Automating clinical assessments of memory deficits: Deep Learning based scoring of the Rey-Osterrieth Complex Figure

Abstract

Memory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state-of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patient's ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinician's experience, motivation and tiredness. Here, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, a multi-head convolutional neural network was trained on 20225 ROCF drawings. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. The neural network outperforms both online raters and clinicians. Our AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably and time-efficiently the performance in the ROCF test from hand-drawn images.

Interpretable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons in older adults

“…Automated scoring methods that utilize machine learning methods offer the potential to address some or all of these weaknesses. For example, deep-learning techniques have demonstrated promising performance in automating ratings for the PDT 7,8 , Rey Complex Figure Test 9,10 , and Clock Drawing Test 11,12 . However, the primary objective of the previous automated scoring is to reproduce human-based conventional ratings and few machine learning approaches directly predict cognitive performance from drawings 13 .…”