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スペクトログラムベースの肺音分類におけるマルチモーダル言語モデルを用いた少数ショットプロンプトの評価

Nicholas Dietrich1, David McShannon2, Mark F Rzepka3

  • 1Temerty Faculty of Medicine, University of Toronto, 1 King's College Cir, Toronto, Ontario, Canada.

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まとめ
この要約は機械生成です。

大規模言語モデル（LLM）は肺音分類に有望である。GPT-4oを用いた少数ショットプロンプトはゼロショットと比較して精度を向上させたが、臨床応用にはさらなる開発が必要である。

キーワード:
大規模言語モデルGPT-4o肺音分類少数ショットプロンプト呼吸音スペクトログラムマルチモーダル学習人工知能医療応用

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科学分野:

  • 人工知能
  • 生体信号処理
  • 呼吸器医学

背景:

  • 従来の深層学習による肺音分析には、広範なラベル付きデータが必要です。
  • マルチモーダル大規模言語モデル（LLM）は、潜在的なプロンプトベースの代替手段を提供します。
  • 呼吸音分類のための汎用LLMの評価は重要です。

研究 の 目的:

  • メルスペクトログラムを用いた肺音分類におけるGPT-4oの有用性を評価する。
  • このタスクにおける少数ショットプロンプトとゼロショットプロンプトを比較する。
  • 呼吸音響学におけるプロンプトベースのマルチモーダル推論のベースラインを確立する。

主な方法:

  • ICBHI 2017データベースからの6898のアノテーション付き呼吸サイクルをメルスペクトログラムに変換しました。
  • 分類のために、ゼロショットおよび少数ショットのプロンプト戦略の両方でGPT-4oを使用しました。
  • 精度、適合率、再現率、F1スコア、マクネマー検定を使用してパフォーマンスを評価しました。

主要な成果:

  • 少数ショットプロンプトは、精度（0.363対0.320）およびその他の指標において統計的に有意な改善をもたらしました。
  • モデルの再現性分析では、高い一致度（κ = 0.76-0.88）が示され、優れた一貫性が示されました。
  • パフォーマンスの向上は限定的でしたが、統計的に有意でした（p < 0.001）。

結論:

  • GPT-4oは、プロンプトベースの肺音分類に可能性を示しており、少数ショットプロンプトはゼロショットを上回りました。
  • 現在のパフォーマンスは臨床展開には不十分ですが、将来の研究の基盤を提供します。
  • プロンプトベースのマルチモーダル推論は、呼吸器医学におけるスペクトログラム分析に柔軟なアプローチを提供します。