マリオ内視鏡スコアによる潰瘍性大腸炎の評価,マルチモダル大言語モデルを用いて:ヒト盲検精度研究
Antonietta Gerarda Gravina1, Raffaele Pellegrino1, Giuseppe Imperio1
1Division of Hepatogastroenterology, Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy.
Intestinal research
|February 12, 2026
まとめ
ChatGPT-4oのような大型言語モデルは,内視鏡画像からメイオ内視鏡スコア (MES) を割り当てるための軽度の可能性を示しています. 臨床応用における精度を高めるため,さらなる開発が必要である.
科学分野:
- 医療における人工知能
- 医学画像分析 医学画像分析
- 自然言語処理 (Natural Language Processing) とは,自然言語処理で処理される言語のことです.
背景:
- 大型言語モデル (LLM) の進歩により,画像処理機能が実現されています.
- エンドスコピーの臨床スコアリングにおけるLLMの有用性を調査する.
研究 の 目的:
- ChatGPT-4oのメヨ内視鏡スコア (MES) を正確に割り当てる能力を評価する.
- 専門家エンドスコピストの評価に対してLLMベースの画像分析のパフォーマンスを評価する.
主な方法:
- 4つの入力モデルを比較するために,内視鏡フレームの選択セットが使用されました.
- 304フレームの拡張セットで,最高性能のモデルが確認されました.
- コンコンダンスは,コーエンのカッパとスピアーマンのロを用いて評価された.
主要な成果:
- 1つのモデルは,専門家のスコアと有意な一致 (κ = 0.260) を示した.
- 拡張セットでは,絶対的一致度は45.3%に達した.
- 中等から重度の疾患に対する感受性は73%,90%のネガティブな予測値でした.
結論:
- ChatGPT-4oは,内視鏡のフレームでMES評価のための軽度の可能性を露呈しています.
- 臨床的有用性と精度の向上のために,モデルをさらに改良することが不可欠です.
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