Fellowship in Reproductive Medicine: AI is Reproductive Medicine

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I was thinking if this was the scenario some 10 years back, people would have thought AI in reproductive medicine means artificial insemination in medicine. But we have come a long way. IVF is a very young science, but it is the fastest growing reproductive science medical faculty. And within 40 years, it has become a $40 billion industry now. So it's a huge market for AI. I would like to focus on those areas where AI is going to play a pivotal role and decisive role in reproductive medicine and what the IVF in future would look like.  

Artificial intelligence is a strong technological way of providing the ability for a machine to perform the human brain function, like perceiving, reasoning, learning, and interacting. Broadly defined, AI here refers to machine making human decisions and tasks. And machine learning is a subset of AI technology which uses statistical method to draw a conclusion or prediction model.

Take care of the time. Because AI is zero without data. So, as data accessibility improves, insights gained may lead to decision support tools and could guide the doctor or the patient whether to continue the IVF cycle or to cancel it.

Deep learning is now a different subset of machine learning. As a subset of artificial intelligence, it enables computers to identify patterns in large, complicated data sets. Large amounts of data are too much for even the CPU to handle. A GPU is necessary. After that, it forecasts.

Why ART?

You can see limited success. We are stuck around 30 percent.

·       The LIBOR threat. Reliance on human expertise

·       Lack of personalisation

High       -cost ART

·       Invasive procedure.

And there are some ethical concerns. And with significant technological advancement, the success rate of IVF has notably increased. But implantation rate without PGT, it is around 50 percent. And with PGT it is around 60 percent. But as far as LIBOR threat is concerned, it's typically hovering around 30, 32 percent, indicating the challenges that persist despite advancement in IVF technology.

Why ART is required?

Fellowship in Reproductive Medicine in India is a complex, multi-phase process that uses a variety of resources, but also has drawbacks, including high inter- and intra-observer variability and labor and time requirements. These difficulties affect ART's efficiency and reproducibility. That issue can be resolved by AI. AI could reduce the amount of effort that embryologists and physicians do.

AI technologies are fast and uniform across all IVF labs. AI lowers the possibility of human error while also ensuring best practices and outcomes. You can now observe how we use reproductive medicine.

We obtain the information from hospital data and computerized medical records. Next are natural language processing and machine learning. Additionally, we employ reproductive medicine in research, experimentation, and clinical practice.

In clinical practice, we are using it for sperm cells, quality detection, oocyte, embryo, then cost effectiveness also. With machine learning, we are using only supervised, mainly supervised and unsupervised.

Why AI in ART? With rapid pace of computer and genomic science, the future of reproductive science is likely to be a personalized digital fingerprint or digital embedded in patient's medical records. With regards to pharmacogenomics, the genetic heterogeneity between each IVF patient is an opportunity to tailor IVF treatment specifically for that individual because we'll have the DNA fingerprinting.

For example, FSH receptor polymorphism, we know there are three varieties. Someone needs more dosage of FSH, but in some cases it requires less.

So that targeted treatment we can do. Startup companies in Israel, such as Fertility, Embryonics, and Alive in California are developing AI systems aimed at clinics which can integrate IVF workflow and offer end-to-end optimisation and cost savings balanced with the promise of personalization of the treatment. We are striving for a personalized medicine.

Again, workflow optimisation. If someone comes to an IVF lab, if some five, six cases are going on, in one day, you see that the sites are coming here, then some consultant has to do some embryo transfer, then some thawing has to be done. It's really a powerhouse.

How to optimize the whole work schedule?

·       It optimizes the scheduling of the predicting peak times, when exactly it will be busy. Accordingly, you can arrange your staffs. In AI, we start with outpatient. We'll be using AI in outpatient and before. The advances in natural language processing may lead to, in the future, to the analysis of the clinical notes and ability to prepare reports, engage in conversational AI chatbot for common questions.

·       I mean, there are lots of questions they ask, and that can be solved. Some, because they get a lot of information and disinformation from internet, so just to confirm that they will give a call. So, AI will solve that problem .

·       For the clinician, time will be much less. And in the same time, she or he will be able to see more patients. AI chatbot responses may provide answers to simple questions that would reduce the time requirement for the clinic staff, make the clinic available 24 hours a day.

·       They are very insecure, infertile couples. They can phone even at 12 o'clock. But consultant will never pick up. So AI can solve that problem. And they are satisfied that clinic is available 24 hours a day. AI feedback and mood tracking, very much defaulted about the emotional part.

·       Capabilities can be captured in real time. AI deep learning models have been shown to sense and respond to emotion. There will be social robo.

·       They will find out who is depressed, sitting in OPD, or who is crying. They will solve the problem.

·       Whether they have the need in one-to-one counselling or group counselling, that will be solved by AI. And automatic speech recognition and real-time language translation with capacity for speech to text translation may reduce language barrier. If any patient from Afghan or Arabic country, you don't need a translator.

Now, we have recruited patient. Now we'll start the IVF. How much of gonadotropin we'll be using.

Normally what we're doing, we take care of the age BMI, AFC, means answer follicle count and image level. And it's 150 to 450 units. However, the potential for follicle recruitment decreases after about 8 days. Suppose we find after 7-8 days follicles are not growing, then even after pumping in more gonadotropin, it's not going to respond. So the critical nature of the starting FSH dose is very important. AI is going to guide us there.

Several machine learning models harnessing historical clinical data are being advanced to streamline and selection of the initial FSH dose for IVF. Providing a standardized framework to enhance the personalization of treatment protocol, mitigating the variability. Sometimes there will be suboptimal response.

Sometimes there will be over response. So AI will take care of that. Exactly what amount of gonadotropin we have to start.

This machine learning model by Fenton et al, the personalized dose response profile using variables. They have taken care of age, BMI, AMH, and answer follicle count. Could indeed customize the starting dose to enhance IVF outcomes and reduce the overall gonadotropin consumption by 195 units with 1.5 more M to mature sites.

One way reducing the dose and at the same time we are getting more mature sites. Showing the potential of AI to personalize and improve fertility treatment protocol. It's not only the starting dose.

Another business effort of Padma Shri Prof. Dr. Kamini A. Rao is Medline Academics, which focuses on educating people in the field of reproductive medicine. The Fellowship in Reproductive Medicine is this institution's most sought-after course. This institution's hybrid training approach, in which all theory is taught online and students participate in the simulations in person in our Bangalore location, is its most distinctive characteristic. Everyone can benefit from the hybrid learning style.

Dr. Kamini Rao Hospitals, the top IVF treatment center in Bangalore , has been a center of excellence for many years and is carrying on the tradition. In addition to assisting couples in starting their own small children, this institution exposes students enrolled in Indian embryology fellowship programs to a variety of ART cases. From menarche to menopause and beyond, we also provide comprehensive healthcare for women. Under the direction of Padma Shri Prof. Dr. Kamini A. Rao, students enrolled in prestigious fellowship programs are finishing their clinical attachment at Dr. Kamini Rao Hospitals.