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在慢性脏疾病中基于深度学习的管间隙损伤的组织病理学评估

Nonoka Suzuki¹, Kaname Kojima², Silvia Malvica³

¹Division of Nephrology, Endocrinology and Vascular Medicine, Graduate School of Medicine, Tohoku University, Sendai, Japan.

Communications medicine

|January 5, 2025

概括

一种新的深度学习方法DLRS准确地从活检图像中量化脏管间隙损伤. DLRS得分与估计的膜过率 (eGFR) 相相关,并预测慢性病 (CKD) 的疾病进展.

科学领域:

腎臟病學 (nephrology) 是一種醫學.
数字病理学数字病理学
人工智能的人工智能

背景情况:

慢性病 (CKD) 涉及不可逆转的损伤.
脏活检对于CKD诊断和预后至关重要.
从整个幻灯片图像 (WSIs) 中量化管间伤害在视觉上具有挑战性.

研究的目的:

开发一种深度学习方法 (DLRS) 来量化管间隙损伤评分从脏活检WSIs.
评估DLRS得分与科医生评估的一致性.
评估DLRS得分与估计的膜过率 (eGFR) 和预测CKD进展的相关性.

主要方法:

开发了DLRS,一种深度学习模型,用于细分WSIs并检测间歇性核.
量化间歇性纤维化和炎症细胞透作为管间歇性损伤得分.
将DLRS应用于来自日本和欧洲祖先的CKD患者的WSI.

主要成果:

来自DLRS的分数与科医生的评估结果一致.
在活检中,DLRS纤维化得分与eGFR的相关性比手动得分更好.
DLRS得分与EGFR有显著的相关性,并预测CKD患者的EGFR每年下降.
纳入DLRS得分可以改善每年eGFR下降的预测.

结论:

来自DLRS的管间伤害得分是可靠的,可以与专家评估相比较.
DLRS得分与eGFR相关,并预测不同人群的病预后.
DLRS显示出作为预测CKD进展和指导治疗的工具的潜力.

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