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LetoHealth: Minimizing in-vitro fertilization failures by utilizing artificial intelligence to evaluate the health of human embryos

LetoHealth serves as a second opinion for embryologists in the IVF treatment by providing accurate and reliable assessments of the health of patient embryonic cells and sperm morphology. The thorough evaluation of embryos and sperm can potentially avoid miscarriages and the failure to conceive through the IVF treatment process.

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An Introduction to LetoHealth

IVF, or In Vitro Fertilization, Therapy is a method of conception often used to aid infertile individuals when other methods of conception have failed. Selecting an embryo for transfer is a key step in the IVF procedure, and is usually done by an embryologist or a fertility specialist. This selection of a potential embryo is made by examining zona pellucida thickness variation, number of blastomeres, degree of cell symmetry and cytoplasmic fragmentation, etc. However, inconsistencies and potential errors made in this selection process can lead to potential life changing mistakes, including miscarriage and failure to conceive. This is where LetoHealth can help.

Why LetoHealth?

Letohealth employs artificial intelligence in order to assist embryologists in this selection process, and offers a second opinion in the evaluation of embryo health. The use of LetoHealth can significantly reduce the chance of miscarriage and failures in the IVF process by allowing embryologists to have a way to cross-check their initial diagnoses. If LetoHealth’s evaluation and the embryologists evaluation do not line up, embryologists can reevaluate and reanalyze their conclusion, potentially avoiding some life-changing mistakes.

Evaluation of Sperm Health

The evaluation of the morphological and physical traits of the donor sperm used in the fertilization of an embryo in the IVF process is also a prominent part in avoiding miscarriages and failures to conceive. LetoHealth utilizes convolutional neural networks to assess images of sperm samples and classify them into 2 classes based on poor or good quality sperm samples.

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The birth of the first baby conceived using in vitro fertilization (IVF) occurred in the late 1970s. Since then, there have been many advances in IVF. However, for many infertile patients, the journey to parenthood via IVF is prolonged and emotionally and financially challenging.

The current IVF embryo selection process is subject to inconsistent embryo grading. This is due to the nature of the selection, as it is traditionally manually executed by a medical professional. Additionally, many unsuccessful IVF births have been a result of the faulty embryo grading system, as any clinician, doctor, or medical professional is prone to human error. This led to the following research question.

How has human error in the diagnostic process of evaluating a healthy embryo to undergo fertilization via the IVF process contributed to failures to conceive, and how can artificial intelligence reduce human error to improve this outcome?

Although human error is completely normal and unavoidable, when it comes to a matter of life and death, the accuracy that artificial intelligence and machine learning provide results in less of a chance of error. My initial impression of many other works in this field surrounded the idea that the usage of deep learning allows machine learning models to make more precise and accurate decisions. Keeping this knowledge in mind and utilizing information learned from research I have conducted on the intersection of AI with the IVF process.

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