移動式心電図装置を使用して,人工知能で心拍数修正QT間隔の評価
John R Giudicessi1, Matthew Schram2, J Martijn Bos3
1Clinician-Investigator Training Program (J.R.G.), Mayo Clinic, Rochester, MN.
Circulation
|February 1, 2021
まとめ
人工知能 (AI) によるディープニューラルネットワーク (DNN) は,心拍数修正QT間隔 (QTc) をモバイルEKGデバイスから正確に予測します. このAI対応の 移動式ECGスクリーニングは QT症候群の検出に 費用対効果の高い方法を提供します
科学分野:
- 心臓病科
- 人工知能
- 医療機器
背景:
- 心拍数調整によるQTインターバル (QTc) の延長により,心室不律や突然の心臓死のリスクが増加します.
- 原因には薬物,遺伝 (先天性QT長症候群),COVID-19のような病気があります.
- 現在のQTcモニタリングは12リードの心電図に依存しています.
研究 の 目的:
- 12リードのECGを用いたQTc決定のためのAI対応アルゴリズムをトレーニングし,検証する.
- モバイルECG (mECG) トレーシングでAIアルゴリズムを将来的にテストする.
- 臨床的に重要なQTc延長を検出するAIの能力を評価する.
主な方法:
- ディープニューラルネットワーク (DNN) が訓練され,160万以上の12リードECGで検証されました.
- DNNはQTc値を予測し,心臓科医のオーバーリーディング値をゴールドスタンダードとして使用しました.
- 前向きな試験は,遺伝性心疾患の686人の患者を対象に,12鉛の心電図とプロトタイプのmECG装置の両方を使用しました.
主要な成果:
- AI DNNは,検証セットのヒトによるQTc値と強い一致を示した.
- mECGによるDNN予測のQTcは,12リードのECGと比較して名目的な差異を示した.
- AIは,mECGデータでQTc延長 (AUC 0. 97,感度 80. 0%,特異性 94. 4%) を検出する高精度を示した.
結論:
- AI DNNは,スマートフォン対応の電極を使用して,標準の12リードECGからQTcを正確に予測できます.
- AI対応のmECGデバイスは 既得QT症候群と先天性QT症候群の 費用対効果の高いスクリーニングツールです
- この技術は,アクセスしやすいまたは費用対効果の高い12リードECGがない環境で価値があります.
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