人工智能在肝脏纤维化、变性和炎症损伤形态学诊断中的方法论应用

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详细

非肿瘤性肝脏疾病广泛存在，且仍然是诊断中的难题。根据现代数据，在俄罗斯，成人非酒精性脂肪肝的流行率约为25%。肝脏纤维化、脂肪和气球性变性、炎症浸润以及肝组织坏死的形态学验证依赖于专家的主观看法，这使得标准化变得困难。因此，开发客观且自动化的形态学变化分析方法可以显著提高诊断的可重复性。本综述分析了现代人工智能方法在非肿瘤性肝脏病变形态学诊断中的应用，重点讨论了神经网络算法的主要应用方向，包括组织学图像的分类和分割。此外，还评估了开发的模型在识别主要形态学特征，如纤维化、气球变性、脂肪变性和炎症浸润方面的有效性。

本综述使用了来自Google学术和PubMed数据库的文献。搜索时间范围为2020至2025年，最终纳入了22篇文献。

研究表明，人工智能模型在肝脏病变的诊断中表现出较高的准确性，但其效果仍依赖于样本量、实验室间差异、形态学模式、显微镜放大倍数以及切片染色方法。为进一步推动这一方向的研究，未来需要增加公开数据量并标准化方法。尽管如此，模型也能在较小的数据集上开发，这使得该技术对广泛的研究人员群体更具可访问性。

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作者简介

Tatiana O. Novikova

LLC Laboratoires de Genie

编辑信件的主要联系方式.
Email: tn.path1910@yandex.ru
ORCID iD: 0000-0002-1686-5629
SPIN 代码: 9993-9645
俄罗斯联邦, Moscow

Ashot A. Melikbekyan

MIREA-Russian Technological University

Email: melikbekyan.ashot@yandex.ru
ORCID iD: 0009-0003-6470-4891
SPIN 代码: 8683-6870
俄罗斯联邦, Moscow

Artyom M. Borbat

MVZ Pathologie Spandau

Email: aborbat@yandex.ru
ORCID iD: 0000-0002-9699-8375
SPIN 代码: 8948-9169
德国, Berlin

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