Sexual stimulation, penile erection and subsequent orgasm generally result in ejaculation. When successfully induced in a clinical environment, ejaculation provides a primary procurement source for sperm.

Two vital subcomponents concurrently responsible for ejaculation include semen emission and expulsion. Any difficulties with either or both of these functions will affect sperm quantity and quality, necessitating a change in spermprocurement strategy.

Semen production and release

Spermatozoa produced in the testicles, when mixed with accessory sex glands' fluids, becomes semen (typically referred to as an “ejaculate,” or simply as the “sample”).

The neurophysical mechanism inducing sperm emission and expulsion is the orgasm.Thehumanorgasm is typically dependent upon a feedback mechanism between direct penile stimulation and excitation of various portions of the central nervous system.

As such, orgasm is a vital component of the sperm procurement process. Difficulties with orgasm inducementmust be addressed or circumvented to facilitate any sperm retrieval regimen.

Once orgasm has been achieved, emission and expulsion should autonomously occur.

During emission, bladder neck and external urethral sphincter remain closed to contain any deposited seminal fluid. Physical closure of the bladder neck is essential for prevention of retrograde semen flow back into the bladder.

Introduction

Urology is a diverse surgical specialty that includes disorders of the urinary and male reproductive systems. As such, it includes diagnosis and treatment of cancers, urinary stones, male infertility, impotence, urinary diseases in children, neurological and anatomical disorders resulting in an inability to properly empty the bladder, trauma to the urinary and male reproductive systems, and other diseases. This diversity contributes to the challenging nature of diagnosing and treating urological disorders, and to the difficulty in modelling urological data sets.

In this chapter, we review urological diagnostic and prognostic modelling problems that we found intractable to discriminant function analysis in which neural computational modelling was superior in performance. Because of its simplicity and explicit nature, we chose discriminant function analysis (DFA) as a traditional, linear statistical approach with which we compared neural computational modelling (Duda & Hart 1973). This analysis does not prove that neural computational modelling is superior to all forms of classical data modelling; rather, we found neural computational modelling to be a useful and robust technique for a variety of diverse urological data sets.

In this chapter we also review two aspects of neural computational modelling that, while not necessarily central to modelling per se, are of substantial interest to physicians entrenched in the clinical realm. The first is feature extraction. Physicians encountering our trained and tested neurological models invariably asked which feature or features were significant to the model.