Rapid Sperm Capture

Infertility, not being able to conceive after a year of trying for a baby, affects around one in six couples. Problems with sperm - for example low numbers of rapid swimming sperm, or poorly-formed sperm (which may have damaged DNA) contribute in around half of all cases. Therapies such as IVF, or ICSI (injection of sperm into an egg) are used to treat sperm-related problems, however they are ineffective, are very expensive and put physical and emotional strain on the couple, particularly the woman. Worryingly, using sperm with damaged DNA may contribute to miscarriage or health problems in any resulting children. Some patients would be better off continuing to try for a baby naturally, but with some lifestyle changes (stopping smoking, improving diet), or with a less invasive treatment (insemination into the womb). Other patients should be quickly moved to IVF, some will only conceive through ICSI. The difficulty with treatment decisions is that methods to determine the type and severity of sperm problems are imprecise. The main methods are manually 'counting' swimming sperm, and drying them out on a slide to examine their shape. This does not take advantage of the huge leaps in computer and camera technology made in recent years - indeed even 1980s 'Computer-Aided Semen Analysis' methods are not generally used in clinics (partly because of their unsatisfactory accuracy). Think of the technology in a typical smartphone - a high definition (possibly rapid framerate) camera, pattern recognition, and high volume data processing/storage - this is the type of 21st Century technology that needs to be brought into the fertility clinic.

We will develop a new way to examine sperm, using both rapid digital camera imaging, and computer-based pattern recognition. The aim will be to be able to automatically, accurately and repeatably examine a semen sample, collecting simultaneous data on how cells swim, and what their shape is like. The target of this technique will be to look for the 'special' few cells that have the right shape, and can swim well - so that in natural fertilisation they would be able to travel through the cervix, womb and fallopian tubes and fertilise the egg.

There will be a number of practical issues that will need to be solved. For example, do we need to make the cells fluorescent so we can see their shape better, or can we achieve our aims with a 'standard' type of microscopy? Can we work with samples at any concentration, or do we need to dilute them to recognise the cells properly? Should we use a 'micro-fluidic' chamber to separate out the swimming cells first - and should we use a high viscosity ('sticky') fluid that better represents the physical challenge sperm face in the female reproductive tract?

A key question will be how to convert the large volume of information we can measure into information that doctors and patients can make use of. We will apply a type of machine learning based on prototypes, representations of typical types of patients based on the many 'features' we can extract from rapid sperm videos. These prototypes will be progressively modified as more patients come through the system, making the model more accurate. In the long term the possibility of integrating the large volume of data through a model we can train will lead to a very powerful way to bring together clinical information nationally or even internationally.

As the system makes its way into real application, patients will receive the right treatment more quickly, saving resources, patients will have a less difficult experience of fertility treatment and will achieve success more quickly. A longer-term benefit will be by helping clinical research and toxicology - the system will provide researchers with a powerful method to test new advances in fertility treatment, for example drugs, lifestyle changes, and they will also be able to check for unintended sperm-toxic effects of other chemicals.

Infertility is highly prevalent - around one in six UK couples fail to conceive after a year of trying; birth rates in developed countries are falling. Male factors are present in around 25% of couples undergoing IVF and 70% of couples undergoing ICSI (direct injection of a single sperm into an egg). Assisted reproduction has become a routine treatment in the UK, the number of IVF cycles performed annually increasing steadily over the last 20 years, exceeding 60000/year in 2012 (HFEA data).

Success rates however remain unacceptably low, with the live birth rate per cycle being around 25%. IVF has significant financial cost, limited availability, and takes a physical and emotional toll on the couple, particularly the woman. While ICSI (direct injection of a sperm into the egg) has made treatment possible even with severe sperm motility deficiency, its expense and possible safety risks for the healthy female partner and resulting child continue to cause concern. Despite advances in computer-based motility assays and viscous penetration tests, real-life clinical diagnostics are generally restricted to visual assessments of sperm count, morphology and motility in uncontrolled viscosity microscopy slide environments. While computer-aided methods offer the possibility of rapid and repeatable screening, the counting accuracy of these methods in human patients is unsatisfactory, and moreover morphology cannot be assessed in populations of live cells.

This project will impact infertility treatment by providing a much more accurate and informative way to assess semen samples, 'Rapid Sperm Capture.' This method will combine state of the art high frame rate imaging and analysis with simultaneous motility and morphology assessment, augmented with novel microfluidic techniques to enrich the population of relevant cells under consideration. Data analysis and interpretation will be built around machine learning, which will go beyond simply outputting numbers, and will instead provide a means to progressively carry out differential diagnosis and stratification by comparing new patients to previous patients, hence supporting medical decision making.

These developments will have economic impacts by reducing unnecessary treatment - for example with over 60000 cycles performed per year, each costing several thousand pounds, if even a few percent of couples undergoing IVF could be transferred to a cheaper therapy, the cost savings to the UK would be of the order of millions of pounds per year. Alongside this there will be impact on patient well-being, by reducing the use of invasive treatment methods, directing scarce treatment resources more effectively, and achieving successful fertilisation, pregnancy and live birth in a more timely way. Detecting chromosomal structure and chromatin integrity issues (revealing ploidy problems and elevated DNA damage) through live cell morphology could also provide a means to select sperm for use in treatment - helping to reduce miscarriage and benefiting the health of the resulting child.

Another potential Impact is as a screening tool for new motility-stimulating drugs, assessing lifestyle changes/dietary supplementation, and as a tool for toxicology. At present no fertility drugs exist that directly target sperm motility, however candidate compounds exist, for example 'Omega' being investigated in Birmingham. To design and assess such drugs rationally - and conversely, possible contraceptive substances - it is valuable to have a means to understand and enhance their mechanism of action on motility. These advances would have major economic and societal benefits, saving NHS resources, benefiting the pharmaceutical industry, and assisting toxicologists in protecting the public from fertility-damaging substances.

Computer-aided semen analysis has been most widely-used in domestic animal breeding, therefore there would be significant economic impact beyond healthcare.