Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Clinical Engineering, Faculty of Medical Sciences, Juntendo University, Urayasu, Japan.
Acta Cytol. 2022;66(6):542-550. doi: 10.1159/000526098. Epub 2022 Sep 6.
Deep learning is a subset of machine learning that has contributed to significant changes in feature extraction and image classification and is being actively researched and developed in the field of cytopathology. Liquid-based cytology (LBC) enables standardized cytological preparation and is also applied to artificial intelligence (AI) research, but cytological features differ depending on the LBC preservative solution types. In this study, the relationship between cell detection by AI and the type of preservative solution used was examined.
The specimens were prepared from five preservative solutions of LBC and stained using the Papanicolaou method. The YOLOv5 deep convolutional neural network algorithm was used to create a deep learning model for each specimen, and a BRCPT model from five specimens was also created. Each model was compared to the specimen types used for detection.
Among the six models, a difference in the detection rate of approximately 25% was observed depending on the detected specimen, and within specimens, a difference in the detection rate of approximately 20% was observed depending on the model. The BRCPT model had little variation in the detection rate depending on the type of the detected specimen.
The same cells were treated with different preservative solutions, the cytologic features were different, and AI clarified the difference in cytologic features depending on the type of solution. The type of preservative solution used for training and detection had an extreme influence on cell detection using AI. Although the accuracy of the deep learning model is important, it is necessary to understand that cell morphology differs depending on the type of preservative solution, which is a factor affecting the detection rate of AI.
深度学习是机器学习的一个分支，它促进了特征提取和图像分类的重大变化，并且在细胞病理学领域得到了积极的研究和开发。液基细胞学（LBC）可实现细胞学标本的标准化制备，也适用于人工智能（AI）研究，但细胞学特征因 LBC 保存液类型而异。在本研究中，检查了 AI 细胞检测与保存液类型之间的关系。
使用 LBC 的五种保存液制备标本，并使用巴氏染色法进行染色。使用 YOLOv5 深度卷积神经网络算法为每个标本创建一个深度学习模型，还为五个标本创建了一个 BRCPT 模型。每个模型都与用于检测的标本类型进行了比较。
在六个模型中，根据检测的标本，检测率存在约 25%的差异，在标本内，根据模型，检测率存在约 20%的差异。BRCPT 模型根据检测标本的类型，检测率变化很小。
相同的细胞用不同的保存液处理，细胞学特征不同，AI 阐明了保存液类型对细胞学特征的差异。用于训练和检测的保存液类型对 AI 细胞检测的影响极大。尽管深度学习模型的准确性很重要，但有必要了解到，细胞形态因保存液类型而异，这是影响 AI 检测率的一个因素。
