Estimation of Multi-Expert Sperm Assessments Using Video Recognition Based Model Trained by EMD Loss

Infertility is a common problem, affecting approximately one in six adults worldwide. Some studies have shown that male factors contribute to infertility in up to 50% of couples. Intracytoplasmic sperm injection (ICSI) is a common treatment for male infertility. This procedure requires a quick and accurate determination of whether sperm are suitable for ICSI. However, this assessment requires expertise and is time-consuming. Several computer-based systems for sperm analysis have been proposed to mitigate the burden on experts. However, there are no systems that can consider both sperm motility and morphology, or that can directly assess sperm suitability for ICSI. To address this problem, we constructed the multi-expert rated sperm video dataset for analysis, that includes motion information and developed an end-to-end sperm grade distribution estimation model using this dataset. Our model predicts a distribution that reflects multiple expert assessments, and thus helps to easily determine the suitability of a given sperm for ICSI. To develop this model, we conducted an exhaustive evaluation of various feature extractors and loss functions. Through this analysis, TimeSformer was identified as the optimal feature extractor from sperm videos, improving on average by
$0.1\times 10^{-2}$
in MSE, 1.17% in grade distribution accuracy, and 3.41% in grade mode accuracy compared to ResNet, an image recognition model. Moreover, we identified earth mover’s distance loss as the most suitable loss function, particularly in segments with lower scores.