A new reliable test for crossmatching: microplate hydrogel immunoassay technology

A CNN-LASSO ensemble classification model for incomplete antibody reactants screening in coombs test

BACKGROUND

Precise classification of incomplete antibody reactants (IAR) in the Coombs test is the primary means to prevent incompatible blood transfusions. Currently, an automatic and contactless method is required for accurate IAR classification to avoid human error.

OBJECTIVE

We present an ensemble learning algorithm that integrates five convolutional neural networks and the least absolute shrinkage and selection operator (LASSO) regression algorithm into an IAR intensity classification model.

METHODS

A dataset including 1628 IAR and corresponding labels of IAR intensity categories ((-), (1+), (2+), (3+), and (4+)) was used. We trained the ensemble model using 1302 IAR and validated its performance using 326 IAR. The optimal ensemble model was used to assist immunologists in classifying IAR. The chord diagrams based on the human-machine interaction were established.

RESULTS

The ensemble model achieved 98.8%, 98.4%, 99.7%, 99.5%, and 99.4% accuracies in the (-), (1+), (2+), (3+), and (4+) categories, respectively. The results were compared with those of manual classification by immunologists (average accuracy: 99.2% vs. 75.6%). Using the model, all three immunologists achieved increased accuracy (average accuracy: +8.4%).

CONCLUSIONS

The proposed algorithm can thus effectively improve the accuracy and efficiency of IAR intensity classification and facilitate the automation of haemolytic disease screening equipment.

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

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.