A new sperm preparation solution improves the outcome of human conventional in vitro fertilization with hyperactivated spermatozoa

Abstract

source, and batteries. A smartphone application was developed which performed the analysis locally. The AI utilized by the application was transfer-learned, trained, and validated with 1790 embryo images. To test our system, 50 embryos donated by patients were imaged using the smartphone system at 113 hpi. Images were automatically analyzed by our developed network without the need for any image processing. Performance metrics were calculated for the smartphone system and the overall performance of the smartphone system with the performance of deep-learning based approach that used Embryoscope data was compared.RESULTS: The accuracy of such a system in classifying 50 embryos based on their blastocyst status was 96% (CI: 86.29% to 99.51%). Its sensitivity and specificity were 93.55% (CI: 78.58% to 99.21%) and 100% (CI: 82.35% to 100%), respectively, while its positive and negative predictive values were 100% and 90.48% (CI: 71.32% to 97.32%), respectively. A chi-squared analysis comparing the performance of an Embryoscope-based deep-learning approach with our smartphone system-based deep-learning approach revealed an insignificant difference of 5.03% (P¼0.33, P>0.05).CONCLUSIONS: The results reported here demonstrate that combined with the use of an AI-empowered imaging system, automated embryo analysis is not limited to only expensive time-lapse hardware and inexpensive (<$100) systems can be developed for use at fertility centers without loss in performance. The overall impact of our AI-empowered system is significant since it enables integration into clinical practices at resource-limited settings at very minimal costs.