pHインピーダンスモニタリングトレースに適用された機械学習アルゴリズムは病的な上腹部げっぷを特定する

Benjamin D Rogers^1,2, Michael Holloway¹, Akinara Sawada³

¹Division of Gastroenterology, Hepatology, and Nutrition, University of Louisville School of Medicine, Louisville, Kentucky, USA.

Neurogastroenterology and motility

|January 19, 2026

まとめ

機械学習は上腹部げっぷ（SGB）を正確に特定し、手動レビューを超えた検出を改善します。このアルゴリズムは、SGB患者とGERDおよび健康な個人を効果的に区別します。

科学分野:

消化器病学
人工知能
医療診断

背景:

上腹部げっぷ（SGB）は、現在のソフトウェアおよび手動レビューでは検出が困難です。
SGBの正確な特定は、患者の診断と管理に不可欠です。

主な方法:

GERD患者、過剰SGB患者、健常ボランティアからのpHインピーダンスデータを使用して、畳み込みニューラルネットワーク（CNN）を開発しました。
CNNは、手動レビューされたSGBイベントでトレーニング、検証、テストされ、アルゴリズムによって特定された新しいイベントが含まれていました。

結論:

新規MLアルゴリズムは、SGBイベントを正確に特定できます。
MLアプローチは、SGB患者と健常者およびGERD患者を確実に区別します。
この技術は、SGBの診断と管理を改善する可能性があります。

キーワード:

機械学習 pHインピーダンス上腹部げっぷ

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