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在标准化植入前脏活检评估方面的挑战以及人工智能驱动解决方案的潜力

Karolien Wellekens^1,2, Priyanka Koshy^1,3, Maarten Naesens^1,2

¹Department of Microbiology, Immunology and Transplantation, KU Leuven.

Current opinion in nephrology and hypertension

|January 20, 2025

概括

脏活检处理中的变化会影响解释和器官分配. 数字病理学中的人工智能 (AI) 显示出对标准化的承诺,但需要进一步验证临床效用和长期结果预测.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
病理学病理学病理学
医疗成像医学成像
人工智能的人工智能

背景情况:

植入前脏活检处理方法表现出显著的变化.
采样,处理和病理学家专业知识的不一致性阻碍了组织学解释和可重现性.
越来越多的器官短缺需要根据活检结果改进器官分配的方法.

研究的目的:

审查植入前脏活检处理中的变异性.
评估这些变异对组织学解释和器官分配的影响.
评估人工智能 (AI) 和数字病理学的潜力,以应对这些挑战.

主要方法:

对植入前脏活检处理现有文献的综述.
分析方法变化对组织学评估的影响.
探索新兴的人工智能和数字病理学的标准化工具.

主要成果:

在活检采样 (芯与) 和加工 (冷与对嵌) 中发现了重大不一致.
病理学家专业知识的变化使得比较和可复制性变得复杂.
人工智能和数字病理学为标准化和改进可重现性提供了潜力,但临床实用性和长期结果预测在很大程度上仍未得到验证.

结论:

人工智能驱动的工具显示出对脏活检评估标准化和提高准确性的承诺.
临床应用因缺乏与移植后结果的验证关联而受到限制.
未来的研究需要大规模的纵向研究,严格验证短期和长期结果的预测性表现.

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