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AI in Reproductive Medicine: Revolutionizing Assisted Reproductive Technology


Can the integration of AI in reproductive medicine develop assisted reproductive technology?

There is a rapid growth in technology, especially artificial intelligence over the past few years. AI has gone through various stages from the stage of experimental to the stage of implementation in various fields and medicine is not an exception. Can the integration of AI in reproductive medicine revolutionize assisted reproductive technology?

AI and ML are rapidly changing the practice of medicine across various disciplines. AI is proving to be increasingly applicable to healthcare. Major instances have already been made in disciplines where pattern recognition and classification are integral to the practice such as dermatology, radiology, and pathology.  The field of reproduction science has been slow to track the opportunities in AI. Despite this, multiple artificial intelligence solutions have been used to enhance the performance of assisted reproductive technology (ART).


Assisted Reproductive Technology and AI

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

AI-based solutions in reproductive medicine may become a solution to such existing uncertainty. The main focus for these developments is to enhance the treatment and prognosis for infertility patients, using the large quantities of data produced by complex diagnostic and therapeutic modal quality. AI has the potential to provide greater efficiency and success in clinical activities, thereby optimizing the treatment cycle of ART.


How can AI be applied to the practice of ART?

Researchers have been successful in experimenting with AI to identify and distinguish the most feasible oocytes and embryos. On the basis of a certain set of criteria which are often developed from personal experience rather than evidence-based sources, embryologists select oocytes and embryos. To systemize, formalize and enhance the selection process, researchers designed and tested an AI system on two data sets of 269 oocytes and 269 relative embryos from 104 women. It was found that the AI system could successfully recognize and determine oocyte and embryo quality by using the information it had learned through previous training.

There are two biggest drawbacks of assisted reproductive technology, one is the lack of reliability of results and another one is high cost. Deploying AI in the field of assisted reproductive technology can help establish a functional, quantifiable, and dependable prediction model. In turn, this will increase the dependability, the cost-effectiveness of fertility services even as providing individual-oriented and accurate treatment.

While the worldwide use of electronic medical records will help pave the way for data mining and AI applications, the high variability of stimulation and embryology techniques across laboratories is a major barrier to ML. While newer AI algorithms can moderately compensate for missing data, all ML systems work best when they can learn on vast, complete, codified data. Until reproductive specialists acquire a common clinical language and standard data acquisition criteria, data mining cannot arise to the degree required for off-the-shelf ART applications. Thus, the near-term will likely be an iterative process. AI can begin to learn from partial, varied data and provide limited insights. Reproductive specialists can begin to standardize their systems as collective knowledge grows. Comprehensive note-taking, detailed outcomes reporting, and routine collection of high-quality imaging can accelerate this innovation. As such, all fertility specialists can take part in the AI revolution in ART.

Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. There are high prospects and future directions in the context of reproductive medicine.

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