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人工知能によるうつ病検出

Gabor Kiss1, Patrik Viktor2

  • 1Institute of Safety Science and Cybersecurity, Obuda University, 1034 Budapest, Hungary.

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まとめ
この要約は機械生成です。

新しいAIシステムは、虹彩スキャンとアイトラッキングを使用して、パイロットやドライバーのうつ病を客観的に検出します。この技術は、判断力の低下による事故を防ぐためのリアルタイムの安全ソリューションを提供します。

キーワード:
人工知能航空うつ病認識道路輸送安全

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科学分野:

  • 眼科学
  • 精神医学
  • 人工知能

背景:

  • うつ病は、航空や運転などの重要な職業において重大な安全リスクをもたらします。
  • 現在のうつ病検出は、速度と客観性を欠く主観的な自己報告に依存しています。
  • 事故を防ぐための早期検出には、客観的なバイオマーカーが必要です。

研究 の 目的:

  • リアルタイムで客観的なうつ病検出のためのAIベースシステムの開発と評価。
  • 虹彩認識と瞳孔測定および眼球運動分析の統合。
  • 輸送部門のための予防安全対策の提供。

主な方法:

  • 虹彩認識とうつ病検出のためのCNN-LSTMネットワークを組み合わせたAIシステム。
  • 瞳孔測定（瞳孔拡張）および眼球運動（サッカード速度、注視時間）バイオマーカーの分析。
  • 中立的、肯定的、および否定的な刺激に対する反応を比較して、242人の参加者からのデータを使用して評価しました。

主要な成果:

  • 虹彩認識モジュールは0.5%未満の等しいエラー率を達成しました。
  • うつ病検出モデル（CNN-LSTM）は89%の精度と0.94のAUCを示しました。
  • うつ病の個人は、対照群と比較して、異なる瞳孔測定および眼球運動パターンを示しました。

結論:

  • 虹彩および眼球運動バイオマーカーのAI駆動分析は、うつ病を確実にスクリーニングできます。
  • このシステムは、輸送における安全性を向上させるための潜在的な客観的かつリアルタイムのソリューションを提供します。
  • この技術は、うつ病に関連する人的ミスに起因する事故を大幅に削減する可能性があります。