Application of deep learning in immunofluorescence images recognition of antinuclear antibodies
10.3760/cma.j.cn114452-20230215-00092
- VernacularTitle:深度学习在抗核抗体荧光核型识别中的应用初探
- Author:
Junxiang ZENG
1
;
Wenqi JIANG
;
Jingxu XU
;
Yahui AN
;
Chencui HUANG
;
Xiupan GAO
;
Youyou YU
;
Xiujun PAN
;
Lisong SHEN
Author Information
1. 上海交通大学医学院附属新华医院检验科，上海 200092
- Keywords:
Artificial intelligence;
Deep learning;
Antibodies, antinuclear
- From:
Chinese Journal of Laboratory Medicine
2023;46(10):1094-1098
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a prototype artificial intelligence immunofluorescence image recognition system for classification of antinuclear antibodies in order to meet the growing clinical requirements for an automatic readout and classification of immunof luorescence patterns for antinuclear antibody (ANA) images.Methods:Immunofluorescence images with positive results of ANA in Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from April 2020 to December 2021 were collected. Three senior technicians independently and in parallel interpreted the Immunofluorescence images to determine the ANA results. Then the images were labeled according to the ANA International Consensus on Fluorescence Patterns (ICAP) classification criteria. There were 7 labeled groups: Fine speckled, Coarse speckled, Homogeneous, nucleolar, Centromere, Nuclear dots and Nuclear envelope. Each group was randomly divided into training dataset and validation dataset at a ratio of 9∶1 by using random number table. On the deep learning framework PyTORCH 1.7, the convolutional neural network (CNN) training platform was constructed based on ResNet-34 image classification network, and the automatic ANA recognition system was established. After the model was established, the test set was set up separately, the judgment results of the model were output by ranking the prediction probability, with the results of the 2 senior technicians was taken as "golden standard". Parameters such as accuracy, precision, recall and F1-score were used as indicators to evaluate the performance of the model.Results:A total of 23138 immunofluorescence images were obtained after segmentation and annotation. A total of 7 models were trained, and the effects of different algorithms, image processing and enhancement methods on the model were compared. The ResNet-34 model with the highest accuracy andswas selected as the final model, with the classification accuracy of 93.31%, precision rate of 91%, and recall rate of 90% and F1-score of 91% in the test set. The overall coincidence rate between the model and manual interpretation was 90.05%, and the accuracy of recognition of nucleolus was the highest, with the coincidence rate reaching 100% in the test set.Conclusion:The current AI system developed based on deep learning of the ANA immunofluorescence images in the present study showed the ability to recognize ANA pattern, especially in the common, typical, simple pattern.
