Role of Artificial Intelligence in Quality Assurance in ART: A Review

Autor: Haroon Latif Khan, Shezae Khan, Shahzad Bhatti, Sana Abbas
Rok vydání: 2022
Předmět:
Zdroj: Fertility & Reproduction. :1-7
ISSN: 2661-3174
2661-3182
DOI: 10.1142/s2661318223300015
Popis: During the last few decades, assisted reproductive technologies (ARTs) have flourished rapidly and accompanied a set of advanced procedures such as intracytoplasmic sperm injection (ICSI), electronic witnessing, digital monitoring through embryoscope time-lapse systems, consistent decision-making algorithms with advanced statistics modes and preimplantation genetic testing (PGT). In usual practice, manual procedures were routinely used in IVF (in vitro fertilization) laboratories worldwide, but automation and artificial intelligence (AI) systems are promising techniques for quality assurance, which reduced the burden on the working staff in the embryology laboratory. In addition, these systems are equipped with powerful mathematical tools that minimize technician variability in the IVF lab and efficiently generate data for impaired gametes and embryos. The principal challenge of single-sperm selection out of 108 gametes can be sorted out by incorporating machine learning algorithms coupled with advanced data processing capabilities. In the same line, the emergence of closed embryo culture systems (CECSs) in human embryology has enabled the accurate morphokinetic evaluation of the more rapid cell division and the identification of normal and abnormal hallmarks of embryo viability. In particular, these CECSs are guided by the latest time-lapse microscopy (TLM) facility to continuously monitor embryo development kinetics without removing them from controlled and stable incubator conditions. In conclusion, AI-driven models can reduce technical variability in sample handling and remove the burden of the most subjective, tedious and/or monotonous aspects of the IVF lab. Furthermore, these systems also highlight environmental stressors that could hamper embryo development competence. In a broader sense, AI-based approaches are more accurate, precise and rapid in predicting embryo quality noninvasively.
Databáze: OpenAIRE
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