ディープラーニングアルゴリズムに基づく男性神経性下尿路機能障害の補助診断に関する研究
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PubMedで要約を見る
まとめ
この要約は機械生成です。ディープラーニングモデルは,男性の神経性下尿路機能障害 (NLUTD) の診断に役立ちます. Yolov10xとyolov5xモデルは,膀出口阻害とデトルーザー収縮性を評価する際に高い精度を示しています.
科学分野
- 泌尿器科
- 人工知能
- 医療診断
背景
- 神経性下尿路機能障害 (NLUTD) の診断は困難であり,経験のない医師によって誤診されやすい.
- 正確な診断は 効果的な患者管理と治療に不可欠です
- 現在の診断方法では 複雑な症例に必要な精度が欠けています
研究 の 目的
- 男性のNLUTDの自動診断のためのディープラーニングアルゴリズムモデルの開発と検証.
- 診断の精度を高めるために ビデオウロダイナミクスをAIと統合する
- NLUTDの診断と治療のための自動化されたプロセスを確立する.
主な方法
- 男性NLUTD患者384人 (2009~2021年) の尿動力学データを利用した.
- プレッシャー・フロー・トレースとシストウレトログラフィー画像で訓練され,検証されたディープ・ラーニング・モデル (yolov5,yolov10)
- 男性のNLUTDの定性および局所評価モデルを構築した.
主要な成果
- yolov10xモデルの平均精度 (mAP) は,膀出口阻害とデトルーザー収縮性の質的評価で0. 89でした.
- yolov5xモデルは,膀出口阻害の局所評価で平均mAP0. 83を達成しました.
- 両方のモデルはそれぞれの診断作業において高いパフォーマンスを示した.
結論
- yolov10xモデルは,雄性NLUTDにおける膀出口阻害とデトルーザー収縮性の質的評価に有効である.
- yolov5xモデルは,男性NLUTD患者の膀出口阻害の局所評価に適しています.
- 人工知能を駆使したビデオウロダイナミクスは 精確で自動化されたNLUTD診断のための有望なアプローチを提供します.
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