A new deep‐learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure

Abstract

Abstract Purpose To create and evaluate a machine‐learning model for YOLOv3 that can simultaneously perform morphological evaluation and tracking in a short time, which can be adapted to video data under an inverted microscope. Methods Japanese patients who underwent intracytoplasmic sperm injection at the Jikei University School of Medicine and Keiai Reproductive and Endosurgical Clinic from January 2019 to March 2020 were included. An AI model that simultaneously performs morphological assessment and tracking was created and its performance was evaluated. Results For morphological assessment, the sensitivity and positive predictive value (PPV) of this model for abnormal sperm were 0.881 and 0.853, respectively. The sensitivity and PPV for normal sperm were 0.794 and 0.689, respectively. For tracking performance, among the 51 objects, 40 (78.4%) were mostly tracked, 11 (21.6%) were partially tracked, and 0 (0%) were mostly lost. Conclusions This study showed that evaluating sperm morphology while tracking in a single model is possible by training YOLO v3. This model could acquire time‐series data of one sperm, which will assist in acquiring and annotating sperm image data.

Keywords

• deep learning
• infertility
• intracytoplasmic sperm injection
• sperm morphology
• sperm motility