通过多类人工智能模型对组织学的慢性变化
Aleksandar Denic1, Muhammad S Asghar1, Lucas Stetzik2
1Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA.
Kidney international reports
|August 15, 2025
概括
一个人工智能 (AI) 模型从组织学幻灯片准确量化慢性病变化. 这种人工智能工具有助于预测功能衰竭和评估组织的慢性变化.
科学领域:
- 数字病理学数字病理学
- 医学中的人工智能
- 腎臟病學 (nephrology) 是一種醫學.
背景情况:
- 评估组织学中的慢性变化对于预测病结果至关重要.
- 目前依赖人类解释的方法是主观的,准确性有限.
- 组织学特征的自动量化可以提高诊断一致性和预后价值.
研究的目的:
- 开发和验证一种人工智能 (AI) 模型,用于细分和量化脏组织学全片图像 (WSI) 中的慢性变化.
- 评估AI衍生的慢性病 (CKD) 措施与脏捐赠者和脏瘤患者的临床结果之间的关联.
主要方法:
- 一个人工智能模型被训练使用20509注释在20个类的脏组织WSIs.
- 人工智能模型的性能与人类注释进行了验证,比较了协议水平.
- 人工智能模型量化了1426名捐赠者和1699名瘤患者的慢性变化,包括脏大小和硬化标志物.
主要成果:
- 人工智能模型在大多数检测中显示出与人类对相似的一致性,对动脉动脉质症 (AH) 的一致性较低.
- 人工智能衍生的慢性变化与捐赠后减少的膜过率 (GFR) 和脏切除术后的功能衰竭相关.
- 从人工智能检测得出的长期性得分显示,功能衰竭的预后能力很强 (C-统计 = 0.819).
结论:
- 一个多类人工智能模型有效地自动化了从WSIs的慢性病变化的量化.
- 基于AI的组织学评估为预测CKD进展和结果提供了可靠的工具.
- 这项技术有可能提高病学诊断准确度和患者管理.
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