Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
This work addresses the need for improved, automated methods in male fertility assessment, offering a potential tool for clinical use, though it appears incremental as it builds on existing computer-aided systems with modern techniques.
The authors tackled the problem of predicting sperm motility by applying machine learning to 85 semen sample videos and participant data, finding that deep learning on videos alone provided rapid and consistent predictions, while adding participant data reduced performance.
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.