Geriatrics

Artificial intelligence classifies primary progressive aphasia from connected speech.

Neurodegenerative dementia syndromes, such as primary progressive aphasias (PPA), have traditionally been diagnosed based, in part, on verbal and non-verbal cognitive profiles. Debate continues about whether PPA is best divided into three variants and regarding the most distinctive linguistic features for classifying PPA variants. In this cross-sectional study, we initially harnessed the capabilit...

MRI-based and metabolomics-based age scores act synergetically for mortality prediction shown by multi-cohort federated learning

Biological age scores are an emerging tool to characterize aging by estimating chronological age based on physiological biomarkers. Various scores have shown associations with aging-related outcomes. This study assessed the relation between an age score based on brain MRI images (BrainAge) and an age score based on metabolomic biomarkers (MetaboAge). We trained a federated deep learning model to...

Automated deep learning segmentation of neuritic plaques and neurofibrillary tangles in Alzheimer disease brain sections using a proprietary software.

Neuropathological diagnosis of Alzheimer disease (AD) relies on semiquantitative analysis of phosphorylated tau-positive neurofibrillary tangles (NFTs...

Enhancing Care for Older Adults and Dementia Patients With Large Language Models: Proceedings of the National Institute on Aging-Artificial Intelligence & Technology Collaboratory for Aging Research Symposium.

Large Language Models (LLMs) stand on the brink of reshaping the field of aging and dementia care, challenging the one-size-fits-all paradigm with the...

Common and unique brain aging patterns between females and males quantified by large-scale deep learning.

There has been extensive evidence that aging affects human brain function. However, there is no complete picture of what brain functional changes are ...

Mechanism of Zuogui pill enhancing ovarian function and skin elastic repair in premature aging rats based on artificial intelligence medical image analysis.

BACKGROUND: AI medical image analysis shows potential applications in research on premature aging and skin. The purpose of this study was to explore t...

Evaluation of End-User Participation in Artificial Intelligence Nursing Projects.

Artificial Intelligence (AI) projects in healthcare, particularly in nursing, currently gain relevance but encounter challenges in user acceptance. Ac...

Using Natural Language Processing on Expert Panel Discussions to Gain Insights for Recruitment, Retention and Intervention Adherence for Online Social Support Interventions on a Stage II-III Clinical Trial Among Hispanic and African American Dementia Caregivers.

We applied natural language processing (NLP) to a corpus extracted from 4 hours of expert panel discussion transcripts to determine the sustainability...

Dementia Ontology Development to Facilitate Collection of High-Quality Dementia Data.

The population of dementia patients is on the rise, as society undergoes rapid aging. This led to an expansion of dementia-related data. This study ai...

Vertebral compression fractures at abdominal CT: underdiagnosis, undertreatment, and evaluation of an AI algorithm.

Vertebral compression fractures (VCFs) are common and indicate a high future risk of additional osteoporotic fractures. However, many VCFs are unrepor...

Bayesian Network Modeling of Causal Influence within Cognitive Domains and Clinical Dementia Severity Ratings for Western and Indian Cohorts

This study investigates the causal relationships between Clinical Dementia Ratings (CDR) and its six domain scores across two distinct aging dataset...

Uncertainty Quantification in Alzheimer's Disease Progression Modeling

With the increasing number of patients diagnosed with Alzheimer's Disease, prognosis models have the potential to aid in early disease detection. Ho...

Omobot: a low-cost mobile robot for autonomous search and fall detection

Detecting falls among the elderly and alerting their community responders can save countless lives. We design and develop a low-cost mobile robot th...

Towards improving Alzheimer's intervention: a machine learning approach for biomarker detection through combining MEG and MRI pipelines

MEG are non invasive neuroimaging techniques with excellent temporal and spatial resolution, crucial for studying brain function in dementia and Alz...

Anatomical Foundation Models for Brain MRIs

Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative disorders. One of th...

A Backbone for Long-Horizon Robot Task Understanding

End-to-end robot learning, particularly for long-horizon tasks, often results in unpredictable outcomes and poor generalization. To address these ch...

A deep spatio-temporal attention model of dynamic functional network connectivity shows sensitivity to Alzheimer's in asymptomatic individuals

Alzheimer's disease (AD) progresses from asymptomatic changes to clinical symptoms, emphasizing the importance of early detection for proper treatme...

Can we screen opportunistically for low bone mineral density using CT scans of the shoulder and artificial intelligence?

OBJECTIVE: To evaluate whether the CT attenuation of bones seen on shoulder CT scans could be used to predict low bone mineral density (BMD) (osteopen...

DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.

Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batc...

Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network.

PURPOSE: Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learni...