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声帯振動を音声で分析し、誤嚥リスクを予測する機械学習モデルの開発と検証

Cyril Varghese1, Jianwei Zhang2, Sara Charney3

  • 1Division of Pulmonary and Department of Critical Care Medicine, Mayo Clinic in Arizona, 5777 East Mayo Blvd, Phoenix, US.

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まとめ

機械学習は母音発声の分析によって誤嚥リスクを正確に予測する。この新しいアルゴリズムは、専門家と同等の嚥下安全性を評価するための非侵襲的なツールを提供する。

キーワード:
機械学習誤嚥音声分析非侵襲的診断耳鼻咽喉科

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科学分野:

  • 耳鼻咽喉科学
  • 音声科学
  • 人工知能

背景:

  • 誤嚥は呼吸器疾患のリスクとなるが、現在の診断方法は侵襲的であるか信頼性が低い。
  • 主観的なベッドサイド評価は一貫性に欠け、VFSSやFEESのような検査はリソース集約的である。

研究 の 目的:

  • 誤嚥リスクを予測するための機械学習（ML）アルゴリズムを開発および検証すること。
  • アルゴリズムは単純な母音発声の音響的特徴を分析する。

主な方法:

  • VFSSをグラウンドトゥルースとして使用し、ハイリスク対ローリスクの誤嚥者を区別するように訓練された教師ありMLモデル。
  • 患者163人からの[i]母音発声のレトロスペクティブ分析、音響的特徴の記録。
  • モデルは外部コホートで検証され、言語聴覚士（SLP）と比較された。

主要な成果:

  • MLモデルは、ハイリスク（0.530）およびローリスク（0.243）の誤嚥群間でリスクスコアに有意差を示した（p<0.001）。
  • 開発コホートで0.76、外部コホートで0.70の曲線下面積（AUC）を達成した。
  • MLモデルのパフォーマンスは、誤嚥リスクの分類において訓練されたSLPと同等であった。

結論:

  • 耳鼻咽喉科（ENT）患者における定量化可能な音声特性は、誤嚥リスクと相関している。
  • 持続的な発声を分析するMLモデルは、ハイリスクおよびローリスクの誤嚥者間の違いを効果的に検出できる。
  • このアプローチは、誤嚥リスク評価のための有望な非侵襲的方法を提供する。