Can the Sperm Class Analyser (SCA) CASA-Mot System for Human

Sperm Motility Analysis Reduce Imprecision and Operator

Subjectivity and Improve Semen Analysis?

Chey G Dearinga, Channa N. Jayasenab, Kevin Lindsayc

aEastern Institute of Technology, Taradale Campus, Hawkes Bay, New Zealand, 4112,

cdearing@eit.ac.nz.

bAndrology Laboratory, Hammersmith Hospital, Imperial College NHS Trust, London,

United Kingdom, W120HS.

uUnaffiliated, Cornwall, UK.

Semen analysis is considered mandatory for suspected infertility in men though

its clinical value has recently become questionable. Sperm motility is an

essential parameter for semen analysis, though is limited by high measurement

uncertainty, which includes operator subjectivity. Computer-Assisted Sperm

Analysis (CASA) is recognised as able to reduce measurement uncertainty

compared with manual semen analysis. The objective of this study was to gather

the evidence to determine whether the Sperm Class Analyser (SCA) CASA-Mot

system could reduce specific components of sperm motility measurement

uncertainty compared with the WHO manual method in a routine diagnostic

single-laboratory setting. The criteria examined included operator subjectivity,

precision, accuracy against internal and external quality standards, and a pilot

sub-study examining the potential to predict an IVF fertilisation rate. Compared

with the manual WHO method on 4000 semen samples, SCA reduces but does

not completely eliminate operator subjectivity. This work suggests that SCA and

CASA-Mot are useful tools for well-trained staff that allow rapid, high-number

sperm motility categorisation with less analytical variance than the manual

equivalent. Our initial data on a pilot sub-study suggest that SCA motility may

have superior predictive potential compared with the WHO manual method for

predicating IVF fertilisation.

Keywords: SA, CASA, CASA-Mot, sperm, motility

Introduction

Semen Analysis (SA) is mandatory in the investigation of suspected infertility or

subfertility in men [1]. In addition to infertility, SA may provide stratification of the

probability of natural conception [2, 3], and be used as an aid for the selection of

appropriate Assisted Reproductive Technology (ART) [4, 5]. SA parameters vary

depending on clinical requirements, available technology, expertise and afforded

timeframes. The fundamental parameters are semen volume, sperm count, sperm

motility (SM) and morphology. The clinical usefulness of SA is limited due to large

within‐ and between‐subject biological variation [6] and the influences of a growing

number of environmental, lifestyle and socioeconomic factors [7-9]. SA is also hindered

by substantial analytical variance [10, 11]. The World Health Organisation (WHO),

with regular updated editions of its SA manual, has attempted to address analytical

variance concerns through promoting standardisation. However, evidence suggests that

this has not been universally successful [12-16]. SA is thus associated with a high level

of measurement uncertainty [17] which limits it’s clinical value.

SM may be considered to be a particularly limited analysis as it suffers from

both operator subjectivity and a lack of any traceable standard [18]. Also, SM is limited

in that many sperm are simply a ‘redundant fraction’ because very few post-coital

sperm reach the site of fertilisation [19, 20]. However, the few sperm that do reach the

site of fertilisation are aided by means of a motile flagella [20]. Because of this, it is

generally accepted that a high proportion of sperm that are immotile or exhibiting poor

motility will adversely affect male fertility [21]. SM is thus, considered an essential

parameter of SA in spite of its limitations. Historically, SM has been categorised into

four grades of motility based on velocity [22]. However, in 2010 the WHO

amalgamated two grades, creating a simpler three grade system [23] in response to

operator difficulties in accurate classification of sperm velocity [24]. This

reclassification removed velocity as a measure, and was not adopted universally [25]. It

has proved highly controversial [26] and may have even decreased the clinical value of

SA [17]. Certainly evidence supports that sperm velocity is a useful variable [27-30]. A

solution to high analytical variance and operator difficulties in accurate classification of

sperm velocity is to replace manual elements of analysis with automation. Development

of computer assisted sperm analysis (CASA) has resulted in systems that are capable of

analysing vast numbers of sperm in short time frames [31, 32]. However, in spite of the

enormous potential of such systems, they remain largely underutilised in clinical

diagnostic laboratories [32].

The objective of this study was to compare the SCA CASA-mot system with the

WHO method for SM in a single laboratory setting. While a full validation of SCA

CASA-Mot was beyond the scope of this study, we wanted to examine operator

subjectivity, precision, and performance against external and internal standards. In

addition, we sort to perform a pilot sub-study to compare SCA CASA-Mot with WHO

SM for predicting IVF fertilisation.

Methods

Study Population and Samples

A total of 4422 men attending the Andrology Laboratory (Andrology

Laboratory, Hammersmith Hospital, Imperial College NHS Trust, London, UK) for SA

were included in this study. Azoospermia cases were excluded for all experiments.

Asthenozoospermia cases were excluded for all experiments with the exception of

experiment two.

Samples were produced by masturbation into non-toxic plastic containers at

room temperature (23 0C ± 3 0C) in private collection rooms adjacent to the laboratory

after 2–8 days abstinence. Samples were analysed after liquefaction (30–60 min) at

room temperature. After mixing, a 5 µm aliquot was transferred with a glass capillary to

a Leja 20µm chamber (20µm Leja slides, Leja; Gynotec Malden, Nieuw Vennep, The

Netherlands. After allowing flow within the specimen to cease, samples were analysed

on a heated (37 ± 0.10C) microscope stage.

For the IVF fertilisation pilot sub study 55 cases were included after exclusion

of men with female partners receiving IUI (9 cases), ICSI (73 cases), use of

frozen/thawed sperm (9 cases) and significant SCA sperm tracking error (one case).

Stimulation method was not examined as a factor but included GnRH antagonist (25

cases) and GnRH analogue (30 cases).

SCA Spermatozoa Motility

The SCA (SCA version 4.1, Microptic, S.L. Viladomat, Barcelona, Spain) system used

has been validated for count [33] against the WHO recommended [34] Improved

Neubauer Haemocytometer. The microscope used with SCA is an Olympus BH-2

(Olympus, Southend-on-Sea, UK), used with an x10 phase objective with no

magnification in the trinocular head. SCA accommodates frame rates between 25-100

frames/second. Acquisition parameters for all current experiments are 25 images

acquired at an acquisition rate of 25 images per second.

WHO Spermatozoa Motility

In order to include Grade a motility in this study, WHO SM was performed using the

1999 WHO method [22] under phase contrast at 200x magnification. Operators were

four experienced (3-25 years) technicians who participated in quality control (QC) and

external quality assurance (EQA) processes in a laboratory which performs SA on 90-

225 samples per week. In the UK, SA EQA is provided by the UK National External

Quality Assessment Service (NEQAS). The Andrology Laboratory and has never been a

poor performer in the EQA scheme.

Experiment 1: Motility Comparisons

Separate aliquots from 225 patient’s samples were analysed for SM by four

trained operators using the WHO method and by one operator using SCA. WHO and

SCA operators had no view of each other’s workstation and analysed samples

contemporaneously using different chambers. Results were recorded independently and

collated at the end of the experiment.

Experiment 2: Operator Subjectivity Comparison

SM operator subjectivity was estimated by collating and analysing 10 months of

sequential patient SA results (n=4000) from our laboratory. Each operator (n=4)

provided 1000 patient SA results, 500 for SCA and WHO methods respectively.

Operator subjectivity was estimated for SM grades a, b, c, d and a+b by calculating the

probability that individual operator SA results originated from the same distribution.

Experiment 3 Precision and Internal Standard Comparisons

Digital videos (n=10) were created from single ejaculates from men (n=10)

using the SCA system. Each video comprised of 10 microscope fields of view, each of

which were 15 seconds long in duration, giving a total duration of 150 seconds per

video. In an effort to estimate realistic variance in a diagnostic laboratory, operators

(n=4) analysed videos during the same sessions as they performed routine SA on

patients samples. Videos were analysed prior to beginning SA for the session and after

each sixth patient’s sample. Videos were randomly selected for each analysis and

operators did not have access to their own or other operator’s results. Each operator

recorded a minimum of 10 repeats for each of the 10 videos.

WHO and SCA motility within-operator precision profiles were calculated from video

results and compared. Precision was calculated using coefficient of variation (CV%).

Each CV% calculation consisted of 400 sperm (200 analysed twice). CV% was

calculated for each operator and SCA for each motility grade for each the 10 videos,

giving a total of 160 within-operator comparisons per method..

Each video was also given an internal standard result. This was achieved during group

sessions were every individual spermatozoa on each video was examined. During

examination SCA motility tracks were viewed and measured on the video screen with a

string ruler calibrated against a stage micrometre. Each sperm and track was repeatedly

viewed and measured until all four operators reached consensus upon the correct WHO

grade classification.

Experiment 4: External Standard Comparison

NEQAS samples for SM are videos of neat semen prepared by the NEQAS

Reproductive Science Scheme (Reproductive Medicine, Andrology Laboratories, Saint

Mary’s Hospital, Manchester, UK). A total of 32 NEQAS videos representing 2 years

of EQA (distributions 213 to 252) were analysed for SM by one operator using the

WHO method and by SCA. NEQAS videos do not run on SCA. SCA analysis was

performed by focussing a camera lens (Penatx, TV Zoom Lens 12.5-77mm 1:1.8) on a

computer screen playing the videos at a distance of approximately two meters from the

camera.

Experiment 5: Predicting IVF Pregnancy

The WHO SM method was compared with SCA for predicting IVF fertilisation

in a blinded design by using two separate clinics. SA was performed at one clinic (The

Andrology Laboratory), while IVF treatment was performed at a separate clinic (IVF

Hammersmith, Hammersmith Hospital, London, UK). The SA was a pre-treatment

fertility workup sample used to aid ART selection. The same chamber was used for both

analyses. The WHO method was performed first followed by the SCA method with no

time delay. After exclusion (see study population above) 55 SA results from patients

who attended the Andrology Laboratory and produced a single ejaculate for fertility

workup prior to their female partners IVF treatment were collated. All collated SA

results were matched with female partners IVF outcomes provided by the IVF treatment

centre. Data provided by the IVF treatment centre precluded any other analysis.

Experiment 6: Error Assessment

Spurious motility (SCA motility signals recorded from stationary objects) was

examined by performing SCA analysis on heat treated immotile sperm. 16 samples were

prepared in chambers and heat immobilised by placing chambers on a hot plate at 60 0C.

SCA analysis was performed 160 times (10 replicates on each of 16 chambers, one per

patient).

Statistics

Distributions were tested for normality with descriptive statistics and the

D'Agostino and Pearson omnibus normality test to examine suitability for parametric

testing. Correlations were performed by Spearman’s rank correlation and regression

calculated using Deming regression. Bland-Altman plots were constructed to test for

bias. Coefficient of Variation (CV%) was calculated from a minimum of 10 replicates

for each category examined. The Wilcoxon matched pairs test was used to compare

SCA and WHO categories. Receiver Operator Characteristics curves were used to

compare WHO and SCA for IVF fertilisation prediction. Operator subjectivity was

estimated by the Kruskal–Wallis test by ranks with Dunn's Multiple Comparison Test as

a post hoc test. All data were analysed using Prism version 4.0 (GraphPad Version

4.01,San Diego, CA, USA, www.graphpad.com).

Results

Experiment 1: Motility Comparisons

On patients samples (n=220), SCA compared with the WHO method recorded lower a

grade motility (SCA median = 12, WHO median = 43, p<0.001), higher b grade (SCA

median = 11.5, WHO median = 9, p<0.001), higher c grade (SCA median = 13, WHO

median = 5, p<0.001) and higher d grade motility (SCA median = 62, WHO median =

41, p<0.001). Only a class (r=0.58 p<0.001) and d class (r=0.63, p<0.001) correlated

between methods. Bland Altman plots demonstrated proportional bias with each WHO

grade.

Experiment 2: Operator Subjectivity Comparison

The assessment of between operator variance demonstrated that SCA does not

fully eliminate between operator variance. However, variance between operators is

much reduced in comparison with the WHO method (Figure 1).

Figure 1. Operator Subjectivity for Sperm Motility WHO vs SCA

0 20 40 60 80 1000

1020304050607080 Operator WHO

Operator SCA

SCA p=0.18 (4.92)

WHO p<0.0001 (61.76 Kruskal-Wallis statistic)

Number* of significant post hoc tests

0*

4*

Percentile

Gra

de a

(%)

0 20 40 60 80 1000

10

20

30

40

50

SCA p=0.0319 (8.82)

WHO p<0.0001 (88.46) 5*

1*

Percentile

Gra

de b

(%)

0 20 40 60 80 1000

10

20

30

40

SCA p=0.0005 (17.72)

WHO p<0.0001 (57.46)

2*

3*

Percentile

Gra

de c

(%)

0 20 40 60 80 1000

20

40

60

80

100

WHO p<0.0001 (37.37)

3*

SCA p=0.0048 (12.91)1*

Percentile

Gra

de d

(%)

Figure 1. Individual operator (n=4) SM grade percentiles from SA results (n=4000) are

presented. WHO (n=2000) and SCA (n=2000) methods are compared with p values

(Kruskal-Wallis statistic) and the number* of significant post hoc tests.

Experiment 3 Precision and Internal Standard Comparisons

For within-operator variance, SCA was more precise than the WHO method

(Figure 2).

0 10 20 30 40 50 60 70 80 9005

10152025303540455055

WHOSCA

Mean Motility %

CV

%

Figure 2. WHO vs SCA precision profiles (mean and 95%CI are shown)

For all 10 videos, the WHO method overestimated motility and SCA was generally

closer to consensus results. This was most apparent for grade a motility (figure 3).

WH

OC

onse

nsus

SCA

WH

OC

onse

nsus

SCA

WH

OC

onse

nsus

SCA

WH

OC

onse

nsus

SCA

WH

OC

onse

nsus

SCA

WH

OC

onse

nsus

SCA

WH

OC

onse

nsus

SCA

WH

OC

onse

nsus

SCA

WH

OC

onse

nsus

SCA

WH

OC

onse

nsus

SCA

0

10

20

30

Video Video Video Video Video Video Video Video Video Video 1 2 3 4 5 6 7 8 9 10

WHOConsensusSCA

Gra

de a

%

Figure 3. a class motility, SCA exhibited lower variance and values closer to internal consensus.

Experiment 4: External Standard Comparison

SCA and WHO methods were similarly close to NEQAS designated values for c

and d grade motility, neither method produced results father than two standard

deviations from the NEQAS designated values. For NEQAS samples with greater than

20% grade a sperm, SCA recorded a proportionally lower number of grade a sperm

compared with the WHO method and NEQAS designated values. SCA categorised a

higher proportion of grade b sperm at these values (Figure 4).

Figure 4. Two years of SCA and WHO with NEQAS

0 10 20 30 40 50 60 70 80

0

25

50

75

100

NEQAS 2SDWHOSCA

NEQAS

NEQAS Designated value

Rap

id (

Gra

de a

)

10 20 30 40 500

25

50

75

NEQAS Designated value

Slu

ggis

h (G

rade

b)

5 10 15 20 25 30-10

0

10

20

30

40

50

NEQAS Designated value

Non

Pro

gres

sive

(G

rade

c)

10 20 30 40 500

25

50

75

NEQAS Designated value

Imm

otile

(G

rade

d)

Experiment 5: Predicting IVF Fertilisation

Female partner median and interquartile range data on this cohort were as

follows: age 36 years (31-38), number of oocytes per retrieval 9 (6-14), oocyte

fertilisation rate 67 % (50-85), cycle number 1 (1-1). Male partner median and

interquartile range data on this cohort were as follows: semen volume 3.1 mL (2.2-3.8),

count 59 x106/mL (38-79), progressive motility 50% (41-62), total motility 60% (52-

70), days of abstinence 3 (2-3).

ROC analysis using the median fertilisation rate of 67% as a binary diagnostic

threshold (28 cases positive and 27 cases negative) demonstrated that semen analysis

motility from a single pre-treatment sample is predicative of fertilisation rate at

treatment. Both progressive (p=0.027) and total motility (p=0.018) from SCA produced

significant areas under the curve. Motility the WHO method was not significant (Table

1).

Variable ROC Area 95% Confidence Interval

Lower Bound Upper Bound p

WHO Progressive Motility .594 .441 .747 .232

WHO Total Motility .558 .403 .712 .464

SCA Progressive Motility .673 .527 .820 .027

SCA Total Motility .685 .541 .829 .018

Figure 3. SCA motility was a greater discriminator of IVF success than the WHO

manual method

Experiment 6: Error Assessment

SCA erroneously recorded motility on 4.7 % (95% CI 3.8 – 5.7) of heat

immobilised sperm which correlated (p=0.02, r=-0.35) with sperm count. By WHO

classification motility was; a class motility = 0.03 % (0.0-0.7), b class motility = 1.3 %

(1.1-1.5), and c class motility = 3.1 % (2.7-3.5).

Discussion

The primary aim of this study was to compare SCA CASA-Mot with the WHO method

for SM in a single laboratory setting. We also sort to examine operator subjectivity,

precision, performance against external and internal standards. Also, as a pilot sub-

study, we compared SCA with WHO for predicting IVF fertilisation. Our evidence

suggests that SCA CASA-Mot in comparison with the WHO method; (1) decreases

operator subjectivity, (2) offers greater precision, (3) produces results that are closer in

agreement to an internal standard, and (4) produces results that are similar in agreement

to an external standard. In addition on limited preliminary evidence, SCA CASA-Mot

compared with the WHO method may offer greater diagnostic potential for appropriate

ART selection. However, SCA is not a “black box” technology and cannot be used

without specific training, both in SA and in the use of CASA-Mot.

Our results on a large dataset (n=4000) suggest substantial operator subjectivity for SM

with the WHO method. In comparison, SCA reduces but does not fully eliminate

operator subjectivity. Central tendency calculations are recognised [35] SA quality

tools, and are established methods for examining bias [36, 37]. The advantages are that

operators are blind to this quality assessment and a complete data set of actual SA

results can be examined. Additionally, uncertainties related to differences between

patient samples and quality material (Alvarez et al., 2003) and concentration levels [38]

are avoided. However, observation numbers, timeframes, changes in patient

populations, equipment, consumables or testing environments are all possible

confounding factors [35]. While seasonal effects on semen parameters have been

reported [39, 40], the combined central tendency measures for our laboratory did not

deviate with season. Other than the use of SCA, nor were there changes in equipment or

consumables. The operators involved in the current study were trained, experienced

staff who participated in robust quality assurance practices and the laboratory has never

been a poor performer in the EQA scheme. CASA-Mot appears more consistent than the

manual equivalent though operator variation remains a significant source of error, which

is similar to previous findings [41]. Moreover, we believe that this may not be apparent

in quality systems without similar large dataset analysis. We suggest that CASA-Mot is

preferential to the WHO method for controlling SM operator subjectivity.

This current study used operator consensus values as a quasi internal standards

as SM lacks any true traceable standard [18]. These internal standards, where a group of

trained experienced operators achieved WHO classification consensus for each

spermatozoa, were closer to SCA than WHO results. These findings are in accordance

with previous studies highlighted that sperm motility is subjective [24, 42] and easily

overestimated [24, 43]. Most notably from our results, only SCA was able to distinguish

a grade motility with reliability, which is similar to findings with other CASA-Mot

systems [24]. This is important as evidence clearly supports that sperm velocity is a

useful variable [27-29]. Considering recent reports that the loss of velocity as a SA

variable has increased clinical uncertainty [17], we believe that CASA-Mot increases

the usefulness of SA.

We used an EQA scheme as an external standard and observed SCA achieved

similar agreement as with the WHO method, though also noted SCA records

proportionally lower grade a and higher grade b motility. These differences between the

WHO and SCA methods with NEQAS designated values has striking similarity to the

differences observed when both methods are compared with our internal standard

results. Our internal standard results clearly demonstrates that the WHO method

overestimates grade a motility. We believe the NEQAS designated values are similarly

overestimated. Moreover, as we demonstrated operator subjectivity is reduced with

SCA, EQA results may improve for laboratories with multiple operators who adopt

CASA-Mot analysis. The introduction of CASA has been favoured by several authors

[43-46] and we support this view from these results.

Precision with the WHO method was comparable [47] or higher [42, 48] than

previously reported between-operator variances for sperm motility. However, unlike in

previous studies, operators were required to perform this analysis under significant time

demands in a busy working laboratory. Additionally, the assessment was not part of a

training scheme [42, 48], not part of our internal quality control practices. It is

interesting to note that the same operators when performing our internal quality control

for SM (quite separate from this study), use a CV upper limit of 10% and are routinely

well below this threshold. This is similar to results produced by well-trained operators

during training schemes [42, 48]. We believe operators using WHO methods are

capable of achieving very reproducible results, however the results of our study are an

accurate reflection of WHO method imprecision on patient samples in a busy

laboratory. In contrast, SCA proved to offer greater precision than the WHO method.

Thus, CASA-Mot is preferential in terms of precision compared with the WHO method.

It was beyond the scope of this work to attempt to provide any SCA values that

can be applied to any populations of interest. However, the results of a pilot-sub study

on IVF fertilisation prediction from motility on 55 couples comparing SCA and the

WHO method are interesting: Only the SCA produced significant ROC areas. While

this data is preliminary and indeed limited, we believe that SCA motility with decreased

operator variance and increased precision may offer greater diagnostic potential than the

WHO method. Further work on this is warranted. There are limitations from this current

study. We have not fully validated SCA, which may require plotting sperm tracks on

acetate sheets to verify velocities and examining errors where sperm collide or

encounter other particles. We used a frame rate of 25 frames s-1 and 60 frames s-1 or

faster are often preferred [49]. However, this appears to be less of a concern with the

aim of reducing operator variance [50]. Additionally, we observed when increasing the

frame rate (SCA allows users to select between 25-100 frames s -1) increasing velocity

parameters and thus decided to employ the more commonly reported 25 frames s-1

In conclusion, SCA CASA-Mot offers less operator subjectivity, greater

precision, and is closer in agreement to an internal laboratory standard than the WHO

method. SCA CASA-Mot can be used with a video based EQA scheme with

comparable results to the WHO method. We believe from limited evidence that SCA

CASA-Mot may offer greater diagnostic potential for appropriate ART selection

compared with the WHO method. Considering the continuing questions concerning the

usefulness of SA, we believe that CASA-Mot should be trialled by more laboratories

and that operator subjectivity should be a parameter specifically examined during the

trial.

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