Can a deep learning tool perform better than trained clinicians in selecting the highest-quality embryos for implementation?
A deep learning system was able to choose the most high-quality embryos for in-vitro fertilization (IVF) with 90 percent accuracy, according to a study published in eLife.
When compared with trained embryologists, the deep learning model performed with an accuracy of approximately 75 percent while the embryologists performed with an average accuracy of 67 percent.
The average success rate of IVF is 30 percent, researchers stated. The treatment is also expensive, costing patients over $10,000 for each IVF cycle with many patients requiring multiple cycles in order to achieve successful pregnancy.
While multiple factors determine the success of IVF cycles, the challenge of non-invasive selection of the highest available quality embryos from a patient remains one of the most important factors in achieving successful IVF outcomes.
Currently, tools available to embryologists are limited and expensive, leaving most embryologists to rely on their observational skills and expertise. Researchers from Brigham and Women’s Hospital and Massachusetts General Hospital (MGH) set out to develop an assistive tool that can evaluate images captured using microscopes traditionally available at fertility centers.
“There is so much at stake for our patients with each IVF cycle. Embryologists make dozens of critical decisions that impact the success of a patient cycle. With assistance from our AI system, embryologists will be able to select the embryo that will result in a successful pregnancy better than ever before,” said co-lead author Charles Bormann, PhD, MGH IVF Laboratory director.
The team trained the deep learning system using images of embryos captured at 113 hours post-insemination. Among 742 embryos, the AI system was 90 percent accurate in choosing the most high-quality embryos.
The investigators further assessed the system’s ability to distinguish among high-quality embryos with the normal number of human chromosomes and compared the system’s performance to that of trained embryologists.
The results showed that the system was able to differentiate and identify embryos with the highest potential for success significantly better than 15 experienced embryologists from five different fertility centers across the US.
Researchers pointed out that in its current state, the deep learning system is meant to act only as an assistive tool for embryologists to make judgments during embryo selection.
“We believe that these systems will benefit clinical embryologists and patients,” said corresponding author Hadi Shafiee, PhD, of the Division of Engineering in Medicine at the Brigham. “A major challenge in the field is deciding on the embryos that need to be transferred during IVF. Our system has tremendous potential to improve clinical decision making and access to care.”
The team also stated that while the study demonstrates the potential for deep learning to outperform human clinicians, further research is needed before these tools can be deployed in regular clinical care.
“Advances in artificial intelligence have fostered numerous applications that have the potential to improve standard-of-care in the different fields of medicine. While other groups have also evaluated different use cases for machine learning in assisted reproductive medicine, this approach is novel in how it used a deep learning system trained on a large dataset to make predictions based on static images,” researchers said.
“Although the current retrospective study shows that these systems can perform better than highly-trained embryologists, randomized control trials are required before routine use in clinical practice is adopted.”
The findings offer hope for individuals seeking to undergo IVF, the group concluded.
“Our approach has shown the potential of AI systems to be used in aiding embryologists to select the embryo with the highest implantation potential, especially amongst high-quality embryos,” said Manoj Kumar Kanakasabapathy, one of the co-lead authors.
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This article originally appeared in Health IT Analytics by Jessica Kent