ループス腎炎における腎臓慢性指数の深層学習に基づく定量評価

Tianqi Tu¹, Hui Wang², Jiangbo Pei³

¹Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China; Institute of Nephrology, Peking University, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China; Key Laboratory of CKD Prevention and Treatment, Ministry of Education of China, Beijing 100034, China.

Annals of diagnostic pathology

|August 28, 2025

まとめ

新しいディープラーニング (DL) パイプラインは,狼性腎炎 (LN) の腎臓慢性指数 (CI) を正確に評価し,病理学者の合意とよりよい患者の結果のための予後精度を改善します.

科学分野:

腎臓科
コンピューター病理学
人工知能

背景:

腎臓慢性指数 (CI) は,狼性腎炎 (LN) の長期的なアウトカムを予測する上で極めて重要です.
病理学者の手作業によるCI評価は時間のかかるもので,観察者間の変動が起こりやすく,疲労によって影響を受けます.

研究の目的:

LNにおけるCIの自動評価のためのディープラーニング (DL) パイプラインを開発し,検証する.
LN患者の管理における予後洞察と観察者間の合意を改善する.

主な方法:

DLパイプラインは2つのコホートから141人の腎臓生検スライドで訓練されました.
パイプラインは,セグメンテーションの性能と病理学者とCIの相関性について,内部および外部テストセットで評価されました.
診断の正確さは,DL-CIと臨床データと病理学者が評価したCIを組み合わせて評価された.

主要な成果:

DLパイプラインは,組織区間と組織病変の高度なセグメンテーションの精度を達成しました.
DLベースのCI評価は病理学者の評価と強く相関し,観察者間の合意を大幅に高めました.
DL-CIを臨床パラメータと組み合わせることで,結果予測の精度が向上しました.

結論:

DLパイプラインは,LNにおけるCIを評価するための正確で効率的な方法を提供します.
このツールは,CIの評価を標準化し,変異性を軽減し,LN患者の治療における予後能力を向上させる可能性を示しています.

キーワード:

人工知能ディープラーニングルプス腎炎腎臓病理

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