Advanced techniques for fertility treatment

The field of Infertility treatment is gaining maximum research to explore “other unknown causes of infertility”. New techniques and technologies are being continuously developed to ensure you have the best possible chance of a healthy pregnancy. At PSFC, we are the early adopters of these technologies by investing in equipment and skill development and we have expertise in providing services based on innovative technologies with leading evidence of maximizing outcomes.

These services can increase your likelihood of experiencing successful fertilization, implantation, conception and an overall healthy pregnancy. In addition to improving your chances of success, these enhancements can reduce your likelihood of experiencing a pregnancy with genetic complications or miscarriage. These treatments are not necessary for every fertility patient, but they are critical for healthy outcomes when our specialists recommend them for your specific situation.  While many of these essential treatments will not incur any additional costs, it is important to note that some of these treatments will have additional costs associated.

Artificial Intelligence Embryo Selection

Recently, the use of artificial intelligence (AI) has gained traction in its ability to predict clinical outcomes using routinely obtained information, such as patient attributes, medical images, and blood test results. Adopting AI within the fertility clinic could lead to a major increase in IVF success. For example, AI could potentially aid the embryologist in providing rapid, objective, and accurate assessment of gamete and embryo health. AI may also aid physicians in formulating an optimal, personalized fertility treatment plan based on patient characteristics.

Role of AI in IVF

AI will likely demonstrate utility in several facets of the IVF procedure. Below, we discuss some of the more recent literature on the use of AI in gamete and embryo selection as well as the development of a treatment.

” AI has the potential to reinvent fertility treatments. “
AI in gamete selection

Current clinical assessment of gamete health is focused on identifying early markers of quality. This includes visualisation of gametes either through direct inspection or via static images or time-lapse videos. The quality of female gametes is associated with follicle size, oocyte morphology, and cytoplasmic characteristics. In terms of sperm, morphology, concentration, and motility are known factors that are directly correlated with IVF success. However, the selection is prone to a high degree of variation between operators. AI can be transformative in this aspect. AI would remove the subjectivity of human assessment from the decision-making process, and objectively rank gametes based on quality. The use of AI for oocyte selection may be limited due to the practice of fertilising all available oocytes. Unless of course, AI was able to predict blastocyst formation or more importantly, live birth prior to fertilisation. The greatest benefit of AI may come from the selection of sperm for intracytoplasmic sperm injection (ICSI), a process currently performed by the embryologist. The development of new assessment criteria for sperm selection might arise through the use of unsupervised AI, where new markers of sperm quality are identified, such as swimming patterns, direction of motion, or difference in sperm compartments (i.e. length of head vs tail). Although more challenging, AI may compute the optimal sperm–egg combination to achieve the highest success rate or perhaps determine whether IVF or ICSI is the best fertilisation approach. Interestingly, AI has demonstrated proficiency in predicting fertility outcomes based on distinct ultrastructural details of mouse sperm – a larger sperm head compared to the midpiece is associated with improved blastocyst development. Similarly, AI has yielded high accuracy in classifying human sperm using kinetic parameters – 89.9%; as well as classifying sperm head morphology with a high concordance rate with current manual classifications (SCIAN and HuSHeM, 88 and 94%, respectively). Notably, these studies used images or videos acquired during a routine assessment. AI can, therefore, complement current clinical practices whilst offering objective gamete selection to substantiate the assessment made by embryologists.

AI in embryo selection

Morphological assessment of embryos is the most commonly used process to select embryos for transfer. This occurs through direct visualisation using a light microscope or by time-lapse imaging. Both approaches grade embryos on their ability to reach particular stages of development in a timely manner. As with gamete selection, there is a high degree of variation between operators and clinics due to the subjective nature of these assessments. Consequently, standardisation is challenging within a clinic and near-impossible between institutions. As such, morphological grading remains limited in its ability to predict live birth outcomes. AI, using routinely generated images or time-lapse videos, may objectively and accurately grade and rank embryos, thus, assisting in the decision-making process to transfer or freeze them. Further, AI may have a role in analysing data from non-invasive metabolomic and secretory profiles from the embryo during culture. Consequently, this may lead to improved culture media formulations and regimens.

AI in treatment regimen

In the IVF clinic, decision-making for an IVF cycle regimen is guided by patient age, gamete quality, medical history, and many more. This process intends to maximise the chances of pregnancy and birth of a healthy baby. From patient to patient, an IVF cycle might thus differ in stimulation protocol and mode of fertilisation (IVF vs intracytoplasmic sperm injection) as well as the potential for other procedures including assisted hatching and preimplantation genetic testing, amongst others. Planning for an IVF cycle is heavily reliant upon input from the clinician, who may prescribe a different treatment regimen based on their own clinical experience and/or clinic-to-clinic differences in training and in-house practice. AI could aid fertility practitioners in this aspect, enabling objective decision-making to optimise the treatment protocol for the best outcome. AI could also be applied to data-mine existing patient records to discover novel markers that predict pregnancy and live birth.

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Why does reproductive medicine require AI?

The rapid development of ART such as oocyte and embryo cryopreservation, assisted fertilization, preimplantation genetic testing and embryo selection technologies have greatly improved the clinical pregnancy rate in the 40 years since the birth of the first in vitro fertilization (IVF) baby even though many problems remain. The quality of embryos is the most critical factor for the success of IVF, but there is still a lack in the methods of judging the quality of the eggs, the sperm and the embryos accurately. Embryo selection methods using a single parameter or algorithm have not been identified. Therefore, it is difficult to predict the probability of a successful pregnancy for each patient and to fully understand the cause of each failure. AI-based methods in reproductive medicine may become a solution to current dilemmas. The primary driver for the development of these applications is the desire to improve the treatment and prognosis for infertility patients, using the large quantities of data provided by complex diagnostic and therapeutic modalities. AI can provide greater efficacy and efficiency in clinical activities, thereby optimizing the treatment cycle of ART.