Automatic analysis of different types of clinical data is currently advancing rapidly, in particular, multimodal image analysis (learning simultaneously from various sources of data). At this year's ESHRE Annual Meeting, for example, there were several presentations on the subject of machine learning (a subfield of artificial intelligence) and reproductive outcomes. Though promising, most of such current research in human reproduction is, from a machine learning point of view, still in its infancy. Now, a new study from our group in Oslo shows that advanced machine learning methods for analysing videos of semen samples may be a useful tool in the investigation of male infertility.(1) Manual semen analysis is central to male infertility investigation, but is time-consuming and requires extensive training to obtain reproducible results.

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.