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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.
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Laboratory testing is of great value in the management of autoimmune disease. The results can help confirm a diagnosis, estimate disease severity, aid in assessing treatment effect. But the current autoimmunity laboratory system, including testing standards, quality control and supervision, does not match the national conditions well. As a result, the test reports are not mutual-recognized among laboratories. In the current background of precision medicine, with the advances of technology and the application of deep learning and artificial intelligence in the clinical laboratory field, the autoimmune laboratory has ushered in a new development trend of integration, automation and intelligence.
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Laboratory testing is of great value in the management of autoimmune disease. The results can help confirm a diagnosis, estimate disease severity, aid in assessing treatment effect. But the current autoimmunity laboratory system, including testing standards, quality control and supervision, does not match the national conditions well. As a result, the test reports are not mutual-recognized among laboratories. In the current background of precision medicine, with the advances of technology and the application of deep learning and artificial intelligence in the clinical laboratory field, the autoimmune laboratory has ushered in a new development trend of integration, automation and intelligence.
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Objective To investigate the correlation between atypical respiratory pathogens infection and serum total IgE levels . Methods Serum IgM level was detected in 1 913 blood samples of children with atypical respiratory infection by using indirect im‐munofluorescence assay ,including mycoplasma pneumonia(MP) ,legionella pneumophila (LP) ,rickettsia Q(QFR) ,chlamydia pneu‐monia(CPn) ,adenovirus (Adv) ,respiratory syncytial virus (RSV) ,influenza A virus (IAv) ,influenza B virus (IBv) and parainflu‐enza virus (PIV)1/2/3 .The serum total IgE level was detected by immune scatter turbidimetry .Software SPSS 17 .0 was used in data statistical analysis .Results A total of 991 out of 1 913 samples of respiratory inflected children exhibited positive(positive group) ,while 922 exhibited negative(negative group) in indirect immunofluorescence assay .650 out of the 991 positive samples (65 .59% ) contained MP infection and the combination of MP infection and other virus infections .The serum total IgE level in posi‐tive group was significantly higher than that of the negative group ,and the serum total IgE level in samples with MP infection was higher than that in samples with IBv infection ,Adv infection ,and RSV infection .In the samples in which serum total IgE level was higher than the clinical reference range (100 kU/mL) ,the infection rate of MP infection alone was 31 .29% ,which was evidently higher than that in samples of low IgE level(< 100 kU/mL ,21 .30% ) .On the other hand ,the infection rate of RSV alone was 1 .88% and the infection rate of Adv alone was 3 .13% ,which were both evidently lower than those in samples with normal serum total IgE level(both 6 .53% ) .Conclusion MP is the most common pathogen in children with atypical respiratory pathogen infec‐tion ,and can lead to higher serum total IgE levels .
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opping drinking. They were not influenced by gender, smoking and drinking histories. They could serve as monitoring indexes for recent drinking status on healthy individuals.