  Figure drawing is often used as part of dementia screening protocols. The
Survey of Health Aging and Retirement in Europe (SHARE) has adopted three
drawing tests from Addenbrooke's Cognitive Examination III as part of its
questionnaire module on cognition. While the drawings are usually scored by
trained clinicians, SHARE uses the face-to-face interviewers who conduct the
interviews to score the drawings during fieldwork. This may pose a risk to data
quality, as interviewers may be less consistent in their scoring and more
likely to make errors due to their lack of clinical training. This paper
therefore reports a first proof of concept and evaluates the feasibility of
automating scoring using deep learning. We train several different
convolutional neural network (CNN) models using about 2,000 drawings from the
8th wave of the SHARE panel in Germany and the corresponding interviewer
scores, as well as self-developed 'gold standard' scores. The results suggest
that this approach is indeed feasible. Compared to training on interviewer
scores, models trained on the gold standard data improve prediction accuracy by
about 10 percentage points. The best performing model, ConvNeXt Base, achieves
an accuracy of about 85%, which is 5 percentage points higher than the accuracy
of the interviewers. While this is a promising result, the models still
struggle to score partially correct drawings, which are also problematic for
interviewers. This suggests that more and better training data is needed to
achieve production-level prediction accuracy. We therefore discuss possible
next steps to improve the quality and quantity of training examples.
