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Knowledge of spermatozoa motility is important in selecting suitable spermatozoa for assisted reproduction procedures. By considering the internal sliding force within an active filament, its shape in a viscoelastic Oldroyd-B fluid subjected to nonuniform electric field is presented. The resulting velocity is a function of the beating pattern and DEP force, which is dependent on the spermatozoon's morphology as well as the gradient of the mean square electric field. Finally, the velocities of the X- and Y-spermatozoa are compared under various conditions of nonuniform electric field and viscoelasticity of the medium. The presence of DEP force alters their velocities to different extents, giving an 84% level of confidence for selecting spermatozoon that contains the chromosome leading to a female. Therefore, a nonuniform electric field can be used not only to sort and select spermatozoa of desired morphology, but also for gender selection. Moreover, we found that sorting in a viscoelastic fluid medium is more effective as the effect of DEP on the spermatozoa velocity is enhanced by an order of magnitude.

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Machine learning is gaining popularity in the commercial world, but its benefits are yet to be well-utilised by many in the microfluidics community. There is immense potential in bridging the gap between applied engineering and artificial intelligence as well as statistics. We illustrate this by a case study investigating the sorting of sperm cells for assisted reproduction. Slender body theory (SBT) is applied to compute the behavior of sperm subjected to magnetophoresis, with due consideration given to statistical variations. By performing computations on a small subset of the generated data, we train an ensemble of four supervised learning algorithms and use it to make predictions on the velocity of each sperm. Our results suggest that magnetophoresis can magnify the difference between normal and abnormal cells, such that a sorted sample has over twice the proportion of desirable cells. In addition, we demonstrated that the predictions from machine learning gave comparable results with significantly lower computational costs.