Morphological analysis of metabolically dysregulated spermatozoa using Artificial Intelligence based approach

Abstract

Sperm motility is an important parameter in evaluation of infertility in human semen samples and it’s directly associated with the Asthenozoospermia. Poor sperm movements are often related to less production of adenosine triphosphate (ATP), or due to less lactate fermentation and oxidative phosphorylation in mitochondria. In this article, Computer Assisted Sperm Analysis (CASA) based on Convolutional Neural Network (CNN) an Artificial Intelligence (AI) approach have been reported for the understanding of flagellar waveforms and propagation of sperm movement. We also show how microscopy systems are used for evaluating spermatozoa movements and to find the difference in their movement pattern, path length and area followed by flagella of sperm and how it links to its metabolic regulation. We found that sperms covering a distance less than 40µm/sec are metabolically dysregulated or are considered to produce less amount of ATP which could be a possible reason for no fertilization of ova in women. It also suggests that the flagella of sperm are linked with the metabolic activity of the sperm and its movement which affects the rate of the fertilization.

URN:NBN:sciencein.jist.2023.v11.569