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.

Prototype and evaluation of automatic fingertip-blood-sampling system that uses fingertip blood-vessel images to determine puncture position.

We are developing an automatic fingertip-blood-sampling system to reduce the burden on trained medical personnel. For this system to withdraw a consistent volume of sampled blood for blood tests, we developed a mechanism for our system to select and puncture the vicinity of a large blood vessel from the blood-vessel image of an individual's fingertip. We call this mechanism the fingertip-vessel-puncture mechanism. From the results of an experiment in which the fingertips of 20 individuals (men and women in their 20 s to 60 s) were manually punctured at near and far locations from the blood vessel selected with our mechanism, the following conclusions were obtained. The fingertip-vessel-puncture mechanism tends to increase the volume of sampled blood, thus is effective in sampling more than 650 µL of blood for automatic blood analyzers. It was also found that it is more effective in increasing the volume of sampled blood in the men and those who were younger.

A machine learning model of microscopic agglutination test for diagnosis of leptospirosis.

Leptospirosis is a zoonosis caused by the pathogenic bacterium Leptospira. The Microscopic Agglutination Test (MAT) is widely used as the gold standard for diagnosis of leptospirosis. In this method, diluted patient serum is mixed with serotype-determined Leptospires, and the presence or absence of aggregation is determined under a dark-field microscope to calculate the antibody titer. Problems of the current MAT method are 1) a requirement of examining many specimens per sample, and 2) a need of distinguishing contaminants from true aggregates to accurately identify positivity. Therefore, increasing efficiency and accuracy are the key to refine MAT. It is possible to achieve efficiency and standardize accuracy at the same time by automating the decision-making process. In this study, we built an automatic identification algorithm of MAT using a machine learning method to determine agglutination within microscopic images. The machine learned the features from 316 positive and 230 negative MAT images created with sera of Leptospira-infected (positive) and non-infected (negative) hamsters, respectively. In addition to the acquired original images, wavelet-transformed images were also considered as features. We utilized a support vector machine (SVM) as a proposed decision method. We validated the trained SVMs with 210 positive and 154 negative images. When the features were obtained from original or wavelet-transformed images, all negative images were misjudged as positive, and the classification performance was very low with sensitivity of 1 and specificity of 0. In contrast, when the histograms of wavelet coefficients were used as features, the performance was greatly improved with sensitivity of 0.99 and specificity of 0.99. We confirmed that the current algorithm judges the positive or negative of agglutinations in MAT images and gives the further possibility of automatizing MAT procedure.