCA) against the results of parathyroid surgery and to compare its perf...