Embryo selection with artificial intelligence: how to evaluate and compare methods
In recent years, artificial intelligence (AI) has been used extensively to improve and automate the embryo ranking and selection procedure by extracting relevant information from embryo microscopy images. AI models are evaluated based on their ability to identify the embryo(s) with the highest chance(s) of achieving a successful pregnancy. Whether such evaluations should be based on ranking performance or pregnancy prediction, however, seems to divide studies. As such, a variety of performance metrics are reported, and comparisons between studies are often made on different and incompatible outcomes and data foundations.
In this webinar, Mikkel Fly Kragh walk you through the most common evaluation metrics and describe their strengths and weaknesses, while relating them to actual clinical practice. We also provide a checklist of things to be aware of when reading or conducting AU studies on embryo selection. This includes topics such as:
- Clinical relevance
- Data types and sizes
- Evaluation metrics
- Model comparisons, biases and generalization performance
This webinar was recorded in November 2021.
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