데이터-분석가들은-ai-지원에-어떻게-반응하는가-wizard-of-oz-연구

transformer의-긴-문맥-개선을-위한-상대-위치에-대한-기능적-보간

비매끄럽고-약하게-볼록한-유한합-결합-구성-최적화

자율-주행을-위한-카메라-lidar-구성이-3d-객체-감지에-미치는-영향

wie-reagieren-datenanalysten-auf-ki-unterstützung-eine-wizard-of-oz-studie

funktionale-interpolation-für-relative-positionen-verbessert-langkontext-transformer

analyse-von-nicht-glattem-schwach-konvexem-endsummen-gekoppeltem-kompositionsoptimierung

einfluss-der-kamera-lidar-konfiguration-auf-die-3d-objekterkennung-für-autonomes-fahren


An AI-powered scoring system that outperforms human raters in objectively and efficiently assessing memory deficits using the Rey-Osterrieth Complex Figure Test.


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Background 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.

Methods 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 multi-head convolutional neural network.

Results 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.

Conclusions 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.

### Competing Interest Statement

The authors have declared no competing interest.

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

automated-scoring-of-memory-deficits-using-deep-learning-improving-efficiency-and-objectivity-in-the-rey-osterrieth-complex-figure-test


Automated Scoring of Memory Deficits Using Deep Learning: Improving Efficiency and Objectivity in the Rey-Osterrieth Complex Figure Test


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Automated Scoring of Neuropsychological Tests