Publication: A deep learning approach for automated scoring of the Rey–Osterrieth complex figure
A deep learning approach for automated scoring of the Rey–Osterrieth complex figure
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Langer, N., Weber, M., Hebling Vieira, B., Strzelczyk, D., Wolf, L., Pedroni, A., Heitz, J., Müller, S., Schultheiss, C., Troendle, M., Lasprilla, J. C. A., Rivera, D., Scarpina, F., Zhao, Q., Leuthold, R., Wehrle, F., Jenni, O., Brugger, P., Zaehle, T., … Zhang, C. (2024). A deep learning approach for automated scoring of the Rey–Osterrieth complex figure. ELife, 13, RP96017. https://doi.org/10.7554/elife.96017.3
Abstract
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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 l
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Creators (Authors)
- Langer, Nicolas
- Weber, Maurice
- Hebling Vieira, Bruno
- Strzelczyk, Dawid
- Wolf, Lukas
- Pedroni, Andreas
- Heitz, Jonathan
- Müller, Stephan
- Schultheiss, Christoph
- Troendle, Marius
- Lasprilla, Juan Carlos Arango
- Rivera, Diego
- Scarpina, Federica
- Zhao, Qianhua
- Brugger, Peter
- Zaehle, Tino
- Lorenz, Romy
- Zhang, Ce
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Langer, N., Weber, M., Hebling Vieira, B., Strzelczyk, D., Wolf, L., Pedroni, A., Heitz, J., Müller, S., Schultheiss, C., Troendle, M., Lasprilla, J. C. A., Rivera, D., Scarpina, F., Zhao, Q., Leuthold, R., Wehrle, F., Jenni, O., Brugger, P., Zaehle, T., … Zhang, C. (2024). A deep learning approach for automated scoring of the Rey–Osterrieth complex figure. ELife, 13, RP96017. https://doi.org/10.7554/elife.96017.3
