Background: From cell phones to aerospace, artificial intelligence (AI) has wide-reaching influence in the modern age. In this review, we discuss the application of AI solutions to an equally ubiquitous problem in cytopathology – thyroid fine needle aspiration biopsy (FNAB). Thyroid nodules are common in the general population, and FNAB is the sampling modality of choice. The resulting prevalence in the practicing pathologist’s daily workload makes thyroid FNAB an appealing target for the application of AI solutions. Summary: This review summarizes all available literature on the application of AI to thyroid cytopathology. We follow the evolution from morphometric analysis to convolutional neural networks. We explore the application of AI technology to different questions in thyroid cytopathology, including distinguishing papillary carcinoma from benign, distinguishing follicular adenoma from carcinoma and identifying non-invasive follicular thyroid neoplasm with papillary-like nuclear features by key words and phrases. Key Messages: The current literature shows promise towards the application of AI technology to thyroid fine needle aspiration biopsy. Much work is needed to define how this powerful technology will be of best use to the future of cytopathology practice.
Acta Cytologica

中文翻译：

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甲状腺细针穿刺活检中的人工智能

背景：从手机到航空航天，人工智能（AI）在现代有着广泛的影响。在这篇综述中，我们讨论了人工智能解决方案在细胞病理学中同样普遍存在的问题——甲状腺细针穿刺活检 (FNAB) 中的应用。甲状腺结节在一般人群中很常见，FNAB 是首选的采样方式。由此导致的执业病理学家日常工作量的普遍性使甲状腺 FNAB 成为应用 AI 解决方案的一个有吸引力的目标。概括：这篇综述总结了所有关于人工智能在甲状腺细胞病理学中的应用的现有文献。我们遵循从形态分析到卷积神经网络的演变。我们探讨了人工智能技术在甲状腺细胞病理学中的不同问题的应用，包括区分乳头状癌和良性、区分滤泡性腺瘤和癌以及通过关键词和短语识别具有乳头状核特征的非浸润性滤泡性甲状腺肿瘤。关键信息：当前的文献显示了将人工智能技术应用于甲状腺细针穿刺活检的前景。需要做很多工作来确定这种强大的技术将如何最好地用于细胞病理学实践的未来。
细胞学学报