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.
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