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

Reproductive medicine and biology

article

Takuma Sato¹  Hiroshi Kishi¹  Saori Murakata¹  Yuki Hayashi¹  Toshiyuki Hattori²  Shinji Nakazawa³  Yusuke Mori¹  Miwa Hidaka¹  Yuta Kasahara¹  Atsuko Kusuhara¹  Kayo Hosoya⁴  Hiroshi Hayashi⁴  Aikou Okamoto¹ 

[1]Department of Obstetrics and Gynecology, The Jikei University School of Medicine

[2]Technology Innovation

[3]LPIXEL Inc.

[4]Keiai Reproductive and Endosurgical Clinic

关键词: deep learning;    infertility;    intracytoplasmic sperm injection;    sperm morphology;    sperm motility;   

DOI  :  10.1002/rmb2.12454

学科分类：工业工程学

来源: Wiley

【 摘 要 】

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.

【 授权许可】

CC BY|CC BY-NC|CC BY-NC-ND