How the SQA Calculates Multiple Parameters

When moving sperm cells are placed in the path of a narrow beam of light, they cause changes that can be detected by a sensitive phototransistor; that is the single phenomenon measured by the electro-optical technology in the Sperm Quality Analyzer (SQA). The sperm cells being analyzed are held in that optical path in a transparent capillary of precise and standardized internal dimensions.

A very narrow observation window (diameter of 70µ) is created by a fiber optic channel to a calibrated phototransistor. As the cells move across that 70µ channel they cause changes in the transmissivity of the light path. Those small light changes are in the analog domain, and are converted to digital data for processing by the SQA's internal computer. That computer uses a proprietary algorithm and processing protocol to generate all of the output parameters from its optical observation. The information processed by the SQA is expressed in the form of six parameters:

1.) Sperm Motility Index (SMI)

An internal "machine language" datum used by the SQA's computer to describe the detected light changes. This datum is the basis for other parameters. It is also used by some evaluators and physicians as a single term to define impregnation potential.

2.) Functional Sperm Concentration (FSC)

A number that defines the concentration in millions per ml of live, functional sperm cells. Displayed as millions per milliliter.

3.) Motile Sperm Concentration (MSC)

A number that defines the concentration in millions per ml of motile sperm cells. Displayed as millions per milliliter.

4.) Total Cell Concentration

One of the World Health Organization parameters, reflecting the total number of cells, live or dead, per milliliter.

5.) Percent Normal Motility

Another WHO parameter, reflecting the percentage of cells with normal motility.

6.) Percent Normal Morphology

The third of the WHO parameters: the percentage of cells with normal morphology.

Upon initial exposure to the SQA, the question asked by many laboratorians is, "How can the instrument derive so much information from observation of one series of events?"

The answer is based upon statistical correlations developed over many years' experience with the SQA. During more than.4,M comparisons between the SQA and research microscopy, including Computer-Assisted Sperm Analysis (CASA), it was determined that highly reliable correlations exist between the machine language datum (SMI) and other parameters.

The first-generation SQA generated only the SMI, and provided a pull-out printed table that permitted the technologist to extract correlations between that datum and the WHO parameters. The second generation, SQA Model 11, displayed the SMI and also calculated the FSC, which was determined statistically based upon those trials. The FSC was proposed as the single parameter that most accurately defined the impregnation potential of a sample, and is achieving growing acceptance, but the fertility industry continues to depend upon the three WHO parameters. The current SQA (Model IIC-P) has an expanded internal database plus improved control firmware that allows front-panel display of all the key parameters.

In addition to statistical realities, upon which most medical technologies are based, there is a logical answer to the question. A "healthy" specimen will be generally healthy in all aspects. It is highly unlikely that a specimen might demonstrate high motile cell concentration (high SMI), with low concentration of either total cells or low concentration of morphologically normal cells. Further, it may not be important because an adequate concentration of normally motile cells is a successful predictor of impregnation potential.

A sample cm be deemed sub fertile due to insufficient cells (live or dead), insufficient live/motile cells, insufficient morphologically effective/normal cells, absence of the correct acrosomal structure, faulty sperm-egg binding mechanism, or defective biochemistry. A sample with all dead cells is absolutely infertile regardless of the total concentration of cells, and that defect is directly reflected by motility data. A sample with a high concentration of morphologically defective cells (i.e. no tail, multiple tails, multiple heads, etc.) is relatively infertile, and those defect are also directly reflected by motility data.

Of the characteristics that differentiate a "normal" sample from a "sub fertile" one, most andrologists agree that the MSC (Motile Cell Concentration) is a single definitive factor. A good concentration of live and motile cells is the first and most observable result of successor spermatogenesis.