Predicting Embryo Viability to Improve the Success Rate of Implantation in IVF Procedure: An AI-Based Prospective Cohort Study

Autor: Sumantra Ghosh, Deepti Barhate, Aaditya Pathak, Dhruvilsinh Jhala
Rok vydání: 2021
Předmět:
Zdroj: Computational Vision and Bio-Inspired Computing ISBN: 9789813368613
DOI: 10.1007/978-981-33-6862-0_32
Popis: In general, infertility affects one in the seven couples across the globe. Therefore, an innovative and beneficial procedure is used to fertilize an egg outside the human body with the help of in vitro fertilization (IVF) procedure. IVF is considered as the most common procedure, as it accounts for 99% of the infertility procedures. From being the most widely used procedure, its success rate for women under 35 is 39.6%, and above 40 is 11.5% depending on the factors like age, previous pregnancy, previous miscarriages, BMI and lifestyle. However, human embryos are complex by nature, and some aspects of their development are still remaining as a mystery for biologists. Embryologists will subjectively evaluate an embryo and its efficiency by making their observations manually during the embryo division process. Since these embryos are dividing rapidly, the manual evaluations are more prone to error. This paper gives a brief explanation and insights into the topic of evaluation and the success rate prediction by using artificial intelligence techniques.
Databáze: OpenAIRE