[The Digital Lab: 実践的なデジタルワークフローと,前立腺がんの例を通して,病理学のルーチンにAIの統合]

Solène-Florence Kammerer-Jacquet¹, Pierre Allaume¹, Katell Guiheux¹

¹Service d'anatomie et de cytologie pathologiques, centre hospitalier universitaire de Rennes, 35000 Rennes, France.

Annales de pathologie

|February 13, 2026

まとめ

病理学部におけるデジタル病理学と人工知能 (AI) の統合は有望である. 効果的な実施には,高い分析前品質と,最適な診断精度のための組織統合が必要です.

科学分野:

デジタル病理学デジタル病理学
医療における人工知能
病理学情報工学パソロジー情報工学

背景:

デジタル病理学は技術革命をもたらし,日常の実践を近代化し,診断と研究のためのAI統合を可能にします.
レンヌ大学病院は2020年にデジタル病理学を実装し,2023年7月に前立腺腺がん検出のためのAIソリューションを実装しました.

研究の目的:

レンヌ大学病院のデジタル病理学とAIに関する経験をレビューする.
病理学科の専門職に対するデジタル化の影響を評価する.
ルーティン診断のためのAIの将来的な使用を評価する.

主な方法:

デジタル病理学の実装とAIソリューション (Galen® Prostate, Ibex) の使用のレビュー.
癌の検出とグリーソン格付けのためのAIと病理学者の間の一致率の分析.
免疫ヒストケミストリーの使用と分析前人工物関連の失敗に対するAIの影響の評価.

主要な成果:

AIコンコンダンス値は,高確率の癌検出では93.2%,低確率のスライドでは99%でした.
癌は,中間確率のスライドの4.7%で確認された (合計の43%).
グリーソン分級コンコンダンス76.6%;AIは免疫ヒストケミストリーの使用をまだ変更していないが,分析前の人工物により10%の失敗率がある.

結論:

デジタル病理学とAIの有効性は,分析前の品質に大きく依存しています.
統合の成功には,強力な組織戦略が必要です.
病理学におけるAIの最適なパフォーマンスを得るために,分析前のワークフローのさらなる改善が必要である.

キーワード:

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