Rupture Prediction for Microscopic Oocyte Images of Piezo Intracytoplasmic Sperm Injection by Principal Component Analysis

Abstract

Assisted reproductive technology (ART) has progressed rapidly, resulting in a great improvement in the clinical pregnancy ratio. When applying the protocol of piezo intracytoplasmic sperm injection (Piezo-ICSI), it is very important to puncture the zona pellucida and the oocyte cytoplasmic membrane without rupturing the oocyte cytoplasmic membrane. Previous studies have shown that the poor extensibility of the oocyte cytoplasmic membrane might be closely related to rupture. However, no consensus has been reached regarding how the quality of the oocyte for extensible ability or rupture possibility affects the surfaces of the oocyte on the microscopic frames. We conducted this study to provide evidence that artificial intelligence (AI) techniques are superior for predicting the tendency of oocyte rupture before puncturing on Piezo-ICSI. To inspect it, we provided a retrospective trial of 38 rupture oocytes and 55 nonruptured oocytes. This study marked the highest accuracy of 91.4% for predicting oocytes rupture using the support-vector machine method of machine learning. We conclude that AI technologies might serve an important role and provide a significant benefit to ART.