@Article{info:doi/10.2196/20031,
author="Tsai, Vincent FS
and Zhuang, Bin
and Pong, Yuan-Hung
and Hsieh, Ju-Ton
and Chang, Hong-Chiang",
title="Web- and Artificial Intelligence--Based Image Recognition For Sperm Motility Analysis: Verification Study",
journal="JMIR Med Inform",
year="2020",
month="Nov",
day="19",
volume="8",
number="11",
pages="e20031",
keywords="Male infertility; semen analysis; home sperm test; smartphone; artificial intelligence; cloud computing; telemedicine",
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. ",
issn="2291-9694",
doi="10.2196/20031",
url="http://medinform.jmir.org/2020/11/e20031/",
url="https://doi.org/10.2196/20031",
url="http://www.ncbi.nlm.nih.gov/pubmed/33211025"
}