Generative Artificial Intelligence in Sperm Analysis: Applications, Methods, and Future Directions

Generative Artificial Intelligence (GenAI) is a class of advanced artificial intelligence techniques capable of learning generation mechanism of data. Once this mechanism is learned, novel data that follows distribution and characteristics of the original data can be generated. Generative adversarial networks, variational auto-encoders, diffusion models, and normalizing flows are major generative models for generating image data. Sperm analysis is the primary diagnostic tool for assessing male infertility and selecting the most appropriate assisted reproductive technology. These analyses typically evaluate concentration, morphology, motility, and DNA integrity. However, manual analysis is complex, labor-intensive, and time-consuming, with time constraints for each analysis. To address these challenges, artificial intelligence techniques are increasingly employed. Among these, GenAI architectures demonstrate significant promise in improving performance and accuracy of artificial intelligence-based methods of sperm analysis. Various studies reveal that employing GenAI as an augmentation method improves artificial intelligence-based sperm analysis relative to using discriminative artificial intelligence techniques alone. Among diverse GenAI techniques, generative adversarial networks have been widely employed to improve performance of sperm analysis especially in the context of morphology assessment..

کلیدواژه ها:

Artificial intelligence ، Machine Learning ، GenAI ، sperm analysis ، Data Augmentation