The application of computer-assisted semen analysis (CASA) technology to optimise semen evaluation. A review
A. Valverde 1, 2  
,   V. Barquero 2
,   C. Soler 3
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Costa Rica Institute of Technology, School of Agronomy, San Carlos Local Campus 223-21001, Alajuela, Costa Rica
Costa Rica Institute of Technology. School of Agronomy, San Carlos Local Campus, Costa Rica Institute of Technology, 223-21001, Alajuela, Costa Rica.
University of Valencia. Department of Cellular Biology, Functional Biology and Physical Anthropology, Campus Burjassot, C/Dr. Moliner, 50, 46100, Paterna, Spain.
A. Valverde   

Costa Rica Institute of Technology, School of Agronomy, San Carlos Local Campus 223-21001, Alajuela, Costa Rica
Publication date: 2020-09-30
J. Anim. Feed Sci. 2020;29(3):189–198
Currently, artificial insemination (AI) is the most applied technique for assisted reproduction in the livestock industry. The traditional analysis of seminal quality includes the evaluation of concentration and motility (total and progressive), the parameters on which the number of doses producible from an ejaculate is calculated. The introduction of automated sperm evaluation systems based on computer-assisted semen analysis (CASA) represented a revolution in the production of seminal doses and the knowledge in reproductive biology; however, this technology has been generally implemented without a critical analysis of its limitations and dependence on several methodological factors. In addition to the use of CASA technology, the estimation of kinematics and morphometry have been incorporated as novel parameters for sensitivity and reproducibility. AI has facilitated the rapid and universal dissemination of genetic material from a relatively small number of males. The CASA systems allow the analysis of a large number of sperm cells in a short time, thereby providing an array of quantitative data on kinematics and head sperm morphometry, thus making it possible to optimise the reliability of seminal analyses.
This research was founded by the grant (No FI-097B-14) from the Consejo Nacional para Investigaciones Científicas y Tecnológicas (CONICIT) and Ministerio de Ciencia, Tecnología y Telecomunicaciones (MICITT), Costa Rica. The authors would like to thank the Vice-Chancellor’s office of Research and Extension (Vicerrectoría de Investigación y Extensión) at the Costa Rica Institute of Technology (Project 5402-2151-1013).
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