Automatic Scoring of Cognition Drawings: Assessing the Quality of Machine-Based Scores Against a Gold Standard
This addresses data quality issues in large-scale dementia screening programs where non-clinical interviewers score drawings, though it's an incremental application of existing methods to a new domain.
The paper tackles the problem of automating scoring of cognitive drawings used in dementia screening, showing that deep learning models trained on gold standard data achieve 85% accuracy - 5 percentage points higher than human interviewers.
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.