Applying Variational Autoencoder to Explore Changes in Clock Drawing With Hip Fracture Surgery

Older adults with acute hip fractures are particularly vulnerable to delirium and postoperative cognitive decline. Hospital staff, however, do not screen preoperative cognition or systematically monitor for delirium in these at‐risk individuals. Using the clock drawing test (CDT) our team demonstrated the potential for semi‐supervised deep learning using Variational Autoencoder (VAE) to extract clock drawing anomalies predictive of dementia (Bandyopadhyay, in review). We applied the VAE technique to assess preoperative to postoperative changes in CDTs in patients with acute hip fractures.