透析における人工知能と機械学習の応用:現在の応用,課題,そして将来の方向性のレビュー
Aanuoluwapo Clement David-Olawade1, Muyiwa Ademola Ogunbona2, Olabanke Florence Olawuyi3
1Endoscopy Unit, Glenfield Hospital, University Hospitals of Leicester, NHS Trust, Leicester, United Kingdom.
Clinica chimica acta; international journal of clinical chemistry
|February 17, 2026
まとめ
人工知能 (AI) と機械学習 (ML) は腎臓学と透析ケアに革命をもたらしています. これらの技術は,改善された予測,診断,治療のサポートを提供していますが,データプライバシーや解釈可能性などの採用課題に直面しています.
科学分野:
- ネフロロジーは腎臓科
- 医療情報工学 医療情報工学
- 人工知能 (AI) とは,人工知能 (AI) のことです.
背景:
- 人工知能 (AI) と機械学習 (ML) は,腎臓学,特に透析においてますます重要になっています.
- マルチモダルデータ (EHR,機械データ,ラボ,画像) は,予後,診断,治療のための高度なツールを容易にします.
- 透析におけるAI/MLの応用は急速に進化しており,現在の能力と将来の方向性の見直しが必要となっている.
研究 の 目的:
- 透析ケアにおける現在のAI/MLアプリケーションを包括的に検討する.
- これらのAI/MLツールの臨床パフォーマンスを評価する.
- 透析のためのAI/MLにおける将来の研究方法と課題を特定する.
主な方法:
- 2020年から2025年にかけてのピアレビューされた研究のナラティブレビューが行われました.
- 文献検索は,PubMed,Web of Science,およびその他の主要なデータベースで行われました.
- 研究は,応用領域 (予測,CKDの進行,意思決定支援,血管アクセス,NLP) により分類され,方法論とパフォーマンスを分析しました.
主要な成果:
- AI/MLのアプリケーションは,予測 (内分析性低血圧,死亡率,入院),CKDの進行,治療の最適化 (貧血管理),血管へのアクセスモニタリング (画像分析),症状検出 (NLP) をカバーしています.
- 高性能メトリック,例えばIDH予測 (0.89-0.95) と血管アクセスモニタリング (≈0.96) のAUROC,死亡率予測のC指数は0.83まで報告されました.
- 連邦学習 (FL) は,プライバシーを保護するマルチセンターのコラボレーションのために出現しています.
結論:
- AI/MLは,精度向上とパーソナライズされた治療を通じて,透析ケアを強化する大きな希望を示しています.
- 広範な臨床採用は,データのプライバシー,モデルの解釈可能性,規制上の障害,およびデータの代表性に関する問題により妨げられています.
- これらの課題に対処し,AI/MLを日常透析の実践に統合することを促進するために,さらなる研究が必要である.
キーワード:
人工知能 (AI) は,人工知能 (AI) を利用する.臨床的意思決定支援についてイントラダイアリティック低血圧機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,ダイエリシス・ダイアリス
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