A longitudinal spontaneous speech (machine learning audio) dataset for dementia diagnosis.

Brain white matter hyperintensity segmentation, with T1 and FLAIR MRI images, using UNet.

Code for NeuraHealth: An Automated Screening Pipeline to Detect Undiagnosed Cognitive Impairment in Electronic Health Records using Deep Learning and Natural Language Processing

a low-cost, convenient, remote, and consistent platform for research into Dementia

Better dementia diagnosis through data

To provide a complete pipeline to develop a deep learning model. More specifically, deep learning for computer vision (2D). Using a multiclass image classification (single label) deep learning model that can predict what stage (refer below) of Alzheimer's a patient is, from their MRI image. This study will focus on CNN Verus DCGAN (Generative)

To provide a complete pipeline to develop a deep learning model. More specifically, a multiclass classification (single label) deep learning model that can predict what stage of Alzheimer's a patient is, from their MRI image

Msc. Thesis: Revising the clinical criteria for Dementia using explainable machine learning.

Repository of code used to generate data in Mansel et al., 2025 "Distinct Medical and Substance Use Histories Associate with Cognitive Decline in Alzheimer's Disease"

Final project for AC209A: Data Science 1 @ Harvard University. This project aims to understand how different factors affect whether a person develops dementia by utilizing various ML models to predict diagnosis outcomes.

Alzheimer's Disease Classification Research performed at University of Cagliari in 2020.

UK dementia diagnoses rise 40% in five years

Project to examine dataset of dementia patients and predict likelihood of dementia in future patients