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Related Concept Videos

Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
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Using Explainable Artificial Intelligence in the Clock Drawing Test to Reveal the Cognitive Impairment Pattern.

Carmen Jiménez-Mesa1,2, Juan E Arco1,2,3, Meritxell Valentí-Soler4

  • 1Data Science and Computational Intelligence (DASCI) Institute, Spain.

International Journal of Neural Systems
|February 17, 2023
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Summary

This study introduces an AI-powered system for automatic diagnosis of cognitive impairment using the Clock Drawing Test (CDT). The computer-aided diagnosis (CAD) system achieves high accuracy, aiding early detection and understanding of dementia progression.

Keywords:
Alzheimer’s diseaseClock drawing testclinical diagnosiscognitive impairmentcomputer-aided diagnosisdeep learningexplainable AIimage processingmachine learning

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Dementia prevalence is rising globally, necessitating early and accurate diagnostic tools.
  • The Clock Drawing Test (CDT) is a common cognitive assessment, but its scoring is often subjective.
  • Objective and automated analysis of CDT could improve diagnostic efficiency and reliability.

Purpose of the Study:

  • To develop and evaluate a computer-aided diagnosis (CAD) system using artificial intelligence (AI) for automatic cognitive impairment (CI) detection from CDT drawings.
  • To enhance the objectivity and efficiency of CDT analysis in clinical settings.
  • To utilize explainable AI (XAI) to understand the patterns indicative of CI.

Main Methods:

  • A preprocessing pipeline was developed to detect, center, and binarize clock drawings.
  • A Convolutional Neural Network (CNN) was employed to analyze preprocessed CDT images for CI-related patterns.
  • The system was validated on a large, balanced dataset of CDT drawings from patients with CI and controls.

Main Results:

  • The proposed AI system achieved a classification accuracy of [Formula: see text] and an AUC of [Formula: see text] in distinguishing between CI and controls.
  • Explainable AI (XAI) methods identified key regions in CDT drawings crucial for classification.
  • The large sample size indicates high reliability for clinical application.

Conclusions:

  • The AI-based CAD system offers a reliable and objective method for diagnosing cognitive impairment using the CDT.
  • This approach can aid clinicians in early CI detection, potentially slowing disease progression.
  • The study highlights the potential of AI and XAI in advancing cognitive assessment tools.