Geriatrics

ITCFN: Incomplete Triple-Modal Co-Attention Fusion Network for Mild Cognitive Impairment Conversion Prediction

Alzheimer's disease (AD) is a common neurodegenerative disease among the elderly. Early prediction and timely intervention of its prodromal stage, mild cognitive impairment (MCI), can decrease the risk of advancing to AD. Combining information from various modalities can significantly improve predictive accuracy. However, challenges such as missing data and heterogeneity across modalities compli...

Self-CephaloNet: A Two-stage Novel Framework using Operational Neural Network for Cephalometric Analysis

Cephalometric analysis is essential for the diagnosis and treatment planning of orthodontics. In lateral cephalograms, however, the manual detection of anatomical landmarks is a time-consuming procedure. Deep learning solutions hold the potential to address the time constraints associated with certain tasks; however, concerns regarding their performance have been observed. To address this critic...

Region-wise stacking ensembles for estimating brain-age using MRI

Predictive modeling using structural magnetic resonance imaging (MRI) data is a prominent approach to study brain-aging. Machine learning algorithms...

Deep Learning for Early Alzheimer Disease Detection with MRI Scans

Alzheimer's Disease is a neurodegenerative condition characterized by dementia and impairment in neurological function. The study primarily focuses ...

IFRA: a machine learning-based Instrumented Fall Risk Assessment Scale derived from Instrumented Timed Up and Go test in stroke patients

Effective fall risk assessment is critical for post-stroke patients. The present study proposes a novel, data-informed fall risk assessment method b...

On the challenges of detecting MCI using EEG in the wild

Recent studies have shown promising results in the detection of Mild Cognitive Impairment (MCI) using easily accessible Electroencephalogram (EEG) d...

TimeFlow: Longitudinal Brain Image Registration and Aging Progression Analysis

Predicting future brain states is crucial for understanding healthy aging and neurodegenerative diseases. Longitudinal brain MRI registration, a cor...

Head Motion Degrades Machine Learning Classification of Alzheimer's Disease from Positron Emission Tomography

Brain positron emission tomography (PET) imaging is broadly used in research and clinical routines to study, diagnose, and stage Alzheimer's disease...

DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI Reconstruction

The accelerated MRI reconstruction poses a challenging ill-posed inverse problem due to the significant undersampling in k-space. Deep neural networ...

Combining imaging and shape features for prediction tasks of Alzheimer's disease classification and brain age regression

We investigate combining imaging and shape features extracted from MRI for the clinically relevant tasks of brain age prediction and Alzheimer's dis...

3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene Understanding

Multi-modal Large Language Models (MLLMs) exhibit impressive capabilities in 2D tasks, yet encounter challenges in discerning the spatial positions,...

Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data

By 2030, the senior population aged 65 and older is expected to increase by over 50%, significantly raising the number of older drivers on the road....

Deep Learning on Hester Davis Scores for Inpatient Fall Prediction

Fall risk prediction among hospitalized patients is a critical aspect of patient safety in clinical settings, and accurate models can help prevent a...

BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systems

Creating end-to-end bioinformatics workflows requires diverse domain expertise, which poses challenges for both junior and senior researchers as it ...

Text-to-Edit: Controllable End-to-End Video Ad Creation via Multimodal LLMs

The exponential growth of short-video content has ignited a surge in the necessity for efficient, automated solutions to video editing, with challen...

MRI Patterns of the Hippocampus and Amygdala for Predicting Stages of Alzheimer's Progression: A Minimal Feature Machine Learning Framework

Alzheimer's disease (AD) progresses through distinct stages, from early mild cognitive impairment (EMCI) to late mild cognitive impairment (LMCI) an...

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT

Objective: There exist several X-ray computed tomography (CT) scanning strategies to reduce a radiation dose, such as (1) sparse-view CT, (2) low-do...

Motif Discovery Framework for Psychiatric EEG Data Classification

In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response ...

Application of machine learning for detecting high fall risk in middle-aged workers using video-based analysis of the first 3 steps.

OBJECTIVES: Falls are among the most prevalent workplace accidents, necessitating thorough screening for susceptibility to falls and customization of ...

Survival Analysis Revisited: Understanding and Unifying Poisson, Exponential, and Cox Models in Fall Risk Analysis

This paper explores foundational and applied aspects of survival analysis, using fall risk assessment as a case study. It revisits key time-related ...