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Automating Rey Complex Figure Test scoring using a deep learning-based approach: a potential large-scale screening

Jun Young Park1,2,3, Eun Hyun Seo1,4, Hyung-Jun Yoon5

  • 1Gwangju Alzheimer's & Related Dementia Cohort Research Center, Chosun University, Gwangju, 61452, South Korea.

Alzheimer'S Research & Therapy
|August 30, 2023
PubMed
Summary

An artificial intelligence (AI) scoring system for the Rey Complex Figure Test (RCFT) was developed using deep learning. This AI system demonstrates high accuracy comparable to experienced psychologists, offering a faster, cost-effective screening tool for neurocognitive disorders.

Keywords:
Alzheimer’s diseaseArtificial intelligenceConvolutional neural networkDeep learningRey Complex Figure TestScoring

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging Analysis

Background:

  • The Rey Complex Figure Test (RCFT) is crucial for evaluating neurocognitive functions across diverse age groups and clinical populations.
  • Current RCFT scoring is complex, potentially leading to inter-rater variability.
  • Existing digital RCFT formats lack direct automatic scoring comparable to expert psychologists.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) scoring system for the Rey Complex Figure Test (RCFT).
  • To utilize a deep learning (DL) algorithm for automated RCFT scoring.
  • To ensure the AI scoring system's validity and comparability to experienced psychologists' assessments.

Main Methods:

  • Trained a deep learning model (DenseNet architecture) on 20,040 RCFT images from 6,680 subjects.
  • Improved model performance by re-examining and re-training on images with poor initial results.
  • Validated the AI scoring system using 150 independent test images scored by five expert psychologists.

Main Results:

  • The final AI model achieved a mean absolute error (MAE) of 0.95 points and an R-squared value of 0.986 in cross-validation.
  • AI-predicted scores significantly differentiated between normal cognition, mild cognitive impairment, and dementia.
  • External validation showed an MAE of 0.64 points and an R-squared of 0.994 between AI and expert human scores.

Conclusions:

  • The AI scoring system for RCFT shows no fundamental difference in accuracy compared to experienced psychologists.
  • The developed AI system can facilitate faster and more cost-effective screening for early-stage Alzheimer's disease pathology.
  • Potential applications include medical checkup centers and large-scale community-based research institutes.