Objectification of evaluation criteria in microscopic agglutination test using deep learning.

We aim to objectify the evaluation criteria of agglutination rate estimation in the Microscopic Agglutination Test (MAT). This study proposes a deep learning method that extracts free leptospires from dark-field microscopic images and calculates the agglutination rate. The experiments show the effect of objectification with real pictures.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: We hereby declare the following conflicts of interest in relation to this study. The authors YO and RO are co-inventors of a patent 7370573 based on the technology developed in this research. This patent is registered in Japan. To ensure that this conflict of interest has not biased the research outcomes, an independent audit of the entire process of data collection, analysis, and interpretation was conducted. The other co-authors declare no direct financial conflicts of interest related to this research.
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