motilitAI: A machine learning framework for automatic prediction of human sperm motility

Sandra Ottl; Shahin Amiriparian; Maurice Gerczuk; Björn W Schuller

doi:10.1016/j.isci.2022.104644

motilitAI: A machine learning framework for automatic prediction of human sperm motility

iScience. 2022 Jun 20;25(8):104644. doi: 10.1016/j.isci.2022.104644. eCollection 2022 Aug 19.

Authors

Sandra Ottl¹, Shahin Amiriparian¹, Maurice Gerczuk¹, Björn W Schuller^{1

2}

Affiliations

¹ Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany.
² GLAM - Group on Language, Audio, and Music, Imperial College London, UK.

Abstract

In this article, human semen samples from the Visem dataset are automatically assessed with machine learning methods for their quality with respect to sperm motility. Several regression models are trained to automatically predict the percentage (0-100) of progressive, non-progressive, and immotile spermatozoa. The videos are adopted for unsupervised tracking and two different feature extraction methods-in particular custom movement statistics and displacement features. We train multiple neural networks and support vector regression models on the extracted features. Best results are achieved using a linear Support Vector Regressor with an aggregated and quantized representation of individual displacement features of each sperm cell. Compared to the best submission of the Medico Multimedia for Medicine challenge, which used the same dataset and splits, the mean absolute error (MAE) could be reduced from 8.83 to 7.31. We provide the source code for our experiments on GitHub (Code available at: https://github.com/EIHW/motilitAI).

Keywords: Artificial intelligence; Biocomputational method; Bioinformatics; Health sciences; Medicine; Reproductive medicine.