Can the Sperm Class Analyser (SCA) CASA-Mot system for human sperm motility analysis reduce imprecision and operator subjectivity and improve semen analysis?

Abstract Semen analysis (SA) is considered mandatory for suspected male infertility although its clinical value has recently become questionable. Sperm motility is an essential parameter for SA, but is limited by high measurement uncertainty, which includes operator subjectivity. Computer-assisted sperm analysis (CASA) can reduce measurement uncertainty compared with manual SA. The objective of this study was to determine whether the Sperm Class Analyser (SCA) CASA-Mot system could reduce specific components of sperm motility measurement uncertainty compared with the World Health Organization (WHO) manual method in a single laboratory undertaking routine diagnostic SA. The study examined: (i) operator subjectivity; (ii) precision, (iii) accuracy against internal and external quality standards; and (iv) a pilot sub-study examining the potential to predict an IVF fertilisation rate. Compared with the manual WHO method of SA on 4000 semen samples, SCA reduces but does not completely eliminate operator subjectivity. Study SCA and CASA-Mot are useful tools for well-trained staff that allow rapid, high-number sperm motility categorization with less analytical variance than the manual equivalent. Our initial data suggest that SCA motility may have superior predictive potential compared with the WHO manual method for predicating IVF fertilization.

[1]  N. Cherry,et al.  Modifiable and non-modifiable risk factors for poor sperm morphology. , 2014, Human reproduction.

[2]  Aleksander Giwercman,et al.  Environmental factors and testicular function. , 2011, Best practice & research. Clinical endocrinology & metabolism.

[3]  T. Cooper,et al.  Computer-aided evaluation of assessment of "grade a" spermatozoa by experienced technicians. , 2006, Fertility and sterility.

[4]  Tony Pridmore,et al.  Validation of a novel computer-assisted sperm analysis (CASA) system using multitarget-tracking algorithms. , 2010, Fertility and sterility.

[5]  Tina Kold Jensen,et al.  Relation between semen quality and fertility: a population-based study of 430 first-pregnancy planners , 1998, The Lancet.

[6]  Diarmaid H Douglas-Hamilton,et al.  Particle distribution in low-volume capillary-loaded chambers. , 2005, Journal of andrology.

[7]  A. Pacey,et al.  Is quality assurance in semen analysis still really necessary? A view from the andrology laboratory. , 2006, Human reproduction.

[8]  J. Kirkman-Brown,et al.  CASA: tracking the past and plotting the future. , 2018, Reproduction, fertility, and development.

[9]  S. Ho,et al.  Environmental factors, epigenetics, and developmental origin of reproductive disorders. , 2017, Reproductive toxicology.

[10]  G. Centola,et al.  Seasonal variations and age-related changes in human sperm count, motility, motion parameters, morphology, and white blood cell concentration. , 1999, Fertility and sterility.

[11]  D. Smith,et al.  Sperm motility: is viscosity fundamental to progress? , 2011, Molecular human reproduction.

[12]  [Laboratory manual of the WHO for the examination of human semen and sperm-cervical mucus interaction]. , 2001, Annali dell'Istituto superiore di sanita.

[13]  N. Keiding,et al.  Time to pregnancy and semen parameters: a cross-sectional study among fertile couples from four European cities. , 2002, Human reproduction.

[14]  E. Nieschlag,et al.  Semen analysis and external quality control schemes for semen analysis need global standardization. , 2002, International journal of andrology.

[15]  Chey G Dearing,et al.  Validation of the sperm class analyser CASA system for sperm counting in a busy diagnostic semen analysis laboratory , 2014, Human fertility.

[16]  David Mortimer,et al.  The future of computer-aided sperm analysis , 2015, Asian journal of andrology.

[17]  G. B. Buck Louis,et al.  Male Reproductive Disorders and Fertility Trends: Influences of Environment and Genetic Susceptibility. , 2016, Physiological reviews.

[18]  P. Bossuyt,et al.  Performance of the postwash total motile sperm count as a predictor of pregnancy at the time of intrauterine insemination: a meta-analysis. , 2004, Fertility and sterility.

[19]  A. Pacey,et al.  Sperm transport in the female reproductive tract. , 2006, Human reproduction update.

[20]  W Holt,et al.  Reproducibility of computer-aided semen analysis: comparison of five different systems used in a practical workshop. , 1994, Fertility and sterility.

[21]  G. Clarke,et al.  Automated semen analysis: 'zona pellucida preferred' sperm morphometry and straight-line velocity are related to pregnancy rate in subfertile couples. , 2003, Human reproduction.

[22]  N. Cherry,et al.  Occupation exposures and sperm morphology: a case-referent analysis of a multi-centre study , 2014, Occupational and Environmental Medicine.

[23]  C. Lombard,et al.  Predictive value of normal sperm morphology in intrauterine insemination (IUI): a structured literature review. , 2001, Human reproduction update.

[24]  J P Bonde,et al.  Computer-assisted semen analysis parameters as predictors for fertility of men from the general population. The Danish First Pregnancy Planner Study Team. , 2000, Human reproduction.

[25]  A Spira,et al.  Intra- and inter-individual variability in human sperm concentration, motility and vitality assessment during a workshop involving ten laboratories. , 2000, Human reproduction.

[26]  E. Nieschlag,et al.  Internal quality control of semen analysis. , 1992, Fertility and sterility.

[27]  Regina M Turner,et al.  Moving to the beat: a review of mammalian sperm motility regulation. , 2006, Reproduction, fertility, and development.

[28]  Organización Mundial de la Salud WHO laboratory manual for the examination and processing of human semen , 2010 .

[29]  J. Yániz,et al.  CASA-Mot technology: how results are affected by the frame rate and counting chamber. , 2018, Reproduction, fertility, and development.

[30]  E. Nieschlag,et al.  Bias to routine semen analysis by uncontrolled changes in laboratory environment--detection by long-term sampling of monthly means for quality control. , 1989, International journal of andrology.

[31]  G. van der Horst,et al.  Current perspectives of CASA applications in diverse mammalian spermatozoa. , 2018, Reproduction, fertility, and development.

[32]  S. Swan,et al.  Quality control of laboratory methods for semen evaluation in a multicenter research study. , 2004, Journal of andrology.

[33]  Tomlinson Mj,et al.  Association of Biomedical Andrologists – Laboratory Andrology Guidelines for Good Practice Version 3 – 2012 , 2012 .

[34]  D. Mortimer,et al.  ESHRE special interest group for andrology basic semen analysis course: a continued focus on accuracy, quality, efficiency and clinical relevance. , 2011, Human reproduction.

[35]  M. Tomlinson Is your andrology service up to scratch? , 2010, Human fertility.

[36]  R. Tiseo,et al.  Seasonal variation of human semen parameters: A retrospective study in Italy , 2015, Chronobiology international.

[37]  J. Castilla,et al.  Biological variation of seminal parameters in healthy subjects. , 2003, Human reproduction.

[38]  M. Tomlinson,et al.  Uncertainty of measurement and clinical value of semen analysis: has standardisation through professional guidelines helped or hindered progress? , 2016, Andrology.

[39]  I D Cooke,et al.  Prognostic significance of computerized motility analysis for in vivo fertility. , 1993, Fertility and sterility.

[40]  M. Tomlinson,et al.  CASA in the medical laboratory: CASA in diagnostic andrology and assisted conception. , 2018, Reproduction, fertility, and development.