Reproducing Human Subjective Evaluation in the Microscopic Agglutination Test with Deep Learning

Abstract

ABSTRACTThe Microscopic Agglutination Test (MAT) is widely recognized as the gold standard for diagnosing zoonosis leptospirosis. However, a significant limitation of MAT is the inconsistency in test results, as it relies on the examiners’ subjective perceptions to estimate agglutination rates. To address this issue, we propose a deep neural network to replicate the subjective evaluation process of agglutination rate estimation in MAT. By employing a pre-trained DenseNet121, we can efficiently optimize the network parameters during the training phase. We validated the trained network using our original dataset. Experimental results demonstrate that the proposed network provides accurate agglutination rate estimates. Furthermore, we utilize a standard visualization technique to gain insights into the decision-making process of the deep learning methods. The findings reveal that the proposed network extracts image features indicative of leptospire abundance. Overall, these results suggest that deep learning is effective for estimating agglutination rates and that enhancing interpretability aids medical experts in understanding the functionality of deep learning.