Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction

TL;DR

This study employs machine learning, including deep learning, to automatically analyze sperm videos and participant data for predicting male fertility, aiming to improve accuracy and consistency over manual methods.

Contribution

It introduces a combined approach using classical and modern machine learning techniques to predict sperm motility from videos and participant data, highlighting the potential for automated fertility assessment.

Findings

Deep learning methods provide rapid and consistent sperm motility predictions.

Adding participant data did not improve prediction accuracy.

Machine learning can potentially enhance male infertility diagnostics.

Abstract

Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological data are not used to its fullest potential. Manual evaluation of a semen sample using a microscope is time-consuming and requires extensive training. Furthermore, the validity of manual semen analysis has been questioned due to limited reproducibility, and often high inter-personnel variation. The existing computer-aided sperm analyzer systems are not recommended for routine clinical use due to methodological challenges caused by the consistency of the semen sample. Thus, there is a need for an improved methodology. We use modern and classical machine learning techniques together with a dataset consisting of 85 videos of human semen samples and related participant data to automatically predict sperm motility. Used techniques include simple linear regression and more sophisticated methods using convolutional neural networks. Our results indicate that sperm motility prediction based on deep learning using sperm motility videos is rapid to perform and consistent. The algorithms performed worse when participant data was added. In conclusion, machine learning-based automatic analysis may become a valuable tool in male infertility investigation and research.