Background: With the advance of technology, our capacity to assess patients with dementia is also developing. It is possible to administer cognitive examinations using technology, such as the iPad-based Toronto Cognitive Assessment, but hitherto difficult to autonomously administer them. Many of the ’inputs’ from patients could be easily scored with software, but highly variable inputs such as the clock drawing are extremely difficult to score, precluding automated administration and scoring. This work focuses on the development of a neural network designed to assess cube drawings, infinity drawings, and clock drawings. Methods: 3200 drawings, evenly split between clocks, cubes and infinities were generated, with half being correct and half incorrect. A SqueezeNet was trained on 2000 images, validated on 800 drawings, and then tested on 400 drawings. Results: The SqueezeNet was able to achieve 97% accuracy on 400 images it had never seen before in categorizing images as “Cube”, “Clock”,

“Infinity”, or “Other” (incorrectly drawn). Conclusions: This neural network can successfully determine the difference between correctly and incorrectly drawn images commonly used in cognitive examinations, overcoming the final barrier to autonomously administering and scoring cognitive examinations. Next steps are to clinically validate an autonomous examination program which has been modeled after the Addenbrooke Cognitive Examination-3.