A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images

作者：Rocha Jose Celso; Passalia Felipe Jose; Matos Felipe Delestro; Takahashi Maria Beatriz; Ciniciato Diego de Souza; Maserati Marc Peter; Alves Mayra Fernanda; de Almeida Tamie Guibu; Cardoso Bruna Lopes; Basso Andrea Cristina; Gouveia Nogueira Marcelo Fabio

来源：Scientific Reports, 2017, 7(1): 7659.

DOI：10.1038/s41598-017-08104-9

Morphological analysis is the standard method of assessing embryo quality; however, its inherent subjectivity tends to generate discrepancies among evaluators. Using genetic algorithms and artificial neural networks (ANNs), we developed a new method for embryo analysis that is more robust and reliable than standard methods. Bovine blastocysts produced in vitro were classified as grade 1 (excellent or good), 2 (fair), or 3 (poor) by three experienced embryologists according to the International Embryo Technology Society (IETS) standard. The images (n = 482) were subjected to automatic feature extraction, and the results were used as input for a supervised learning process. One part of the dataset (15%) was used for a blind test posterior to the fitting, for which the system had an accuracy of 76.4%. Interestingly, when the same embryologists evaluated a sub-sample (10%) of the dataset, there was only 54.0% agreement with the standard (mode for grades). However, when using the ANN to assess this sub-sample, there was 87.5% agreement with the modal values obtained by the evaluators. The presented methodology is covered by National Institute of Industrial Property (INPI) and World Intellectual Property Organization (WIPO) patents and is currently undergoing a commercial evaluation of its feasibility.

• 出版日期2017-8-9