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Updated: Jun 28, 2026

Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models
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ディープラーニングによる横行髄膜梗塞後の難解症の重度の分類

Taeheon Lee1, Bo Hae Kim2, Kihwan Nam3

  • 1Department of Physical Medicine and Rehabilitation, Dongguk University Ilsan Hospital, College of Medicine, 27 Dongguk-ro, Ilsandong-gu, Goyang, 10326, Republic of Korea.

Scientific reports
|February 19, 2026
PubMed
まとめ

ディープラーニングモデルは,早期のMRIスキャンを使用して,横行髄膜梗塞 (LMI) 患者における失調症の重症度を分類することができます. このアプローチは,患者のアウトカムを改善するために,タイムリーな介入とパーソナライズされたリハビリテーションの計画に役立ちます.

キーワード:
分類 分類する.ディープラーニングとは,ディープラーニングです.ディスファギア ディスファギア横側髄膜梗塞 横側髄膜梗塞マグネティックレゾナンスイメージング (MRI)

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