Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands

The clock drawing test is a simple and inexpensive method to screen for cognitive frailties, including dementia. In this study, we used the relevance factor variational autoencoder (RF-VAE), a deep generative neural network, to represent digitized clock drawings from multiple institutions using an o...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports Vol. 13; no. 1; pp. 7384 - 12
Main Authors: Bandyopadhyay, Sabyasachi, Wittmayer, Jack, Libon, David J., Tighe, Patrick, Price, Catherine, Rashidi, Parisa
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 06.05.2023
Nature Publishing Group
Nature Portfolio
Subjects:
631/114/1305
631/477
639/166/985
639/705/1042
692/308/53/2421
692/53/2421
692/617/375/132
692/699/375/132
Clocks & watches
Cognitive ability
Deep Learning
Dementia
Dementia disorders
Humanities and Social Sciences
Humans
multidisciplinary
Neural networks
Neural Networks, Computer
Neuropsychological Tests
Persea
Science
Science (multidisciplinary)
Supervised Machine Learning
ISSN:2045-2322, 2045-2322
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The clock drawing test is a simple and inexpensive method to screen for cognitive frailties, including dementia. In this study, we used the relevance factor variational autoencoder (RF-VAE), a deep generative neural network, to represent digitized clock drawings from multiple institutions using an optimal number of disentangled latent factors. The model identified unique constructional features of clock drawings in a completely unsupervised manner. These factors were examined by domain experts to be novel and not extensively examined in prior research. The features were informative, as they distinguished dementia from non-dementia patients with an area under receiver operating characteristic (AUC) of 0.86 singly, and 0.96 when combined with participants’ demographics. The correlation network of the features depicted the “ typical dementia clock ” as having a small size, a non-circular or “avocado-like” shape, and incorrectly placed hands. In summary, we report a RF-VAE network whose latent space encoded novel constructional features of clocks that classify dementia from non-dementia patients with high performance.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-34518-9