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Figure drawing is often used as part of dementia screening protocols. TheSurvey of Health Aging and Retirement in Europe (SHARE) has adopted threedrawing tests from Addenbrooke's Cognitive Examination III as part of itsquestionnaire module on cognition. While the drawings are usually scored bytrained clinicians, SHARE uses the face-to-face interviewers who conduct theinterviews to score the drawings during fieldwork. This may pose a risk to dataquality, as interviewers may be less consistent in their scoring and morelikely to make errors due to their lack of clinical training. This papertherefore reports a first proof of concept and evaluates the feasibility ofautomating scoring using deep learning. We train several differentconvolutional neural network (CNN) models using about 2,000 drawings from the8th wave of the SHARE panel in Germany and the corresponding interviewerscores, as well as self-developed 'gold standard' scores. The results suggestthat this approach is indeed feasible. Compared to training on interviewerscores, models trained on the gold standard data improve prediction accuracy byabout 10 percentage points. The best performing model, ConvNeXt Base, achievesan accuracy of about 85%, which is 5 percentage points higher than the accuracyof the interviewers. While this is a promising result, the models stillstruggle to score partially correct drawings, which are also problematic forinterviewers. This suggests that more and better training data is needed toachieve production-level prediction accuracy. We therefore discuss possiblenext steps to improve the quality and quantity of training examples.

Automatic Scoring of Cognition Drawings: Assessing the Quality of Machine-Based Scores Against a Gold Standard