Explainable Semi-supervised Deep Learning Shows That Dementia Is Associated With Small, Avocado-shaped Clocks With Irregularly Placed Hands

This study used the relevance factor variational autoencoder (RF-VAE), a deep generative neural network, to represent digitized clock drawings from multiple institutions. The model identified unique constructional features of clock drawings in an unsupervised manner, which were then examined by domain experts. These features were able to distinguish 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.