Healthcare is one of the most obvious ways technology is improving our lives. Since the industrial revolution, life expectancy has increased by about 50 years. Of their overall lifespan, people spend an increasing number of years on education due to more and more specialized work environments. These trends and other cultural and economic factors lead to ever-later childbearing in developed countries. In Japan and Italy, for instance, the average age of first-time mothers is now over 30; in the US, it has surpassed 26. Along with other health factors, delayed childbearing has led to decreased fertility. The global fertility rate has halved over the last 50 years.
These tendencies are why fertility medicine has become a growing field over the last decades and will play a crucial role in modern healthcare systems. This growth has been driven by increasing infertility rates and demand for family planning, caused by delayed childbearing, lifestyle choices and environmental factors. Assisted reproductive technology (ART), such as in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI), have been some of the most widely adopted fertility treatment methods. However, these treatments are imperfect, and there is ample room for improvement. This is where artificial intelligence comes into play. I talked to Felicia von Reden, who just co-founded Ovom Care, an AI-first company that takes on the task of revolutionizing the fertility space.
Fertility: A (very) short overview
The fertility space is a complex field, overlapping with many other parts of healthcare. According to Felicia, three layers constitute the framework of the area: General Healthcare, Reproductive Health and Fertility Medicine.
General Healthcare: This is the foundational layer of the fertility space. It encompasses various medical disciplines that indirectly impact fertility by promoting overall physical and mental health. It includes nutrition, endocrinology, genetics, mental health, and more. All of these factors can contribute to an individual's or a couple's overall fertility health.
Reproductive Health: This is the second layer that focuses explicitly on the health and functioning of the reproductive systems in both men and women. It covers menstrual health, sexual health, pregnancy, childbirth, and menopause, among other topics. Also, it deals with preventing and treating sexually transmitted infections and conditions like endometriosis, which can directly impact fertility.
Fertility Medicine: This is the most specific and focused layer of the fertility space. It addresses issues directly related to conception and pregnancy. This involves diagnosing and treating particular fertility issues like ovulation disorders, male factor infertility, and unexplained infertility. The treatment of these issues includes medical techniques such as in vitro fertilization (IVF), intrauterine insemination (IUI), and third-party reproduction methods like surrogacy and egg donation.
In summary, the fertility space is a multilayered field within healthcare. It begins with general health and wellness practices, narrows down to the targeted area of reproductive health, and finally hones in on the specific domain of fertility medicine. Each layer is interconnected and integral to the effective functioning and outcomes of the other.
How AI can transform Fertility
During the last few years, the progress in AI has reached the field of fertility. The technology can transform the in vitro fertilization (IVF) landscape, helping improve outcomes and optimize treatment plans. As a result, we see more and more companies popping up, developing AI-based tools for IVF clinics.
One of AI's key applications in IVF is embryo selection, a critical yet challenging part of the process. Machine learning algorithms can analyze vast datasets of embryo images to predict viability more accurately than human embryologists. The automation of these processes frees up time for the staff in the lab, leading to a better quality of care. By analyzing a wide array of patient data, including medical history, genetic information, and previous treatment responses, AI can suggest the most effective strategies, such as the optimal hormonal dosage for ovarian stimulation.
Furthermore, AI has a significant role in genetic screening. For example, preimplantation genetic testing (PGT) can benefit from AI algorithms that detect chromosomal abnormalities more accurately, increasing the likelihood of a successful, healthy pregnancy. Predictive analytics, another promising application of AI, uses many factors like age, lifestyle, and genetic markers to provide a more precise prognosis, empowering doctors and patients to make informed decisions. In addition, automation of routine tasks, such as cell counting in an embryo, reduces human error and increases efficiency, allowing clinicians to focus on more complex tasks. Finally, AI plays a crucial role in research and development, helping to identify patterns in large datasets, which can lead to breakthroughs in fertility treatments.
So far, IVF is still very expensive. However, if the technological opportunities get realized, this will drive down the costs in a way that makes IVF affordable for everyone. Moreover, fertility medicine will become more precise, adding enormous value to many people's lives. As Felicia put it: "We are all becoming parents at a much later stage, and infertility is becoming a mass phenomenon. Even if we could increase the success chances of IVF by only a few percentage points, this would already prevent a tremendous amount of suffering. And I believe that much more is possible:"
The Future of IVF Clinics
However exciting the above vision is, we have yet to see AI tools good enough to lead the transformation so far. According to Felicia, there are mainly two bottlenecks that hinder progress.
The first significant bottleneck is the lack of high-quality data. The companies that develop AI tools for fertility do not create their own data and have to rely on the data from the existing clinics. The issue, however, is that most of these clinics are not sufficiently digitized. This leads to the data on the market being scarce and of bad quality. According to Felicia, if you buy a dataset, often you cannot use large parts of the data due to quality issues.
Moreover, the data does not represent the whole patient journey from end to end. As a result, innovators are left with fragments and have to rely on those to build correlations. This makes the tools that are being developed imprecise and costly compared to what is possible.
A second issue lies in the slow adoption from the side of the IVF clinics. Their business model typically involves selling IVF cycles, which hinders the adoption of better procedures and innovations in treatment. Because clinics often generate more revenue when more cycles are performed, there is a financial incentive to recommend additional cycles, even when the chances of success might be low. This model also discourages the adoption of new, more efficient techniques or personalized treatment plans that might reduce the number of cycles needed for a successful pregnancy. The focus on selling cycles is diverting resources and attention from investing in and implementing innovative procedures or approaches. The financial incentives are not aligned with the transformation.
This is why Ovom Care wants to lead the transformation by solving the problems from the root up. Felicia and her co-founders plan to build a new generation of digitized IVF clinics. The goal of Ovom Care is to first form the foundation for great tools by creating Clinics that can produce the necessary data and adopt AI-based tools. These clinics can then function as a data flywheel to build better fertility tools powered by AI. The promise of this pathway lies in an overall better quality of care through better success rates and more financial accessibility to fertility medicine and a better patient experience through improved digital support.
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Dec 20, 2019
Feb 22, 2022
