LSTMニューラルネットワークを用いた放射性動脈パルス波による心不全分類モデル
Yi Lyu1,2, Wen-Yue Huang3, Hai-Mei Wu4
1School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China.
BMC medical informatics and decision making
|August 27, 2025
まとめ
ディープラーニングモデルは,非侵襲的な放射線動脈パルス波を使用して心不全 (HF) を検出できます. LSTMモデルは高い精度を示し,HFの早期スクリーニングに費用対効果の高いツールを提供しました.
科学分野:
- バイオメディカルエンジニアリング
- 医療における人工知能
- 心血管診断
背景:
- 心不全 (HF) は世界的に大きな健康問題であり,早期発見方法が必要である.
- ディープラーニング (DL) は,HF検出のための有望な非侵襲的,迅速で費用対効果の高いソリューションを提供します.
研究 の 目的:
- 放射性動脈パルス波データを用いて心不全 (HF) の分類におけるディープラーニングアルゴリズムの有効性を評価する.
- 健康な個人,冠動脈疾患 (CAD) の患者,およびHFの患者との違いを区別するための最も効果的なDLモデルを特定する.
主な方法:
- 462人の被験者 (健康者,CAD,HF) の放射線動脈波データを利用した.
- 4つのDLモデル (LSTM,CNN,GRU,Bi-LSTM) をデータ前処理とバランス処理後に適用して比較した.
- 堅実な性能と安定性の評価のために10倍クロス・バリデーションを使用しています.
主要な成果:
- 長期短期記憶 (LSTM) モデルは,最も高い平均精度 (0.8595 ± 0.0522) を達成した.
- エリア・アンダー・ザ・カーブを含む主要な指標でLSTMは優れたパフォーマンスを示しました.
- SHAP フレームワークは,モデルの解釈性と特徴の重要性分析に使用されました.
結論:
- 放射線動脈パルス波を分析するLSTMモデルは,健康状態,CAD,HF状態を効果的に区別します.
- この非侵襲的で費用対効果の高いアプローチは 心不全の早期スクリーニングの可能性を示しています
- このツールの実用性を確認するために,さらなる臨床検証が推奨されます.
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