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Convolutional neural network-based automatic classification for incomplete antibody reaction intensity in solid phase anti-human globulin test image

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Abstract

The precise classification of incomplete antibody reaction intensity (IARI) in hydrogel chromatography medium high density medium solid-phase Coombs test is essential for haemolytic disease screening. However, an automatic and contactless method is required for accurate classification of IARI. Here, we present a deep ensemble learning model that integrates five different convolutional neural networks into a single model for IARI classification. A dataset, including 1628 IARI images and corresponding labels of IARI categories ((-), (1 +), (2 +), (3 +), and (4 +)), was used. We trained our model using 1302 IARIs and validated its performance using 326 IARIs. The proposed model achieved 100%, 99.4%, 99.4%, 100%, and 100% accuracies in the ( −), (1 +), (2 +), (3 +), and (4 +) categories, respectively. The results were compared with those of manual classification by immunologists (average accuracy: 99.8% vs. 88.3%, p < 0.01). Following model assistance, all three immunologists achieved increased accuracy (average accuracy: + 6.1%), with the average accuracy of junior immunologists maximum increasing by 11.3%. The time required for model classification was 0.094 s·image–1, whereas that required manually was 5.528 s·image–1. The proposed model can thus substantially improve the accuracy and efficiency of IARI classification and facilitate the automation of haemolytic disease screening equipment.

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Acknowledgements

We thank the Wiley Editing Services for editorial assistance.

Funding

This work was supported by the Development Program of Guangdong Province [grant number: 2019B010152001]; the Key Research and Development Program of Jiangsu [grant number: BE2021663]; the Research Fund of Jihua Laboratory [grant number: X190171TD190]; the Special fund for high-tech industrialisation of science and technology cooperation between Jilin Province and Chinese Academy of Sciences [grant number: 2020SYHZ0025]; the National Natural Science Foundation of China [grant number: 81871439]; and the Shandong Province Department of Science and Technology [grant number: ZR2020QF019].

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    KeQing Wu

  2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, 215163, China

    KeQing Wu & Xin Gao

  3. CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, 215163, China

    ShengBao Duan, YuJue Wang & HongMei Wang

  4. Jinan Guoke Medical Engineering and Technology Development Co., Ltd., Pharmaceutical Valley New Drug Creation Platform, Jinan, 250109, Shandong, China

    Xin Gao

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Xin Gao designed the study; HongMei Wang performed data collection; HongMei Wang ShengBao Duan, and YuJue Wang labelled data; and KeQing Wu performed the research and wrote the paper.

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Wu, K., Duan, S., Wang, Y. et al. Convolutional neural network-based automatic classification for incomplete antibody reaction intensity in solid phase anti-human globulin test image. Med Biol Eng Comput 60, 1211–1222 (2022). https://doi.org/10.1007/s11517-022-02523-1

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