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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Scientific reports Ročník 13; číslo 1; s. 7384 - 12
Hlavní autoři:	Bandyopadhyay, Sabyasachi, Wittmayer, Jack, Libon, David J., Tighe, Patrick, Price, Catherine, Rashidi, Parisa
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 06.05.2023 Nature Publishing Group Nature Portfolio
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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.
Bibliografie:	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