Emulating visual evaluations in the microscopic agglutination test with deep learning.

The Microscopic Agglutination Test (MAT) is widely recognized as the gold standard for diagnosing zoonosis leptospirosis. However, the MAT relies on subjective evaluations by human experts, which can lead to inconsistencies and inter-observer variability. In this study, we aimed to emulate expert assessments using deep learning and convert them into reproducible numerical outputs to achieve greater objectivity.

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.

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.