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  A deep learning approach for automated scoring of the Rey–Osterrieth complex figure

Langer, N., Weber, M., Hebling Vieira, B., Strzelczyk, D., Wolf, L., Pedroni, A., et al. (2024). A deep learning approach for automated scoring of the Rey–Osterrieth complex figure. eLife, 13: RP9601. doi:10.7554/eLife.96017.3.sa0.

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 Creators:
Langer, N, Author
Weber, M, Author
Hebling Vieira, B, Author
Strzelczyk, D, Author
Wolf, L, Author
Pedroni, A, Author
Heitz, J, Author
Müller, S, Author
Schultheiss, C, Author
Troendle, M, Author
Arango Lasprilla, JC, Author
Rivera, D, Author
Scarpina, F, Author
Zhao, Q, Author
Leuthold, R, Author
Wehrle, F, Author
Jenni, O, Author
Brugger, P, Author
Zaehle, T, Author
Lorenz, R1, Author                 
Zhang, C, Author more..
Affiliations:
1Research Group Cognitive Neuroscience & Neurotechnology, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3531517              

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 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, we collected more than 20k hand-drawn ROCF drawings from patients with various neurological and psychiatric disorders as well as healthy participants. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. This dataset was used to train and evaluate a multihead convolutional neural network. The model performs highly unbiased as it yielded predictions very close to the ground truth and the error was similarly distributed around zero. The neural network outperforms both online raters and clinicians. The scoring system can reliably identify and accurately score individual figure elements in previously unseen ROCF drawings, which facilitates explainability of the AI-scoring system. To ensure generalizability and clinical utility, the model performance was successfully replicated in a large independent prospective validation study that was pre-registered prior to data collection. 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.

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 Dates: 2024-11
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.7554/eLife.96017.3.sa0
 Degree: -

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Title: eLife
Source Genre: Journal
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Publ. Info: Cambridge : eLife Sciences Publications
Pages: 17 Volume / Issue: 13 Sequence Number: RP9601 Start / End Page: - Identifier: Other: URL
ISSN: 2050-084X
CoNE: https://pure.mpg.de/cone/journals/resource/2050-084X