Geriatrics

Automated Assessment of Choroidal Mass Dimensions Using Static and Dynamic Ultrasonographic Imaging

To develop and validate an artificial intelligence (AI)-based model that automatically measures choroidal mass dimensions on B□scan ophthalmic ultrasound still images and cine loops. Retrospective diagnostic accuracy study with internal and external validation. The dataset included 1,822 still images and 283 cine loops of choroidal masses for model development and testing. An additional 182 still ...

Identifying Sex-Specific Sub-phenotypes of Alzheimer’s Disease Progression Using Longitudinal Electronic Health Records

Alzheimer’s Disease (AD) is a complex neurodegenerative disorder strongly influenced by sex differences, with women comprising nearly two-thirds of cases. However, sex-specific progression patterns remain underexplored due to unclear clinical and molecular mechanisms. To address this gap, we developed a temporal autoencoder framework to identify sex-specific AD sub-phenotypes using longitudinal el...

Integrating Machine Learning Pipelines for Multimodal Biomarker Prediction in Alzheimer’s and Parkinson’s Disease: A Component of the Neurodiagnoses Framework

Alzheimer’s and Parkinson’s diseases are age-related neurodegenerative diseases that often require invasive procedures for diagnosis. Traditional diag...

Accelerometer-measured weekend catch-up sleep and incident dementia: a prospective cohort study

To investigate whether accelerometer-measured weekend catch-up sleep, defined as extending sleep on weekends to compensate for weekday sleep inadequac...

A deep learning algorithm based on fundus photographs to measure retinal vascular parameters and their additional value beyond the CAIDE risk score for predicting 14-year dementia risk

Retinal photography is a valuable non-invasive tool for assessing the nature of vessel changes. It is of interest whether retinal vascular parameters ...

Comparative Prediction of Psychotic and Mood Disorders with Multi-Model Machine Learning

Recently, there has been a surge in the number of mental health cases including paranoid schizophrenia (psychosis) and depression (mood disorder). Thi...

Machine learning algorithm to predict fragility fractures and identification of important features – an explainable approach

In this study, we developed ML algorithms to predict fragility fractures, considering the occurrence of fractures at different skeletal sites. We inve...

Multi-organ AI Endophenotypes Chart the Heterogeneity of Pan-disease in the Brain, Eye, and Heart

Disease heterogeneity and commonality pose significant challenges to precision medicine, as traditional approaches frequently focus on single disease ...

Radiologist-AI Collaboration for Ischemia Diagnosis in Small Bowel Obstruction: Multicentric Development and External Validation of a Multimodal Deep Learning Model

To develop and externally validate a multimodal AI model for detecting ischaemia complicating small-bowel obstruction (SBO). We combined 3D CT data wi...

Evaluating the Generalizability of EEG-Based AI Models in Alzheimer’s and Dementia Diagnosis

We thoroughly investigated the generalizability of deep learning models trained on electroencephalography (EEG) data to detect Alzheimer’s disease and...

Advancing Human Population Genomics with DNA Foundation Models

DNA foundation models offer a new approach to interpret genetic variation, but their potential in population-scale genomics remains untapped. We intro...

An Indicator Cell Assay-based Multivariate Blood Test for Early Detection of Alzheimer’s Disease

The indicator cell assay platform (iCAP) is a novel next-generation approach for blood-based diagnostics that uses standardized cells as biosensors to...

An Unsupervised XAI Framework for Dementia Detection with Context Enrichment

Explainable Artificial Intelligence (XAI) methods enhance the diagnostic efficiency of clinical decision support systems by making the predictions of ...

Artificial Intelligence in Reminiscence Therapy for Older Adults: A Systematic Review Protocol

The global aging population faces increasing challenges related to cognitive decline, social isolation, and psychological well-being. Reminiscence the...

Identification of Key Genes Governing the Effects of Physical Activity on Ferroptosis in Alzheimer’s Disease Patients: A Machine Learning-Based Study

Disrupted brain iron metabolism and activated ferroptosis during ageing constitute significant precursors to neurodegenerative diseases. However, whet...

Dementia Risk and Machine Learning-Derived Brain Age Index from Sleep Electroencephalography: A Pooled Cohort Analysis of Over 7,000 Individuals Across Five Community Cohorts

Sleep electroencephalographic (EEG) microstructures are closely related to cognition and undergo age-dependent changes. However, their multidimensiona...

Using deep learning to improve genetic studies of osteoporosis

To evaluate how recent advances in deep learning can improve the construction of quantitative phenotypes for genome-wide association studies (GWAS), w...

Incidentally discovered Covert Cerebrovascular Disease by CT versus MRI: Agreement and Prognostic Value for Stroke and Dementia in a Large Real-World Cohort

Covert cerebrovascular disease (CCD), comprising covert brain infarction (CBI) and white matter disease (WMD), is common in older adults and linked to...

A randomized clinical trial reveals effects of mindfulness and slow breathing on plasma amyloid beta levels

Prior research suggests that meditation may slow brain aging and reduce the risk of Alzheimer’s disease (AD). However, we lack research systematically...

Predicting Future Brain Atrophy Based on Longitudinal MRI

Neuron loss is a key feature of neurodegenerative diseases often leading to brain atrophy detectable through magnetic resonance imaging (MRI). Various...