Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image
Abstract Purpose To make the artificial intelligence (AI) classifiers of the image of the blastocyst implanted later in order to predict the probability of achieving live birth. Methods A system for using the machine learning approaches, which are logistic regression, naive Bayes, nearest neighbors,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-04-01
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| Series: | Reproductive Medicine and Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/rmb2.12267 |
| _version_ | 1818299825103306752 |
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| author | Yasunari Miyagi Toshihiro Habara Rei Hirata Nobuyoshi Hayashi |
| author_facet | Yasunari Miyagi Toshihiro Habara Rei Hirata Nobuyoshi Hayashi |
| author_sort | Yasunari Miyagi |
| collection | DOAJ |
| description | Abstract Purpose To make the artificial intelligence (AI) classifiers of the image of the blastocyst implanted later in order to predict the probability of achieving live birth. Methods A system for using the machine learning approaches, which are logistic regression, naive Bayes, nearest neighbors, random forest, neural network, and support vector machine, of artificial intelligence to predict the probability of live birth from a blastocyst image was developed. Eighty images of blastocysts that led to live births and 80 images of blastocysts that led to aneuploid miscarriages were used to create an AI‐based method with 5‐fold cross‐validation retrospectively for classifying embryos. Results The logistic regression method showed the best results. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.65, 0.60, 0.70, 0.67, and 0.64, respectively. Area under the curve was 0.65 ± 0.04 (mean ± SE). Estimated probability of belonging to the live birth category was found significantly related to the probability of live birth (P < 0.005). Conclusions Classifiers using artificial intelligence applied toward a blastocyst image have a potential to show the probability of live birth being the outcome. |
| first_indexed | 2024-12-13T04:57:22Z |
| format | Article |
| id | doaj.art-d798de0c72b24c74ae4dc2cb7bb80f50 |
| institution | Directory Open Access Journal |
| issn | 1445-5781 1447-0578 |
| language | English |
| last_indexed | 2024-12-13T04:57:22Z |
| publishDate | 2019-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Reproductive Medicine and Biology |
| spelling | doaj.art-d798de0c72b24c74ae4dc2cb7bb80f502022-12-21T23:58:52ZengWileyReproductive Medicine and Biology1445-57811447-05782019-04-0118220421110.1002/rmb2.12267Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst imageYasunari Miyagi0Toshihiro Habara1Rei Hirata2Nobuyoshi Hayashi3Medical Data Labo Okayama City JapanOkayama Couple’s Clinic Okayama City JapanOkayama Couple’s Clinic Okayama City JapanOkayama Couple’s Clinic Okayama City JapanAbstract Purpose To make the artificial intelligence (AI) classifiers of the image of the blastocyst implanted later in order to predict the probability of achieving live birth. Methods A system for using the machine learning approaches, which are logistic regression, naive Bayes, nearest neighbors, random forest, neural network, and support vector machine, of artificial intelligence to predict the probability of live birth from a blastocyst image was developed. Eighty images of blastocysts that led to live births and 80 images of blastocysts that led to aneuploid miscarriages were used to create an AI‐based method with 5‐fold cross‐validation retrospectively for classifying embryos. Results The logistic regression method showed the best results. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.65, 0.60, 0.70, 0.67, and 0.64, respectively. Area under the curve was 0.65 ± 0.04 (mean ± SE). Estimated probability of belonging to the live birth category was found significantly related to the probability of live birth (P < 0.005). Conclusions Classifiers using artificial intelligence applied toward a blastocyst image have a potential to show the probability of live birth being the outcome.https://doi.org/10.1002/rmb2.12267artificial intelligenceblastocystlive birthmachine learning |
| spellingShingle | Yasunari Miyagi Toshihiro Habara Rei Hirata Nobuyoshi Hayashi Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image Reproductive Medicine and Biology artificial intelligence blastocyst live birth machine learning |
| title | Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image |
| title_full | Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image |
| title_fullStr | Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image |
| title_full_unstemmed | Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image |
| title_short | Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image |
| title_sort | feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image |
| topic | artificial intelligence blastocyst live birth machine learning |
| url | https://doi.org/10.1002/rmb2.12267 |
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