AIMC Topic: Aged

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Automatic Detection of Fatigued Gait Patterns in Older Adults: An Intelligent Portable Device Integrating Force and Inertial Measurements with Machine Learning.

Annals of biomedical engineering
PURPOSE: This study aimed to assess the feasibility of early detection of fatigued gait patterns for older adults through the development of a smart portable device.

Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment.

European journal of nuclear medicine and molecular imaging
PURPOSE: Respiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven respiratory motion correction (uRMC) method, utilizing deep learning neural networks, to solve all th...

Automatic deep learning segmentation of the hippocampus on high-resolution diffusion magnetic resonance imaging and its application to the healthy lifespan.

NMR in biomedicine
Diffusion tensor imaging (DTI) can provide unique contrast and insight into microstructural changes with age or disease of the hippocampus, although it is difficult to measure the hippocampus because of its comparatively small size, location, and sha...

Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An interpretable machine learning framework.

Accident; analysis and prevention
The causes of traffic violations by elderly drivers are different from those of other age groups. To reduce serious traffic violations that are more likely to cause serious traffic crashes, this study divided the severity of traffic violations into t...

A Novel Artificial Intelligence-Based Parameterization Approach of the Stromal Landscape in Merkel Cell Carcinoma: A Multi-Institutional Study.

Laboratory investigation; a journal of technical methods and pathology
Tumor-stroma ratio (TSR) has been recognized as a valuable prognostic indicator in various solid tumors. This study aimed to examine the clinicopathologic relevance of TSR in Merkel cell carcinoma (MCC) using artificial intelligence (AI)-based parame...

Characterization of cardiac resynchronization therapy response through machine learning and personalized models.

Computers in biology and medicine
INTRODUCTION: The characterization and selection of heart failure (HF) patients for cardiac resynchronization therapy (CRT) remain challenging, with around 30% non-responder rate despite following current guidelines. This study aims to propose a nove...

Preventive machine learning models incorporating health checkup data and hair mineral analysis for low bone mass identification.

Scientific reports
Machine learning (ML) models have been increasingly employed to predict osteoporosis. However, the incorporation of hair minerals into ML models remains unexplored. This study aimed to develop ML models for predicting low bone mass (LBM) using health...

Machine learning-based model to predict composite thromboembolic events among Chinese elderly patients with atrial fibrillation.

BMC cardiovascular disorders
OBJECTIVE: Accurate prediction of survival prognosis is helpful to guide clinical decision-making. The aim of this study was to develop a model using machine learning techniques to predict the occurrence of composite thromboembolic events (CTEs) in e...

Movement- and Posture-based Measures of Sedentary Patterns and Associations with Metabolic Syndrome in Hispanic/Latino and non-Hispanic Adults.

Journal of racial and ethnic health disparities
BACKGROUND: Sedentary behavior has been identified as a significant risk factor for Metabolic Syndrome (MetS). However, it is unclear if the sedentary pattern measurement approach (posture vs. movement) impacts observed associations or if association...