MATERIALS AND METHODS: Using a dataset of 3,469 embryos, the deep CNN model was trained and tested to primarily classify images of embryos captured at 113 hours post insemination (hpi). A non-overlapping set of 97 euploid embryo images with known implantation outcomes was then used to compare the embryo predicting accuracy of 15 highly trained embryologists from multiple centers in the US to that of the CNN. Only euploid embryos that had undergone preimplantation genetic testing for aneuploidies (PGT-A) were included to remove the bias introduced by chromosomal abnormalities.RESULTS: The CNN performed with an accuracy of 75.3% while the embryologists performed with an average accuracy of 67.4% (min-max: 64.5%-70.2%) in differentiating euploid embryos based on their implantation outcome. The CNN performed with a sensitivity and specificity of 84.2% (CI: 72.1% to 92.5%) and 62.5% (CI: 45.8% to 77.3%), respectively. The positive predictive value (PPV) and negative predictive value (NPV) of the network were 76.2% (63.8% to 86.0%) and 73.5% (55.6% to 87.1%), respectively. A one sample t-test revealed that the CNN significantly outperformed embryologists in predicting embryo implantation of euploid embryos using a static image obtained at a single time-point (113 hpi) (P<0.0001).CONCLUSIONS: The trained artificial intelligence framework outperformed trained embryologists in identifying PGT-A euploid embryos destined to implant. A large randomized controlled trial is warranted to confirm that the developed CNN can improve in-vitro fertilization outcomes by prospectively selecting embryos with higher implantation potential than those selected with the current methods.
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