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Original Paper

Web- and Artificial Intelligence–Based Image Recognition For
Sperm Motility Analysis: Verification Study
Vincent FS Tsai1,2, MD, PhD; Bin Zhuang3, MEng; Yuan-Hung Pong2,4, MD; Ju-Ton Hsieh2, MD; Hong-Chiang
Chang2, MD
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

Corresponding Author:
Hong-Chiang Chang, MD
Department of Urology
National Taiwan University Hospital
7, Zhung-Shan S. Road
Taipei, 100
TaiwanPhone: 886 223123456 ext 62135
Email: bird8873@gmail.com

Abstract
Background: Human sperm quality fluctuates over time. Therefore, it is crucial for couples preparing for natural pregnancy tomonitor 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 todifferent types of smartphones.
Methods: Sperm videos were captured and uploaded to the cloud with an app. Analysis of sperm motility was performed byan 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 ofexperience. 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.
(JMIR Med Inform 2020;8(11):e20031) doi: 10.2196/20031
KEYWORDS
Male infertility; semen analysis; home sperm test; smartphone; artificial intelligence; cloud computing; telemedicine

Introduction
Infertility is a worldwide problem, with a prevalence of 15%[1], and sperm plays an important role [2]. In the process of
fertilization, sperm are initially ejaculated around the cervix
and then swim to the proximal oviduct, where they encounter
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the oocyte and accomplish fertilization [3]. To accomplish
fertilization, large numbers of sperm are needed to overcome
the filtration of cervical mucus while progressive motility isnecessary for sperm to travel for a long distance. Human sperm
concentration and motility fluctuate over time [4]. Therefore,
frequent monitoring of sperm concentration and motility is

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crucial for couples who are preparing for spontaneous
pregnancy.

measurements of blood sugar, blood pressure, and bodytemperature.

Conventionally, men are asked to have semen analysis
performed at a doctor's office or laboratory, a procedure that isboth time-consuming and embarrassing for men, according toour clinical observations and prior literature [5]. Hence, it makes
frequent measurement of sperm concentration and motility
difficult. To tackle this problem, several trials of home sperm
tests were developed in the past few decades, such as
SpermCheck [6], Fertell [7], and Trak Male Fertility Testing
System [8]. Some, such as YO sperm test and the Kobori single
ball-lens system, attracted a lot of attention; both are systems
that utilizes platforms based on smartphone systems [9,10].
There are some advantages to using smartphone-based home
sperm tests, such as high-resolution video recording, robust
calculation ability, and accessible internet communication. All
these advantages can allow users to be tested in a setting thatencourages more frequent measurements, while preserving theprivacy and accuracy of results, similar to those of otherpoint-of-care test systems that have been applied in

Bemaner (Shenzhen Createcare Technology Co) is asmartphone-based home sperm motility measurement systemcomposed of a microscope and microfluidic modules and isdesigned to adapt to different types of smartphone designs andinterfaces (Figure 1). Bemaner is quite different from the YO
sperm test and the Kobori single ball-lens system [10]. The YO
sperm test utilizes a tailor-made adapter (slide) to fit specifictypes of smartphones, but Bemaner can fit all smartphones
currently. Sperm videos are captured and uploaded to the cloudfrom the smartphone via software apps. Unlike the Kobori single
ball-lens system, in which motile and static sperm are counted
by a person [10], for Bemaner, analysis of sperm motility is
performed by an artificial intelligence (AI) image recognition
algorithm, and results are displayed to end users on thesmartphone interface. The purpose of this study was to verify
the performance of the AI sperm image recognition algorithmand cloud computing system.

Figure 1. Bemaner smartphone-based home sperm motility test system.

Methods
Video Clips of Motile Sperm and Results Computed
by AI
Semen samples can be collected at home by users. After 30
minutes of liquefaction, the semen sample is dipped by a small
biochip cup (analog to glass slide) and then covered by a large
biochip cup (analog to cover slip). The space containing semen
samples between these two cups is designed to be 10
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micrometers deep, which can contain a single layer of sperm
for a specific volume of 0.2 microliters. Through themicroscopic and microfluidic modules of the device, the video
clips of motile sperm can be captured and uploaded by any
recent smartphone, which is easily aligned to the microscopic
and microfluidic modules (Multimedia Appendix 1).
Deidentified (ie, users' information removed) video clips ofmotile sperm were retrieved from the central cloud computing
server (Alibaba cloud, Hangzhou, China). With the process ofdeidentification, it was impossible to obtain specific user consent
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for this study. However, the security and privacy of users'
information were well protected through the process ofdeidentification.

between grade 3 and 5; grade 5, motile sperm occupy almost
the whole field of vision (Multimedia Appendix 2-Multimedia
Appendix 7).

The results, including the concentration of total sperm, theconcentration of motile sperm, and the motility percentage,
computed by the AI image recognition algorithm (version
1.0.8_5/22) with cloud computing on the central server werealso retrieved with every video clip of motile sperm. In detail,concentration of total sperm and motile sperm were derived by
the image recognition AI algorithm. Motility percentage iscalculated as the quotient of concentration of motile to total
sperm.

Relationship Between Human and AI Results

Scoring of Motile Sperm

Results

The gold standard for assessing semen quality in the current
World Health Organization manual [4] is analysis performed
under a microscope by a well-trained professional staff.
According to the number of motile sperm in the vision field ofeach video clip, the 47 deidentified videos of motile sperm wereclassified into 6 grades (0-5) by a male-fertility expert with 10
years of experience. The criteria were the following: grade 0,
there are no areas of motile sperm in the field of vision; grade
1, some motile sperm; grade 2; the amount of motile sperm is
between grade 1 and 3; grade 3, motile sperm occupy half ofthe field of vision; grade 4, the number of motile sperm is

The results (concentration of total sperm, concentration of motile
sperm and motility percentage) of the AI algorithm and the
distribution of scored grade according to the number of motile
sperm in the vision field of each video clip are is shown in Table
1.

The grade assessed by a male-fertility expert was an ordinalvariable; the relationships between grade and concentration of
total sperm, concentration of motile sperm, and motility
percentage was calculated determined with Pearson
product-moment correlation coefficients. Analyses were
calculated using the software Excel (Microsoft Inc, 2013). A P
value less than .01 was considered statistically significant.

Relationships between the grades and Bemaner AI algorithm
results were r=0.65 (P<.001) for concentration of total sperm(Figure 2), r=0.90 (P<.001) for motility percentage (Figure 3),
and r=0.84 (P<.001) for concentration of motile sperm (Figure
4).

Table 1. Results.
Source and variable

Value

Bemaner AI algorithm-based, mean (SD)Concentration of total sperm (million cell/mL)

111.02 (72.13)

Concentration of motile sperm (million cell/mL)

50.87 (61.47)

Motility percentage (%)

32.8 (32.5)

Sperm motility count by expert grade (million cell/mL), n
Grade 07

Grade 18

Grade 28

Grade 3

5

Grade 414

Grade 5

5

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Figure 2. Relationship between Bemaner concentration of total sperm and scored grades.

Figure 3. Relationships between Bemaner motility percentage and scored grades.

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Figure 4. Relationships between Bemaner concentration of motile sperm and scored grades.

Discussion
To our knowledge, this is the first smartphone-based system
for semen analysis accomplished through an AI image
recognition algorithm on cloud computing. Even though there
are other smartphone-based systems, such as YO sperm test [9]and Kobori single ball-lens system [10], neither utilizes an AI
imaging recognition algorithm with cloud computing. YO sperm
test analyzes sperm videos by an app installed on a smartphone,while Kobori single ball-lens system projects the mobile phone
captured videos onto a desktop monitor and counts sperm
parameters manually.
Additionally, there are some benefits for data processing throughcloud computing. Only requiring connection with the internet,
the system provides an increased flexibility and possibility interms of setting up test locations and test frequencies. It is alsoeasier for engineers to perform system maintenance such asalgorithm revision and software updates instead of frequent
update requests on the user end smartphone app. Also,
accumulated big data may demonstrate important

epidemiological characteristics of male fertility (eg, regional or
temporal). Such data processes, based on cloud computing, arequite novel and promising for large-scaled epidemiologicalstudies and public-health related policy making.
The results calculated by Bemaner and grades assessed by amale-fertility expert, motility percentage and concentration of
motile sperm, demonstrated better correlations with the scoredgrade. The respective correlation coefficients are 0.90 (P<.001)
and 0.84 (P<.001). This means that motility percentage andconcentration of motile sperm calculated by Bemaner werecomparable to a male-fertility expert's judgment for assessing
sperm motility.
However, concentration of total sperm was only moderately
correlated (r=0.65; P<.001) with the scored grade. This is
attributed to some of total sperm being immobile sperm, whichcannot achieve natural fertilization [11,12].
With regard to corresponding motile sperm concentration ofeach scored grade, the calibrated reference values of sperm
concentration are provided (Table 2).

Table 2. Calibrated reference values of sperm concentration.
Grade

Range (million cells/mL)

0

0

1

<13.9

2

0-27.73

28.3-51.9

4

50.7-113.9

5

153.4-217.6

The accuracy of these values can be improved as theaccumulated data size grows. This is also one of the benefits
that these types of online processes for point-of-care test results
can provide.

In comparison with other smartphone-based sperm analysis
systems, such as YO sperm test [9] and the Kobori single
ball-lens system [10], Bemaner can provide users withinformation about sperm concentration, motile sperm

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percentage, and motile sperm concentration. These parameters
are essential for a man to achieve natural fertilization. That may
be the reason why these parameters are chosen for screening
tests [13,14].
Graded results show more information about fluctuation of
sperm concentration and motility than results indicating positiveor negative, such as SpermCheck [6] and Fertell [7]. It is also
more easily understood by lay users. YO sperm test andBemaner, in the latest version of app on the smartphone provides
not only total sperm concentration, motile sperm concentration,and percentage of motile sperm but also graded results of the
motile sperm concentration based on the findings of this study.
Both provide graded results for motile sperm concentration (ie,YO score and Bemaner scored grade). It can be used as a home
sperm test instead of having comprehensive and robust sperm
parameters assessments in a fertility laboratory [4]. Practically,
graded results can be applied in some scenarios that require
information about fluctuating test results, such as male infertility
detection, sperm quality test during preparation for pregnancy,
and infertility treatment monitoring.
YO sperm test obtained test results by the installed algorithm
and the results were compared to those of anotherimage-recognition sperm test SQA-Vision (Medical Electronics
Systems) by Agarwal et al [9]. Kobori et al [10] counted sperm
on images projected to desktop screen and compared their resultswith computer assisted semen analysis. In contrast, Bemanerused an AI algorithm with cloud computing to analyze images
for sperm testing. The results were compared with the grades
assessed by an experienced male-fertility expert. Because theimages for analysis were uploaded by end users and deidentified,
it was impossible to acquire any original parameters of thecorresponding semen samples. (The appearance, viscosity, pH,
total sperm concentration and motile sperm concentration
manually measured under microscope for a semen sample are
original parameters of the sample. Only the previous captured
and uploaded videos of sperm could be retrieved in this study.)
Therefore, grades of sperm images were utilized as the reference
method. This is one of the limitations of the study.
Currently, the aforementioned 3 systems cannot analyze sperm
images to assess sperm morphology according to the World
Health Organization morphology assessment paradigm [4]. A
new version of the AI algorithm including morphology
assessment is currently in development for the Bemaner system.
In the face of emerging pandemics causing city lockdown andhealth care need for remote regions, tele-medicine will prevail
and requires on-line processes of data and analysis connected

Tsai et alwith off-line point-of-care tests for end users. The Bemaner
system is a prime example of this type of implementation,
especially for infertility, which is always related to the following
aspects: environmental pollution, life style, and diet [15-17],
the analysis of big data could contribute much more than
traditional case reports in research [18].
The framework of data processing in this system can bereconstructed as the big data grows and we extend our scope tocollect the different aspects related to users' life style, diet,environmental pollutions, and medical interventions. In the
future, some techniques of cloud computing and data storage
to optimize big data processing could be applied to help speed
the process, such as adopting concepts such as intermediate datacaching. Nevertheless, the current process of data transmission
and analyzing is similar to an application program interface
call, in which a request with the data is sent to the server, theserver processes the data with our image recognition algorithm
to generate the result, and the server responses with an answer.
There are still some improvements to be implemented in thenear future. The biochip cups for containing semen specimens
could be adjusted automatically by a step-motor to increase thescope of observation and the number of observed sperm. The
optic module could be modified to increase the magnification
to define sperm's morphology according to the WHO criteria[4]. Revisions of the AI image recognition algorithm willcontinue to improve as data accumulates, enhancing its accuracy.
Big data for this system has huge potential to introduce various
insight, both in clinical and epidemiological fields, to improve
human fertility.
This smartphone-based system for measuring sperm motility(Bemaner) accurately measures parameters related to sperm
motility 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 testing, more data can be collected to help makeclinical decisions and conduct epidemiological studies.
The overall contributions of this paper are (1) to introduce anovel online system for semen analysis calculated by an AI
image recognition algorithm combined with cloud computing
technology; (2) to prove the performance of the system bycorrelating human and AI results; (3) to suggest potential
applications in male infertility detection, sperm quality test
during preparation for pregnancy, and infertility treatment
monitoring; and (4) to foresee that the big data collected by thissystem can play an important role in making clinical decisions
and conducting epidemiological research.

Acknowledgments
Authors want to thank Yu-Chian Tsai from the University of Michigan for his ideas about cloud computing and device networking.

Authors' Contributions
H-CC and VF-ST contributed to the study concept and design. BZ contributed to the technology and devices implementation.
Y-HP contributed to acquisition of data. J-TH contributed to the statistical analysis. H-CC and VF-ST contributed to theinterpretation of results. J-TH and VF-ST drafted the manuscript. All authors gave final approval for the manuscript.

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Conflicts of Interest
BZ is the chief of research and development at Createcare. VF-ST is a medical consultant for Createcare.

Multimedia Appendix 1
Operating process of Bemaner.
[MOV File , 168101 KB-Multimedia Appendix 1]

Multimedia Appendix 2
Scored grades of sperm videos: Grade 0.
[MOV File , 9520 KB-Multimedia Appendix 2]

Multimedia Appendix 3
Scored grades of sperm videos: Grade 1.
[MOV File , 12746 KB-Multimedia Appendix 3]

Multimedia Appendix 4
Scored grades of sperm videos: Grade 2.
[MOV File , 12138 KB-Multimedia Appendix 4]

Multimedia Appendix 5
Scored grades of sperm videos: Grade 3.
[MOV File , 20759 KB-Multimedia Appendix 5]

Multimedia Appendix 6
Scored grades of sperm videos: Grade 4.
[MOV File , 19378 KB-Multimedia Appendix 6]

Multimedia Appendix 7
Scored grades of sperm videos: Grade 5.
[MOV File , 21784 KB-Multimedia Appendix 7]

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Abbreviations
AI: artificial intelligence
WHO: World Health Organization

Edited by G Eysenbach; submitted 18.05.20; peer-reviewed by Z Yang, F Palmieri; comments to author 30.06.20; revised version
received 11.07.20; accepted 28.10.20; published 19.11.20
Please cite as:
Tsai VFS, Zhuang B, Pong YH, Hsieh JT, Chang HC
Web- and Artificial Intelligence–Based Image Recognition For Sperm Motility Analysis: Verification StudyJMIR Med Inform 2020;8(11):e20031
URL: http://medinform.jmir.org/2020/11/e20031/
doi: 10.2196/20031
PMID: 33211025©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. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The
complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright andlicense information must be included.

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