News | OVUM: Artificial Intelligence Will Lead a New Era of IVF Treatment
Despite continued advances in infertility treatment worldwide, in vitro fertilization (IVF) success rates remain relatively low and innovation has been slow. OVUM, a leading reproductive and fertility health brand, recently highlighted that artificial intelligence (AI) could transform IVF, improving treatment quality and success rates.
Current IVF outcomes and challenges
According to the latest Human Fertilisation and Embryology Authority (HFEA) statistics, the current live birth rate per embryo transferred is 25%, while rates for patients aged 35-37 and 38-39 are both 19%. These figures underscore the need for innovation in IVF. The global IVF success rate is about 30%, prompting extensive research into ways to improve outcomes. AI and machine learning are rapidly entering IVF research and clinical application as possible solutions.
Potential applications of AI in IVF
Although the process of fertilizing eggs and transferring embryos to the uterus is well established, success rates vary widely among clinics, showing room for improvement. OVUM asks whether AI can reduce these differences and raise IVF success rates.
AI—especially algorithms based on big-data analysis and machine learning—can automate parts of clinicians' or embryologists' decision-making and potentially improve outcomes. AI can process and classify large datasets, including data from previous IVF cycles, to support personalized treatment plans and identify embryos with the greatest transfer potential, a critical part of IVF success.
How AI may improve embryo selection
Embryo selection is one of the most closely watched applications of AI in IVF and may be the first to achieve widespread use. Embryologists currently select embryos manually based on visual assessment and chromosome testing. Differences in training, clinic practices, and grading methods can introduce bias and error. OVUM's fertility specialists say AI could use pattern recognition and reference datasets to overcome these limitations and recommend embryos most likely to result in pregnancy.
AI and personalized treatment
Beyond embryo selection, AI has substantial potential to create personalized treatment plans. IVF protocols often depend heavily on individualized trial and error, and outcomes vary widely among patients. By analyzing patient characteristics against large datasets, AI may help doctors identify optimal protocols and reduce the emotional and financial burden of repeated attempts.
Challenges and outlook
OVUM founder Jenny Wordsworth, a member of the British Fertility Society, said several issues must be considered before AI is fully adopted in fertility care: “Relying only on high-quality randomized controlled trials (RCTs) to validate AI in IVF may slow progress, because the algorithm may already be outdated by the time an RCT is published. We should explore other validation methods that reflect AI's unique role as a clinical decision-support tool.”
Regulators such as the HFEA also play a critical role in evaluating new technology, including AI tools for embryo selection. Although RCTs remain important, the HFEA's proposed sandbox approach could accelerate innovation by allowing a defined testing period followed by evaluation of real-world results.
Wordsworth added: “AI will gradually take over some traditional embryology tasks, such as measuring follicles or counting embryo cells, but it will not replace professionals. Clinicians need to understand the technology, and education and time will help build trust.”
Future directions
As data sharing and processing improve, AI's potential in fertility treatment will become clearer. More than three million women undergo IVF worldwide each year. Accumulating and sharing these data could substantially improve AI models and, in turn, IVF success rates and treatment outcomes.
News | OVUM: Artificial Intelligence Will Lead a New Era of IVF Treatment
News | OVUM: Artificial Intelligence Will Lead a New Era of IVF Treatment
Despite continued advances in infertility treatment worldwide, in vitro fertilization (IVF) success rates remain relatively low and innovation has been slow. OVUM, a leading reproductive and fertility health brand, recently highlighted that artificial intelligence (AI) could transform IVF, improving treatment quality and success rates.
Current IVF outcomes and challenges
According to the latest Human Fertilisation and Embryology Authority (HFEA) statistics, the current live birth rate per embryo transferred is 25%, while rates for patients aged 35-37 and 38-39 are both 19%. These figures underscore the need for innovation in IVF. The global IVF success rate is about 30%, prompting extensive research into ways to improve outcomes. AI and machine learning are rapidly entering IVF research and clinical application as possible solutions.
Potential applications of AI in IVF
Although the process of fertilizing eggs and transferring embryos to the uterus is well established, success rates vary widely among clinics, showing room for improvement. OVUM asks whether AI can reduce these differences and raise IVF success rates.
AI—especially algorithms based on big-data analysis and machine learning—can automate parts of clinicians' or embryologists' decision-making and potentially improve outcomes. AI can process and classify large datasets, including data from previous IVF cycles, to support personalized treatment plans and identify embryos with the greatest transfer potential, a critical part of IVF success.
How AI may improve embryo selection
Embryo selection is one of the most closely watched applications of AI in IVF and may be the first to achieve widespread use. Embryologists currently select embryos manually based on visual assessment and chromosome testing. Differences in training, clinic practices, and grading methods can introduce bias and error. OVUM's fertility specialists say AI could use pattern recognition and reference datasets to overcome these limitations and recommend embryos most likely to result in pregnancy.
AI and personalized treatment
Beyond embryo selection, AI has substantial potential to create personalized treatment plans. IVF protocols often depend heavily on individualized trial and error, and outcomes vary widely among patients. By analyzing patient characteristics against large datasets, AI may help doctors identify optimal protocols and reduce the emotional and financial burden of repeated attempts.
Challenges and outlook
OVUM founder Jenny Wordsworth, a member of the British Fertility Society, said several issues must be considered before AI is fully adopted in fertility care: “Relying only on high-quality randomized controlled trials (RCTs) to validate AI in IVF may slow progress, because the algorithm may already be outdated by the time an RCT is published. We should explore other validation methods that reflect AI's unique role as a clinical decision-support tool.”
Regulators such as the HFEA also play a critical role in evaluating new technology, including AI tools for embryo selection. Although RCTs remain important, the HFEA's proposed sandbox approach could accelerate innovation by allowing a defined testing period followed by evaluation of real-world results.
Wordsworth added: “AI will gradually take over some traditional embryology tasks, such as measuring follicles or counting embryo cells, but it will not replace professionals. Clinicians need to understand the technology, and education and time will help build trust.”
Future directions
As data sharing and processing improve, AI's potential in fertility treatment will become clearer. More than three million women undergo IVF worldwide each year. Accumulating and sharing these data could substantially improve AI models and, in turn, IVF success rates and treatment outcomes.
Source:
Collected online