AI in Reproductive Medicine: Revolutionizing Assisted Reproductive Technology


Can the integration of AI in reproductive medicine develop assisted reproduction technologies?

There has been rapid growth in technology, especially artificial intelligence, in recent years. AI has gone through different stages from experimental to implementation in various fields and medicine is no exception. Can the integration of AI in reproductive medicine revolutionize assisted reproduction technologies?

AI and ML are rapidly changing the practice of medicine in various disciplines. AI is proving increasingly applicable to healthcare. Major examples have already been made in disciplines where shape recognition and classification are an integral part of practice, such as dermatology, radiology and pathology. The field of reproductive science has been slow to keep up with the opportunities of AI. Despite this, several artificial intelligence solutions have been used to improve the performance of assisted reproduction technologies (ART).

Assisted reproductive technology and AI

There has been rapid development in ART such as cryopreservation of oocytes and embryos, assisted fertilization, embryo selection technologies and preimplantation genetic testing. All of these practices have greatly improved the clinical pregnancy rate over the past 40 years. The most critical factor for the success of IVF is identifying the quality of the embryos, but there is still a lack of methods to thoroughly determine the quality of eggs, sperm and embryos. Selection of embryos using a single parameter or algorithm was not recognized. Therefore, it is difficult to assume the possibility of a successful pregnancy for each patient and fully recognize the cause of each failure.

AI-based solutions in reproductive medicine may become a solution to this existing uncertainty. The main objective of these developments is to improve the treatment and prognosis of infertile patients, using the large amounts of data produced by the complex diagnostic and therapeutic modal quality. AI has the potential to improve the efficiency and success of clinical activities, thereby optimizing the ART treatment cycle.

How can AI be applied to the practice of ART?

Researchers successfully experimented with AI to identify and distinguish between the most feasible oocytes and embryos. Based on a certain set of criteria that are often developed from personal experience rather than factual sources, embryologists select oocytes and embryos. To systematize, formalize and improve the selection process, the researchers designed and tested an AI system on two datasets of 269 oocytes and 269 relative embryos from 104 women. It was found that the AI ​​system could successfully recognize and determine the quality of oocytes and embryos using the information it had learned in the previous training.

There are two biggest drawbacks to assisted reproduction technology, one is the unreliable results and the other is the high cost. The deployment of AI in the field of assisted reproduction technologies can help establish a functional, quantifiable and reliable prediction model. In turn, this will increase the reliability and cost-effectiveness of fertility services while providing accurate and individual-oriented treatment.

While the global use of electronic medical records will help pave the way for data mining and AI applications, the great variability in stimulation and embryology techniques between laboratories is a major obstacle to ML. While new AI algorithms can moderately compensate for missing data, all ML systems perform best when they can learn from large, comprehensive, and coded data. Until reproductive specialists acquire a common clinical language and standard data acquisition criteria, data mining will not be able to reach the degree required for out-of-the-box ART applications. So the short term is likely to be an iterative process. AI can begin to learn from partial and varied data and provide limited information. Reproductive specialists can begin to standardize their systems as collective knowledge grows. Comprehensive note-taking, detailed results reporting, and regular collection of high-quality images can accelerate this innovation. Thus, all fertility specialists can participate in the AI ​​revolution in ART.

Despite various challenges, the integration of AI and reproductive medicine is sure to provide essential direction for medical development in the future. There are great perspectives and future directions in the context of reproductive medicine.

Share this article



Leave A Reply