咳の音を用いた小児呼吸器疾患診断における人工知能アルゴリズムの予測能力:体系的なレビュー

Aya Abuelgasim Ibrahim Abdelhalim¹, Hanady Me M Osman², Sally Ibrahim Hafez Sadaka³

¹Emergency Medicine, Najran Armed Forces Hospital, Ministry of Defense Health Services, Najran, SAU.

Cureus

|August 25, 2025

まとめ

咳の音の人工知能 (AI) 分析は小児呼吸器疾患の診断に有望である. この非侵襲的な方法は高精度で資源が限られた環境では特に価値があります

科学分野:

小児肺科
医療情報学
医療における人工知能

背景:

呼吸器疾患は世界的に子供の死亡の主な原因です
小児の呼吸器疾患の診断は特に資源が少ない地域では困難です
人工知能 (AI) は,咳の音の分析を用いた新しい非侵襲的なアプローチを提供します.

研究の目的:

咳の音に基づく小児呼吸器疾患のAIアルゴリズムの診断性能を体系的に検討する.
AI駆動の咳分析の臨床的適用性と方法論的品質を評価する.
人工知能が資源の限られた環境で診断ツールとして使える可能性に関する証拠をまとめます

主な方法:

PRISMA 2020のガイドラインに沿った体系的な文献レビュー.
主要なデータベース (PubMed,Scopus,Web of Science,IEEE Xplore) から6つの研究が含まれている.
ロジスティック回帰,CNN,SVM,ハイブリッドアプローチを含むAIモデルの分析; QUADAS-2を用いた評価.

主要な成果:

AIモデルは,82%~94%の感度と71%~91%の特異性で高い診断能力を示した.
一部の人工知能のアプローチは WHOの伝統的な臨床アルゴリズムを上回った.
方法論的な限界には,遡及的な設計,小さなサンプルサイズ,そしてほとんどの研究における外部検証の欠如が含まれます.

結論:

AIベースの咳の音分析は小児呼吸器疾患の診断に有望でスケーラブルなツールです
この技術は,資源が限られた環境での診断を改善するために特に価値があります.
将来の検証と現実世界の適用のための臨床ワークフローの統合が必要です.

キーワード:

人工知能咳の音分析診断の正確さ機械学習小児呼吸器疾患体系的なレビュー

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