Harnessing Artificial Intelligence Technology for IVF Embryo Selection

Harnessing Artificial Intelligence Technology for IVF Embryo Selection

Harnessing Artificial Intelligence Technology for IVF Embryo Selection

image: Examples of embryos evaluated by the STORK-A algorithm. From left to right, an embryo is predicted to have a normal number of chromosomes or a single chromosomal abnormality; an embryo that is predicted to have a normal number of chromosomes; an embryo likely to have more than one chromosomal abnormality.
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Credit: Weill Cornell Medicine

An artificial intelligence algorithm can noninvasively determine, with around 70% accuracy, whether an in vitro fertilized embryo has a normal or abnormal number of chromosomes, according to a new study from researchers at Weill Cornell Medicine.

Having an abnormal number of chromosomes, a condition called aneuploidy, is one of the main reasons embryos from in vitro fertilization (IVF) fail to implant or result in a healthy pregnancy. One of the current methods of detecting aneuploidy involves biopsy-like sampling and genetic testing of cells from an embryo, an approach that adds cost to the IVF process and is invasive to the embryo. The new algorithm, STORK-A, described in an article published on December 19 in Lancet Digital Health, can help predict aneuploidy without the drawbacks of biopsy. It works by analyzing microscope images of the embryo and incorporates information about maternal age and the IVF clinic’s notation of how the embryo looks.

“Our hope is that we will eventually be able to predict aneuploidy completely non-invasively, using artificial intelligence and computer vision techniques,” said study lead author Dr Iman. Hajirasouliha, associate professor of computational genomics and physiology and biophysics. at Weill Cornell Medicine and Fellow of the England Institute for Precision Medicine.

The first author of the study is Josue Barnes, a doctoral candidate at the Weill Cornell Graduate School of Medical Sciences who is studying at the Hajirasouliha laboratory. Dr. Nikica Zaninovic, Associate Professor of Embryology in Clinical Obstetrics and Gynecology and Director of the Embryology Laboratory at the Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine at Weill Cornell Medicine and NewYork-Presbyterian/Weill Cornell Medical Center led the embryology work for the study.

According to the United States Centers for Disease Control and Prevention, more than 300,000 IVF cycles were performed in the United States in 2020, resulting in approximately 80,000 live births. IVF experts are always looking for ways to increase this success rate, to achieve more successful pregnancies with fewer embryo transfers, which means developing better methods to identify viable embryos.

Fertility clinic staff currently use microscopy to assess embryos for large-scale abnormalities that correlate with low viability. To obtain chromosome information, clinic staff may also use a biopsy method called preimplantation genetic testing for aneuploidy (PGT-A), primarily in women over 37 years of age.

To develop a computational approach to embryo assessment that capitalizes on the Embryology Laboratory’s pioneering use of time-lapse photography, researchers from the Center for Reproductive Medicine teamed up with colleagues from the England Institute .

In a 2019 study, teams developed an artificial intelligence (AI) algorithm, STORK, that could assess embryo quality as well as IVF clinic staff. For the new study, they developed STORK-A as a potential replacement for PGT-A or as a more selective way to decide which embryos should undergo PGT-A testing.

The new STORK-A algorithm uses microscope images of embryos taken five days after fertilization, clinic staff scoring of embryo quality, maternal age, and other information normally collected as part of of the IVF process. Because it uses AI, the algorithm automatically “learns” to correlate certain characteristics of the data, often too subtle for the human eye, with the risk of aneuploidy. The team trained STORK-A on a dataset of 10,378 blastocysts for which the ploidy status was already known.

Based on its performance, they rated the algorithm’s accuracy in predicting aneuploid “euploid” embryos versus normal chromosomes at almost 70% (69.3%). In predicting aneuploidy involving more than one chromosome – complex aneuploidy – versus euploidy, STORK-A was 77.6% accurate. They then tested the algorithm on independent datasets, including that of an IVF clinic in Spain, and found comparable accuracy results, demonstrating the generalizability of STORK-A.

The study provides proof of concept for an approach that is currently experimental. Standardizing the use of STORK-A in clinics would require clinical trials comparing it to PGT-A and Food and Drug Administration approval – all years to come. But the new algorithm represents progress on the road to less risky, less subjective, less expensive and more precise IVF embryo selection.

“This is another great example of how AI can potentially transform medicine. The algorithm transforms tens of thousands of embryo images into AI models that can ultimately be used to help improve medicine. ‘effectiveness of IVF and to further democratize access by reducing costs,’ said co-author Dr Olivier Elemento, director of the England Institute for Precision Medicine and a professor of physiology and biophysics and Computational Genomics in Computational Biomedicine at Weill Cornell Medicine.

“We believe that ultimately, by using this technology, we can reduce the number of embryos to be biopsied, reduce costs and provide a very good consultation tool with the patient when making the decision to do PGT. -A or not,” Dr. Zaninovic said.

The team now plans to build on this success with algorithms trained on videos of embryo development.

“By using video classification, we can take advantage of temporal and spatial information about embryo development, and we hope this will allow the detection of developmental patterns that distinguish aneuploidy from euploidy with even greater precision. high,” Barnes said.

“This technology is being optimized in the hope that at some point its accuracy will approach genetic testing, which is the gold standard and is over 90% accurate,” said co-author Dr. Zev Rosenwaks, director and chief medical officer of the Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine at NewYork-Presbyterian/Weill Cornell Medical Center and Weill Cornell Medicine, and Revlon Distinguished Professor of Reproductive Medicine in Obstetrics and Gynecology at Weill Cornell Medical. “But we realize that this goal is ambitious.”

Many physicians and scientists at Weill Cornell Medicine maintain relationships and collaborate with external organizations to foster scientific innovation and provide expert advice. The institution makes this information public for the sake of transparency. For this information, see the profiles for Dr. Iman Hajirasouliha, dr. Nikica Zaninovic, Dr AS Olivier Element and Dr. Zev Rosenwaks.

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