What AI Can Do for Assisted Reproductive Technology

What AI Can Do for Assisted Reproductive Technology

Healthcare Tech Outlook | Monday, November 09, 2020

The applications of AI and their amalgamation in the practice of ART have the potential to improve the fertility rate and address the grey areas.

FREMONT, CA: As per reports, the infertility rates in the United States range from 6 to 18 per cent; however, couples receive successful infertility treatment at rates of less than 1 per cent. The major challenges to the extensive use of Assisted Reproductive Technology (ART) are lack of access, high cost, the complexity of treatment, and reduced success rates. Despite decreasing fertility rates, relatively fewer resources have been dedicated to reproductive research. But the field is ripe for innovation. Major inroads have already made in disciplines infertility treatments, but in general, the industry has been slow to pursue opportunities in AI for reasons. AI has a remarkable potential to overcome the barriers of cost, access, and low success rates of ART. Read to know more.

The major three applications of AI that are most broadly used in ART are natural language processing (NLP), machine learning (ML), and robotics. NLP is a subset of AI which assists computers in understanding human language, text processing, and for classification. It can be used to extract useful information from contents like electronic medical records. NLP processes unstructured data and converts it into structured data which can be used for analysis and experimentation using other ML techniques.  Similarly, laboratory data can be prognosticated using NLP. ML is a subfield of AI that runs specific tasks without using explicit instructions, but by depending on patterns and inference instead.

AI finds clinical applicability in assessment, selection, and prediction of the sperm, oocyte, embryo to predict and enhance the success rate of ART. The key determinant for the success of IVF is the quality of gametes and embryo, but in the present practice gametes and embryos are graded by embryologists with the morphological assessment. Hence there is subjectivity in grading the quality. There is also no single parameter to grade or to predict the probability of pregnancy or to learn the cause of failure. These are some of the areas that AI-based approaches can solve, which would result in optimizing the treatment cycle of ART. However, it is important to pen down guidelines, policies, and recommendations for the implementation of AI in ART to ensure ethical practice.

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