A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes.

Infertility clinics would benefit from the ability to select developmentally competent vs. incompetent oocytes using non-invasive procedures, thus improving the overall pregnancy outcome. We recently developed a classification method based on microscopic live observations of mouse oocytes during their in vitro maturation from the germinal vesicle (GV) to the metaphase II stage, followed by the analysis of the cytoplasmic movements occurring during this time-lapse period. Here, we present detailed protocols of this procedure. Oocytes are isolated from fully-grown antral follicles and cultured for 15 h inside a microscope equipped for time-lapse analysis at 37 °C and 5% CO2. Pictures are taken at 8 min intervals. The images are analyzed using the Particle Image Velocimetry (PIV) method that calculates, for each oocyte, the profile of Cytoplasmic Movement Velocities (CMVs) occurring throughout the culture period. Finally, the CMVs of each single oocyte are fed through a mathematical classification tool (Feed-forward Artificial Neural Network, FANN), which predicts the probability of a gamete to be developmentally competent or incompetent with an accuracy of 91.03%. This protocol, set up for the mouse, could now be tested on oocytes of other species, including humans.