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

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. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s11517-022-02523-1.

Machine learning in transfusion medicine: A scoping review

“…In a laboratory setting, 63% (six of eight) studies investigated the use of ML to assist blood group identification including two to help classify antibody reactions where ambiguity in human interpretation exists. 89,90 Doan et al 91 and Kim et al 92 introduced image-based deep learning as a novel approach to perform phenotype assessment of red blood cell storage lesions to predict red cell quality prior to transfusion.…”

Prenatal diagnosis of fetomaternal hemorrhage by a novel hydrogel fluoroimmunoassay that accurately quantifies fetal haemoglobin

“…The hydrogel medium is in a flowing state at room temperature. Under the influence of centrifugal force, high-density RBCs and low-density small molecule antibodies can be directly separated, and then complicated steps such as washing can be omitted, making the procedure simple, quick, and convenient ( Wu et al, 2022 ). The hydrogel fluoroimmunoassay uses the combination of sensitized indicator RBCs and fluorescently labelled anti-HbF antibody, and determines the fluorescence value by flow cytometry.…”