Web- and Artificial Intelligence-Based Image Recognition For Sperm Motility Analysis: Verification Study

Vincent Fs Tsai; Bin Zhuang; Yuan-Hung Pong; Ju-Ton Hsieh; Hong-Chiang Chang

doi:10.2196/20031

Web- and Artificial Intelligence-Based Image Recognition For Sperm Motility Analysis: Verification Study

JMIR Med Inform. 2020 Nov 19;8(11):e20031. doi: 10.2196/20031.

Authors

Vincent Fs Tsai^{1

2}, Bin Zhuang³, Yuan-Hung Pong^{2

4}, Ju-Ton Hsieh², Hong-Chiang Chang²

Affiliations

¹ Department of Urology, Ten-Chan General Hospital, Taoyuan, Taiwan.
² Department of Urology, National Taiwan University Hospital, Taipei, Taiwan.
³ Division of Research and Development, Createcare Technology Corporation, Shenzhen, China.
⁴ Department of Urology, Ten-Chen General Hospital, Taoyuan, Taiwan.

PMID: 33211025
PMCID: PMC7714652
DOI: 10.2196/20031

Abstract

Background: Human sperm quality fluctuates over time. Therefore, it is crucial for couples preparing for natural pregnancy to monitor sperm motility.

Objective: This study verified the performance of an artificial intelligence-based image recognition and cloud computing sperm motility testing system (Bemaner, Createcare) composed of microscope and microfluidic modules and designed to adapt to different types of smartphones.

Methods: Sperm videos were captured and uploaded to the cloud with an app. Analysis of sperm motility was performed by an artificial intelligence-based image recognition algorithm then results were displayed. According to the number of motile sperm in the vision field, 47 (deidentified) videos of sperm were scored using 6 grades (0-5) by a male-fertility expert with 10 years of experience. Pearson product-moment correlation was calculated between the grades and the results (concentration of total sperm, concentration of motile sperm, and motility percentage) computed by the system.

Results: Good correlation was demonstrated between the grades and results computed by the system for concentration of total sperm (r=0.65, P<.001), concentration of motile sperm (r=0.84, P<.001), and motility percentage (r=0.90, P<.001).

Conclusions: This smartphone-based sperm motility test (Bemaner) accurately measures motility-related parameters and could potentially be applied toward the following fields: male infertility detection, sperm quality test during preparation for pregnancy, and infertility treatment monitoring. With frequent at-home testing, more data can be collected to help make clinical decisions and to conduct epidemiological research.

Keywords: Male infertility; artificial intelligence; cloud computing; home sperm test; semen analysis; smartphone; telemedicine.

©Vincent FS Tsai, Bin Zhuang, Yuan-Hung Pong, Ju-Ton Hsieh, Hong-Chiang Chang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 19.11.2020.