静脈潰瘍の評価と傷のケアのための機械学習アプリケーション:レビュー
Miloš Madić1, Nikola Vitković1, Zoran Damnjanović2,3
1Department of Production and Information Technologies, Faculty of Mechanical Engineering, University of Niš, 18000 Niš, Serbia.
Diagnostics (Basel, Switzerland)
|February 13, 2026
まとめ
機械学習 (ML) モデルは,静脈潰瘍の傷のケアを進めている. このレビューでは,2001年から2025年までの79件の研究を分析し,慢性創傷管理におけるMLの応用,利点,および将来の研究方向性を要約しました.
科学分野:
- 医療情報工学 医療情報工学
- 医療における人工知能
- 創傷ケア研究 創傷ケア研究
背景:
- 静脈性潰瘍の傷の治療は,機械学習 (ML) の統合で著しい進歩を遂げています.
- 既存の研究の包括的な分析は,MLの影響を理解するために不可欠です.
研究 の 目的:
- 2001年から2025年8月までの間,静脈潰瘍の創傷ケアにおけるMLの応用に関する研究を体系的に分析する.
- この分野における傾向,利益,限界,および将来の研究機会を特定する.
主な方法:
- Web of Science,Scopus,PubMedで体系的な文献検索を行いました.
- 含有/除外基準は,分析のための79の関連研究を導き出すために適用されました.
- メタアナリシスと静脈潰瘍のケアにおけるMLモデルアプリケーションの詳細な検討.
主要な成果:
- 79件の研究の分析により,静脈潰瘍の創傷管理におけるMLモデルの多様な応用が明らかになりました.
- MLモデルの限界と並行して,ヘルスケアシステムと患者にとっての利益を特定しました.
- 議論には,現在の傾向,機会,および既存の研究の限界が含まれています.
結論:
- MLモデルは,静脈性潰瘍の傷のケアを改善する大きな可能性を秘めています.
- 限界に対処し,慢性創傷管理におけるより広範な応用を探るため,さらなる研究が必要である.
- このレビューは,MLと傷のケアの研究者にとって貴重な洞察を提供します.
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