フィリピンの症例発見の際に結核の推定識別のために,ディープラーニングベースの人工知能を駆使したコンピュータ支援検出による胸部X線の性能

Nichel Marquez¹, Erwin John Carpio², Mary Rosary Santiago³

¹TBIHSS, FHI 360, Makati City, Philippines. nichelmarquez@gmail.com.

BMC global and public health

|August 23, 2025

まとめ

人工知能による胸部X線スクリーニングは,フィリピンで結核の検出に高い感度を示しますが,最適な現実世界の適用のために注意深い値校正が必要です.

科学分野:

医療用イメージング
医療における人工知能
公衆衛生

背景:

フィリピンは結核の大きな負担に直面しており,効率的な診療所でのスクリーニングが必要である.
胸部X線 (CXR) のディープラーニングを用いたコンピュータ支援検出 (AI-CAD) は,組織的な結核症例発見の有望なアプローチを提供します.

研究の目的:

フィリピンの結核検診のためのAI-CADの実用的なパフォーマンスを評価する.
国内TB撲滅プログラムへのAI-CAD統合の精進を図る

主な方法:

フィリピンの4つの地域 (2021年5月〜2024年3月) で,5740人の個体 (≥15歳) のCXRおよび分子迅速診断 (mWRD) 検査結果を遡及的に分析した.
AI-CADの診断精度は,mWRDを基準基準として,固定された異常の値を用いて評価された.
不均衡なデータによる精度リコール (PRC) と受信機動作特性 (ROC) 曲線を使用し,部分的に検証されたケースでは"擬似感度"と"擬似特異性"を使用した.

主要な成果:

異常値0. 5でAI- CADは高い擬似感度 (95. 6%) を示したが,低擬似特異度 (28. 1%) と陽性予測値 (PPV) (18. 4%) を示した.
ROC曲線の下の面積は0.820であり,PRC曲線の下の面積は0.489であった.
値を0.68に上げると,テストの節約率 (42%) が改善されたが,失敗した症例は増加 (10%) した.

結論:

AI-CADはフィリピンで結核検診を拡大し,結核撲滅の取り組みを進める可能性を秘めています.
値の校正は極めて重要で,地元の資源の利用可能性,結核の蔓延率,プログラム目標に合わせて調整されます.
ROCとPRCのプロットは,リソースが限られた環境でモデルの性能と費用対効果を評価するのに価値があります.

キーワード:

胸のX線写真コンピュータによる検出精度リコール曲線受信器の動作曲線結核

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