ArticleScientific reports2022
Variational autoencoder provides proof of concept that compressing CDT to extremely low-dimensional space retains its ability of distinguishing dementia.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Perioperative Artificial Intelligence Driven Integrated Modeling of Surgeries using Anesthetic, Physical and Cognitive Statuses for Predicting Hospital Outcomes.Research square · 2025Article
- Recent Advancements in Neuroimaging-Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e-Health: A Systematic Review.Health science reports · 2025Review
- Developing a fair and interpretable representation of the clock drawing test for mitigating low education and racial bias.Scientific reports · 2024Article
- MRI-based mild cognitive impairment and Alzheimer's disease classification using an algorithm of combination of variational autoencoder and other machine learning classifiers.Journal of Alzheimer's disease reports · 2024Article
- Digital Clock and Recall: a digital, process-driven evolution of the Mini-Cog.Frontiers in human neuroscience · 2024Article
- Attentive pairwise interaction network for AI-assisted clock drawing test assessment of early visuospatial deficits.Scientific reports · 2023Article
- FaIRClocks: Fair and Interpretable Representation of the Clock Drawing Test for mitigating classifier bias against lower educational groups.Research square · 2023Article
- A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to mandible shape.NPJ systems biology and applications · 2023Article
- Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands.Scientific reports · 2023Article
- Using digital assessment technology to detect neuropsychological problems in primary care settings.Frontiers in psychology · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
The clock drawing test (CDT) is an inexpensive tool to screen for dementia. In this study, we examined if a variational autoencoder (VAE) with only two latent variables can capture and encode clock drawing anomalies from a large dataset of unannotated CDTs (n = 13,580) using self-supervised pre-training and use them to classify dementia CDTs (n = 18) from non-dementia CDTs (n = 20). The model was independently validated using a larger cohort consisting of 41 dementia and 50 non-dementia clocks. The classification model built with the parsimonious VAE latent space adequately classified dementia from non-dementia (0.78 area under receiver operating characteristics (AUROC) in the original test dataset and 0.77 AUROC in the secondary validation dataset). The VAE-identified atypical clock features were then reviewed by domain experts and compared with existing literature on clock drawing errors. This study shows that a very small number of latent variables are sufficient to encode important clock drawing anomalies that are predictive of dementia.
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