A deep learning approach for automated scoring of the Rey–Osterrieth complex figure Article Swipe
Nicolas Langer
,
Maurice Weber
,
Bruno Hebling Vieira
,
Dawid Strzelczyk
,
Lukas Wolf
,
Andreas Pedroni
,
Jonathan Heitz
,
Stephan A. Müller
,
Christoph Schultheiß
,
Marius Troendle
,
Juan Carlos Arango‐Lasprilla
,
Diego Rivera
,
Federica Scarpina
,
Qianhua Zhao
,
Rico Leuthold
,
Flavia M. Wehrle
,
Oskar G. Jenni
,
Peter Brügger
,
Tino Zaehle
,
Romy Lorenz
,
Ce Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7554/elife.96017.3
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7554/elife.96017.3
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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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7554/elife.96017.3
- OA Status
- gold
- Cited By
- 2
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404817049
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404817049Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7554/elife.96017.3Digital Object Identifier
- Title
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A deep learning approach for automated scoring of the Rey–Osterrieth complex figureWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-28Full publication date if available
- Authors
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Nicolas Langer, Maurice Weber, Bruno Hebling Vieira, Dawid Strzelczyk, Lukas Wolf, Andreas Pedroni, Jonathan Heitz, Stephan A. Müller, Christoph Schultheiß, Marius Troendle, Juan Carlos Arango‐Lasprilla, Diego Rivera, Federica Scarpina, Qianhua Zhao, Rico Leuthold, Flavia M. Wehrle, Oskar G. Jenni, Peter Brügger, Tino Zaehle, Romy Lorenz, Ce ZhangList of authors in order
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https://doi.org/10.7554/elife.96017.3Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.7554/elife.96017.3Direct OA link when available
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Generalizability theory, Artificial intelligence, Convolutional neural network, Computer science, Deep learning, Machine learning, Psychology, Cognitive psychology, Developmental psychologyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| primary_topic.display_name | Dementia and Cognitive Impairment Research |
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| publication_date | 2024-11-28 |
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