甲状腺细胞病理学中的人工智能:诊断和技术见解
Mariachiara Negrelli1, Chiara Frascarelli1,2, Fausto Maffini1
1Division of Pathology, European Institute of Oncology IRCCS, 20139 Milan, Italy.
Cancers
|November 13, 2025
概括
深度学习 (DL) 在甲状腺细胞学中显示出对分类结节和帮助诊断的希望. 然而,数据变化和缺乏验证等挑战阻碍了这些人工智能工具的广泛采用.
科学领域:
- 细胞病理学 细胞病理学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 精细针吸收细胞学 (FNAC) 是使用贝塞斯达系统进行甲状腺结节评估的标准.
- 不确定的类别 (Bethesda III-IV) 带来了挑战,导致过度处理或延迟测试.
- 深度学习 (DL) 在甲状腺细胞病理学中提供了潜在的应用,包括分类和分类.
研究的目的:
- 为提供甲状腺细胞学中DL的技术概述.
- 突出临床部署的挑战和先决条件.
- 讨论DL在这个领域的未来临床应用.
主要方法:
- 在甲状腺细胞病理学中对DL应用的审查.
- 强调分析前的可变性,架构选择和临床整合.
- 讨论安全临床实施的要求.
主要成果:
- DL在分类,贝塞斯达分类和分子数据集成方面表现有前途.
- 预分析变异性,注释偏差和域位移限制概括性.
- 大多数当前的研究都是追溯的,缺乏外部验证.
结论:
- 标准化数据集,多中心试验和可解释性对于DL部署至关重要.
- DL很可能作为诊断副驾驶员和风险分层工具.
- 验证的DL可以增强细胞病理学家,减少变化,并推动数据驱动的纪律.
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