New methods for comparing embryo selection methods by applying artificial intelligence
Comparing embryo selection AI for live births
DOI:
https://doi.org/10.14738/jbemi.91.11676Keywords:
Artificial Intelligence, Blastocyst, Deep Learning, Live Birth, Computational Neural Network.Abstract
Purpose
To develop a process for comparing embryo selection methods by applying artificial intelligence (AI) for live births retrospectively with historical individual embryo sequence data from live births.
Methods
We sought to predict the results for live birth outcomes with the sequences of embryos from patient data. To do so, we developed two functions. For the first (ranking evaluation) method, we summed the weighted order number of the embryo sequence to determine the order number of embryos that became a live birth. For the second (probability evaluation) method, we calculated the AI-derived confidence scores of the embryo for predicting a live birth via the result of the embryo sequence.
Results
We used both methods to evaluate the embryo sequence for empirical samples. With the assumed known result of becoming a live birth in a sequence of blastocysts, any methods for creating sequences could now be quantitatively evaluated, regardless of whether AI is used.
Conclusions
Through use of AI, we could, to a certain extent, expect to evaluate the superiority or inferiority of past, current, or future methods. The accuracy of AI-based diagnosis for predicting a live birth should improve, meaning the methods presented herein will become increasingly useful.
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Copyright (c) 2022 Yasunari Miyagi
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