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Lee T, Natalwala J, Chapple V, Liu Y. A brief history of artificial intelligence embryo selection: from black-box to glass-box. Hum Reprod 2024; 39:285-292. [PMID: 38061074 PMCID: PMC11016335 DOI: 10.1093/humrep/dead254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 11/21/2023] [Indexed: 02/02/2024] Open
Abstract
With the exponential growth of computing power and accumulation of embryo image data in recent years, artificial intelligence (AI) is starting to be utilized in embryo selection in IVF. Amongst different AI technologies, machine learning (ML) has the potential to reduce operator-related subjectivity in embryo selection while saving labor time on this task. However, as modern deep learning (DL) techniques, a subcategory of ML, are increasingly used, its integrated black-box attracts growing concern owing to the well-recognized issues regarding lack of interpretability. Currently, there is a lack of randomized controlled trials to confirm the effectiveness of such black-box models. Recently, emerging evidence has shown underperformance of black-box models compared to the more interpretable traditional ML models in embryo selection. Meanwhile, glass-box AI, such as interpretable ML, is being increasingly promoted across a wide range of fields and is supported by its ethical advantages and technical feasibility. In this review, we propose a novel classification system for traditional and AI-driven systems from an embryology standpoint, defining different morphology-based selection approaches with an emphasis on subjectivity, explainability, and interpretability.
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Affiliation(s)
- Tammy Lee
- Fertility North, Joondalup Private Hospital, Joondalup, Western Australia, Australia
- School of Human Sciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Jay Natalwala
- Fertility North, Joondalup Private Hospital, Joondalup, Western Australia, Australia
| | - Vincent Chapple
- Fertility North, Joondalup Private Hospital, Joondalup, Western Australia, Australia
| | - Yanhe Liu
- Fertility North, Joondalup Private Hospital, Joondalup, Western Australia, Australia
- School of Human Sciences, University of Western Australia, Crawley, Western Australia, Australia
- Faculty of Health Sciences and Medicine, Bond University, Robina, Queensland, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
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Fruchter-Goldmeier Y, Kantor B, Ben-Meir A, Wainstock T, Erlich I, Levitas E, Shufaro Y, Sapir O, Har-Vardi I. An artificial intelligence algorithm for automated blastocyst morphometric parameters demonstrates a positive association with implantation potential. Sci Rep 2023; 13:14617. [PMID: 37669976 PMCID: PMC10480200 DOI: 10.1038/s41598-023-40923-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/18/2023] [Indexed: 09/07/2023] Open
Abstract
Blastocyst selection is primarily based on morphological scoring systems and morphokinetic data. These methods involve subjective grading and time-consuming techniques. Artificial intelligence allows for objective and quick blastocyst selection. In this study, 608 blastocysts were selected for transfer using morphokinetics and Gardner criteria. Retrospectively, morphometric parameters of blastocyst size, inner cell mass (ICM) size, ICM-to-blastocyst size ratio, and ICM shape were automatically measured by a semantic segmentation neural network model. The model was trained on 1506 videos with 102 videos for validation with no overlap between the ICM and trophectoderm models. Univariable logistic analysis found blastocyst size and ICM-to-blastocyst size ratio to be significantly associated with implantation potential. Multivariable regression analysis, adjusted for woman age, found blastocyst size to be significantly associated with implantation potential. The odds of implantation increased by 1.74 for embryos with a blastocyst size greater than the mean (147 ± 19.1 μm). The performance of the algorithm was represented by an area under the curve of 0.70 (p < 0.01). In conclusion, this study supports the association of a large blastocyst size with higher implantation potential and suggests that automatically measured blastocyst morphometrics can be used as a precise, consistent, and time-saving tool for improving blastocyst selection.
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Affiliation(s)
- Yael Fruchter-Goldmeier
- The Medical School for International Health and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | | | - Assaf Ben-Meir
- Fairtility Ltd., Tel Aviv, Israel
- Fertility and IVF Unit, Department of Obstetrics and Gynecology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Tamar Wainstock
- School of Public Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | | | - Eliahu Levitas
- The Medical School for International Health and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Fertility and IVF Unit, Department of Obstetrics and Gynecology, Soroka University Medical Center, Beer-Sheva, Israel
| | - Yoel Shufaro
- Infertility and IVF Unit, Beilinson Women's Hospital, Rabin Medical Center, Petach-Tikva, Israel
- The Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Onit Sapir
- Infertility and IVF Unit, Beilinson Women's Hospital, Rabin Medical Center, Petach-Tikva, Israel
- The Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Iris Har-Vardi
- The Medical School for International Health and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
- Fairtility Ltd., Tel Aviv, Israel.
- Fertility and IVF Unit, Department of Obstetrics and Gynecology, Soroka University Medical Center, Beer-Sheva, Israel.
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