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A new AI system uses iris scans and eye tracking to objectively detect depression in pilots and drivers. This technology offers a real-time safety solution to prevent accidents caused by impaired judgment.

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Area of Science:

  • Ophthalmology
  • Psychiatry
  • Artificial Intelligence

Background:

  • Depression poses significant safety risks in critical professions like aviation and driving.
  • Current depression detection relies on subjective self-reporting, lacking speed and objectivity.
  • Objective biomarkers are needed for early detection to prevent accidents.

Purpose of the Study:

  • To develop and evaluate an AI-based system for real-time, objective depression detection.
  • To integrate iris identification with pupillometric and eye movement analysis.
  • To provide a preventive safety measure for transportation sectors.

Main Methods:

  • An AI system combining iris identification and a CNN-LSTM network for depression detection.
  • Analysis of pupillometric (pupil dilation) and eye movement (saccadic velocity, fixation time) biomarkers.
  • Evaluation using data from 242 participants, comparing responses to neutral, positive, and negative stimuli.

Main Results:

  • Iris identification module achieved an Equal Error Rate < 0.5%.
  • Depression detection model (CNN-LSTM) showed 89% accuracy and 0.94 AUC.
  • Depressed individuals exhibited distinct pupillometric and eye movement patterns compared to controls.

Conclusions:

  • AI-driven analysis of iris and eye movement biomarkers can reliably screen for depression.
  • The system offers a potential objective, real-time solution for enhancing safety in transportation.
  • This technology could significantly reduce accidents attributed to depression-related human error.