A Homogeneous Ensemble of Robust Pre-defined Neural Network Enables Automated Annotation of Human Embryo Morphokinetics
| Autor: | Gunawan B, Danardono, Alva, Erwin, James, Purnama, Nining, Handayani, Arie A, Polim, Arief, Boediono, Ivan, Sini |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Reproduction & Infertility. |
| ISSN: | 2251-676X 2228-5482 |
| DOI: | 10.18502/jri.v23i4.10809 |
| Popis: | Background: The purpose of the current study was to reduce the risk of human bias in assessing embryos by automatically annotating embryonic development based on their morphological changes at specified time-points with convolutional neural network (CNN) and artificial intelligence (AI). Methods: Time-lapse videos of embryo development were manually annotated by the embryologist and extracted for use as a supervised dataset, where the data were split into 14 unique classifications based on morphological differences. A compilation of homogeneous pre-trained CNN models obtained via TensorFlow Hub was tested with various hyperparameters on a controlled environment using transfer learning to create a new model. Subsequently, the performances of the AI models in correctly annotating embryo morphologies within the 14 designated classifications were compared with a collection of AI models with different built-in configurations so as to derive a model with the highest accuracy. Results: Eventually, an AI model with a specific configuration and an accuracy score of 67.68% was obtained, capable of predicting the embryo developmental stages (t1, t2, t3, t4, t5, t6, t7, t8, t9+, tCompaction, tM, tSB, tB, tEB). Conclusion: Currently, the technology and research of artificial intelligence and machine learning in the medical field have significantly and continuingly progressed in an effort to develop computer-assisted technology which could potentially increase the efficiency and accuracy of medical personnel’s performance. Nonetheless, building AI models with larger data is required to properly increase AI model reliability. |
| Databáze: | OpenAIRE |
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