Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image. (19th February 2019)
- Record Type:
- Journal Article
- Title:
- Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image. (19th February 2019)
- Main Title:
- Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image
- Authors:
- Miyagi, Yasunari
Habara, Toshihiro
Hirata, Rei
Hayashi, Nobuyoshi - Abstract:
- 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. Abstract : Predicting live birth without aneuploidy by machine learning. Classifiers using artificial intelligence applied toward a blastocyst image have a potential to show the probability of live birth being the outcome.
- Is Part Of:
- Reproductive medicine and biology. Volume 18:Number 2(2019)
- Journal:
- Reproductive medicine and biology
- Issue:
- Volume 18:Number 2(2019)
- Issue Display:
- Volume 18, Issue 2 (2019)
- Year:
- 2019
- Volume:
- 18
- Issue:
- 2
- Issue Sort Value:
- 2019-0018-0002-0000
- Page Start:
- 204
- Page End:
- 211
- Publication Date:
- 2019-02-19
- Subjects:
- artificial intelligence -- blastocyst -- live birth -- machine learning
Reproduction -- Periodicals
Reproductive health -- Periodicals
612.6 - Journal URLs:
- http://www.blackwell-synergy.com/loi/rmb ↗
https://onlinelibrary.wiley.com/journal/14470578 ↗
http://www.springer.com/gb/ ↗ - DOI:
- 10.1002/rmb2.12267 ↗
- Languages:
- English
- ISSNs:
- 1445-5781
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7713.706120
British Library DSC - BLDSS-3PM
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- 9822.xml