Sperm-cell DNA fragmentation prediction using label-free quantitative phase imaging and deep learning (Conference Presentation)

Intracytoplasmic sperm injection (ICSI) is the most common practice for in vitro fertilization (IVF) treatments. In ICSI, a single sperm is selected and injected into an oocyte. The quality of the sperm and specifically its DNA fragmentation index (DFI) have significant effects on the fertilization success rate. In our research, we use computer vision and deep learning methods to predict DFI scoring for a single sperm cell. Each cell in the dataset was acquired using multiple white light microscopy techniques combined with state-of-the-art interferometry. In our results, we see a strong correlation between the stained images and our score prediction which can be used in the ICSI process.