BGU Researchers Use AI Imaging to Help Improve IVF Treatment Success
BGU Researchers Use AI Imaging to Help Improve IVF Treatment Success
September 19, 2024
Medical Research, Robotics & High-Tech
Dr. Assaf Zaritsky, Department of Software & Information Systems Engineering Faculty of Engineering Sciences, BGU
San Diego Jewish World – Researchers at Ben-Gurion University of the Negev (BGU) have developed a computational method that allows them to “reverse engineer” the artificial intelligence’s (AI) ‘decision’ by partitioning medical images into components with distinct clinical interpretations that are important for the AI.
Deep learning, using artificial neural networks, is an AI-based computational method capable of learning patterns directly from data by imitating the human brain’s learning process. The primary drawback of using such AI-based methods is the inability to decipher the reasoning behind the neural network’s decision.
BGU doctoral student Oded Rotem, under the guidance of Prof. Assaf Zaritsky from the Department of Software and Information Systems Engineering at BGU, developed a computational method, called DISCOVER, to reverse engineer AI by breaking down an image into semantically meaningful components through which the AI makes its decision. In collaboration with the Israeli startup AIVF, the researchers demonstrated the technology’s ability to characterize the in vitro fertilization (IVF) embryo’s features that were most significant to the AI in making a decision regarding the embryo’s visual quality.
The research team used a rich database of thousands of embryos collected by AIVF. The embryos were imaged using a light microscope, and embryologists at the company examined and ranked each embryo based on several characteristics such as embryo size and the chain of cells surrounding the embryo in the initial stages of development clinically termed the trophectoderm.
“Deep learning can identify hidden patterns that the human eye cannot detect in biomedical images. However, this is not enough – in order to make clinical or scientific decisions, we must decipher the mystery of discovering what the AI identified, interpret the biological or clinical significance of the explanation, and decide based on the interpretation on the next steps in treatment or research,” explained Prof. Zaritsky.
“Embryologists are well aware of the importance of certain biological features in determining embryo quality, but the human eye is often limited in its ability to measure and assess them accurately,” explained Daniela Gilboa, CEO of AIVF and a clinical embryologist by training. “A prime example of this is blastocyst density, a feature of great importance in embryo quality that is not widely used clinically because it is very difficult to quantify when visually examining the embryo in the laboratory. Now, with the visual interpretation of DISCOVER, it is possible to identify and analyze important biological properties more accurately and objectively. As a result, we can improve the process of selecting the embryo with the highest chances of successful implantation in the uterus, thereby increasing the chances of success in fertility treatments.”
DISCOVER’s interpretability mechanism relies on a ‘deepfake’ AI generator, which allows, for example, to replace one person’s face in an image with another.
By creating a series of ‘fake’ AI images of embryos that never existed using image generator AI, the researchers were able to identify changes in the embryo’s size and the chain of cells surrounding it – in accordance with the embryologist’s decision in the clinic.
The researchers demonstrated that the AI can successfully predict embryo quality with performance like a human expert, but the AI did not offer the researchers clues about which embryo features led to successful prediction.
Now, with the visual interpretation of DISCOVER, it is possible to identify and analyze important biological properties more accurately and objectively. As a result, we can improve the process of selecting the embryo with the highest chances of successful implantation in the uterus, thereby increasing the chances of success in fertility treatments.”
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