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Mizobe Y, Kuwatsuru Y, Kuroki Y, Fukumoto Y, Tokudome M, Moewaki H, Orita Y, Iwakawa T, Takeuchi K. Formation of the first plane of division relative to the pronuclear axis predicts embryonic ploidy. Reprod Biomed Online 2024; 49:104110. [PMID: 38968730 DOI: 10.1016/j.rbmo.2024.104110] [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: 03/14/2024] [Revised: 04/30/2024] [Accepted: 05/07/2024] [Indexed: 07/07/2024]
Abstract
RESEARCH QUESTION Is there a relationship between the pronuclear axis and the first cleavage plane formation in human pronuclear-stage embryos, and what are the effects on ploidy and clinical pregnancy rates? DESIGN Transferred embryos were followed up until their prognoses. A total of 762 embryos formed two cells and reached the blastocyst stage after normal fertilization in a time-lapse incubator. Embryos were classified into three groups: group A: embryos in which the first plane of division was formed parallel to the axis of the pronucleus; group B: embryos in which cases of oblique formation were observed; and group C: embryos in which cases of perpendicular formation were observed. RESULTS The euploidy rate was significantly higher in groups A and B than those in group C (P < 0.01), whereas the aneuploidy rate was significantly higher in group C (P < 0.01) than in groups A and B. No differences were found between the three groups in frequency of positive HCG-based pregnancy tests, frequency of clinical pregnancies, miscarriage rates or delivery rates. CONCLUSIONS The formation pattern of the first plane of division relative to the pronuclear axis was a predictor of embryonic ploidy, with a reduced rate of euploidy and a high probability of aneuploidy observed when the first plane of division was perpendicular to the pronuclear axis.
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Affiliation(s)
- Yamato Mizobe
- Takeuchi Ladies Clinic/Center for Reproductive Medicine, 502-2 Higashimochida, Aira-shi, Kagoshima 899-5421, Japan.
| | - Yukari Kuwatsuru
- Takeuchi Ladies Clinic/Center for Reproductive Medicine, 502-2 Higashimochida, Aira-shi, Kagoshima 899-5421, Japan
| | - Yuko Kuroki
- Takeuchi Ladies Clinic/Center for Reproductive Medicine, 502-2 Higashimochida, Aira-shi, Kagoshima 899-5421, Japan
| | - Yumiko Fukumoto
- Takeuchi Ladies Clinic/Center for Reproductive Medicine, 502-2 Higashimochida, Aira-shi, Kagoshima 899-5421, Japan
| | - Mari Tokudome
- Takeuchi Ladies Clinic/Center for Reproductive Medicine, 502-2 Higashimochida, Aira-shi, Kagoshima 899-5421, Japan
| | - Harue Moewaki
- Takeuchi Ladies Clinic/Center for Reproductive Medicine, 502-2 Higashimochida, Aira-shi, Kagoshima 899-5421, Japan
| | - Yuji Orita
- Takeuchi Ladies Clinic/Center for Reproductive Medicine, 502-2 Higashimochida, Aira-shi, Kagoshima 899-5421, Japan
| | - Tokiko Iwakawa
- Takeuchi Ladies Clinic/Center for Reproductive Medicine, 502-2 Higashimochida, Aira-shi, Kagoshima 899-5421, Japan
| | - Kazuhiro Takeuchi
- Takeuchi Ladies Clinic/Center for Reproductive Medicine, 502-2 Higashimochida, Aira-shi, Kagoshima 899-5421, Japan
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Illingworth PJ, Venetis C, Gardner DK, Nelson SM, Berntsen J, Larman MG, Agresta F, Ahitan S, Ahlström A, Cattrall F, Cooke S, Demmers K, Gabrielsen A, Hindkjær J, Kelley RL, Knight C, Lee L, Lahoud R, Mangat M, Park H, Price A, Trew G, Troest B, Vincent A, Wennerström S, Zujovic L, Hardarson T. Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial. Nat Med 2024:10.1038/s41591-024-03166-5. [PMID: 39122964 DOI: 10.1038/s41591-024-03166-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/29/2024] [Indexed: 08/12/2024]
Abstract
To assess the value of deep learning in selecting the optimal embryo for in vitro fertilization, a multicenter, randomized, double-blind, noninferiority parallel-group trial was conducted across 14 in vitro fertilization clinics in Australia and Europe. Women under 42 years of age with at least two early-stage blastocysts on day 5 were randomized to either the control arm, using standard morphological assessment, or the study arm, employing a deep learning algorithm, intelligent Data Analysis Score (iDAScore), for embryo selection. The primary endpoint was a clinical pregnancy rate with a noninferiority margin of 5%. The trial included 1,066 patients (533 in the iDAScore group and 533 in the morphology group). The iDAScore group exhibited a clinical pregnancy rate of 46.5% (248 of 533 patients), compared to 48.2% (257 of 533 patients) in the morphology arm (risk difference -1.7%; 95% confidence interval -7.7, 4.3; P = 0.62). This study was not able to demonstrate noninferiority of deep learning for clinical pregnancy rate when compared to standard morphology and a predefined prioritization scheme. Australian New Zealand Clinical Trials Registry (ANZCTR) registration: 379161 .
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Affiliation(s)
| | - Christos Venetis
- IVFAustralia, Sydney, New South Wales, Australia
- Unit for Human Reproduction, 1st Dept of Ob/Gyn, Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Centre for Big Data Research in Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
| | - David K Gardner
- Melbourne IVF, Melbourne, Victoria, Australia
- School of BioSciences, University of Melbourne, Parkville, Victoria, Australia
| | - Scott M Nelson
- School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, UK
- TFP Fertility, Institute of Reproductive Sciences, Oxford, UK
| | | | | | | | | | - Aisling Ahlström
- IVIRMA Global Research Alliance, Livio Gothenburg, Gothenburg, Sweden
| | | | - Simon Cooke
- IVFAustralia, Sydney, New South Wales, Australia
| | - Kristy Demmers
- Queensland Fertility Group, Brisbane, Queensland, Australia
| | | | | | | | | | - Lisa Lee
- Melbourne IVF, Melbourne, Victoria, Australia
| | | | | | - Hannah Park
- Dept of Reproductive Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | | | - Geoffrey Trew
- TFP Fertility, Institute of Reproductive Sciences, Oxford, UK
- Imperial College London, London, UK
| | - Bettina Troest
- The Fertility Unit, Aalborg University Hospital, Aalborg, Denmark
| | - Anna Vincent
- TFP Fertility, Institute of Reproductive Sciences, Oxford, UK
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3
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Zou H, Wang R, Morbeck DE. Diagnostic or prognostic? Decoding the role of embryo selection on in vitro fertilization treatment outcomes. Fertil Steril 2024; 121:730-736. [PMID: 38185198 DOI: 10.1016/j.fertnstert.2024.01.005] [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: 11/28/2023] [Revised: 12/22/2023] [Accepted: 01/03/2024] [Indexed: 01/09/2024]
Abstract
In this review, we take a fresh look at embryo assessment and selection methods from the perspective of diagnosis and prognosis. On the basis of a systematic search in the literature, we examined the evidence on the prognostic value of different embryo assessment methods, including morphological assessment, blastocyst culture, time-lapse imaging, artificial intelligence, and preimplantation genetic testing for aneuploidy.
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Affiliation(s)
- Haowen Zou
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Victoria, Australia
| | - Rui Wang
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Victoria, Australia
| | - Dean E Morbeck
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Victoria, Australia; Principle, Morbeck Consulting Ltd, Auckland, New Zealand.
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4
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Lee CI, Huang CC, Lee TH, Chen HH, Cheng EH, Lin PY, Yu TN, Chen CI, Chen CH, Lee MS. Associations between the artificial intelligence scoring system and live birth outcomes in preimplantation genetic testing for aneuploidy cycles. Reprod Biol Endocrinol 2024; 22:12. [PMID: 38233926 PMCID: PMC10792866 DOI: 10.1186/s12958-024-01185-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 01/12/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND Several studies have demonstrated that iDAScore is more accurate in predicting pregnancy outcomes in cycles without preimplantation genetic testing for aneuploidy (PGT-A) compared to KIDScore and the Gardner criteria. However, the effectiveness of iDAScore in cycles with PGT-A has not been thoroughly investigated. Therefore, this study aims to assess the association between artificial intelligence (AI)-based iDAScore (version 1.0) and pregnancy outcomes in single-embryo transfer (SET) cycles with PGT-A. METHODS This retrospective study was approved by the Institutional Review Board of Chung Sun Medical University, Taichung, Taiwan. Patients undergoing SET cycles (n = 482) following PGT-A at a single reproductive center between January 2017 and June 2021. The blastocyst morphology and morphokinetics of all embryos were evaluated using a time-lapse system. The blastocysts were ranked based on the scores generated by iDAScore, which were defined as AI scores, or by KIDScore D5 (version 3.2) following the manufacturer's protocols. A single blastocyst without aneuploidy was transferred after examining the embryonic ploidy status using a next-generation sequencing-based PGT-A platform. Logistic regression analysis with generalized estimating equations was conducted to assess whether AI scores are associated with the probability of live birth (LB) while considering confounding factors. RESULTS Logistic regression analysis revealed that AI score was significantly associated with LB probability (adjusted odds ratio [OR] = 2.037, 95% confidence interval [CI]: 1.632-2.542) when pulsatility index (PI) level and types of chromosomal abnormalities were controlled. Blastocysts were divided into quartiles in accordance with their AI score (group 1: 3.0-7.8; group 2: 7.9-8.6; group 3: 8.7-8.9; and group 4: 9.0-9.5). Group 1 had a lower LB rate (34.6% vs. 59.8-72.3%) and a higher rate of pregnancy loss (26% vs. 4.7-8.9%) compared with the other groups (p < 0.05). The receiver operating characteristic curve analysis verified that the iDAScore had a significant but limited ability to predict LB (area under the curve [AUC] = 0.64); this ability was significantly weaker than that of the combination of iDAScore, type of chromosomal abnormalities, and PI level (AUC = 0.67). In the comparison of the LB groups with the non-LB groups, the AI scores were significantly lower in the non-LB groups, both for euploid (median: 8.6 vs. 8.8) and mosaic (median: 8.0 vs. 8.6) SETs. CONCLUSIONS Although its predictive ability can be further enhanced, the AI score was significantly associated with LB probability in SET cycles. Euploid or mosaic blastocysts with low AI scores (≤ 7.8) were associated with a lower LB rate, indicating the potential of this annotation-free AI system as a decision-support tool for deselecting embryos with poor pregnancy outcomes following PGT-A.
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Affiliation(s)
- Chun-I Lee
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
- Department of Obstetrics and Gynecology, Chung Shan Medical University Hospital, Taichung, Taiwan
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Chun-Chia Huang
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Tsung-Hsien Lee
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
- Department of Obstetrics and Gynecology, Chung Shan Medical University Hospital, Taichung, Taiwan
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Hsiu-Hui Chen
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan
| | - En-Hui Cheng
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Pin-Yao Lin
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Tzu-Ning Yu
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | - Chung-I Chen
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | - Chien-Hong Chen
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan.
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan.
| | - Maw-Sheng Lee
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan.
- Department of Obstetrics and Gynecology, Chung Shan Medical University Hospital, Taichung, Taiwan.
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan.
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan.
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Ueno S, Berntsen J, Okimura T, Kato K. Improved pregnancy prediction performance in an updated deep-learning embryo selection model: a retrospective independent validation study. Reprod Biomed Online 2024; 48:103308. [PMID: 37914559 DOI: 10.1016/j.rbmo.2023.103308] [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: 04/11/2023] [Revised: 06/20/2023] [Accepted: 07/24/2023] [Indexed: 11/03/2023]
Abstract
RESEARCH QUESTION What is the effect of increasing training data on the performance of ongoing pregnancy prediction after single vitrified-warmed blastocyst transfer (SVBT) in a deep-learning model? DESIGN A total of 3960 SVBT cycles were retrospectively analysed. Embryos were stratified according to the Society for Assisted Reproductive Technology age groups. Embryos were scored by deep-learning models iDAScore v1.0 (IDA-V1) and iDAScore v2.0 (IDA-V2) (15% more training data than v1.0) and by Gardner grading. The discriminative performance of the pregnancy prediction for each embryo scoring model was compared using the area under the curve (AUC) of the receiver operating characteristic curve for each maternal age group. RESULTS The AUC of iDA-V2, iDA-V1 and Gardener grading in all cohort were 0.736, 0.720 and 0.702, respectively. iDA-V2 was significantly higher than iDA-V1 and Gardener grading (P < 0.0001). Group > 35 years (n = 757): the AUC of iDA-V2 was significantly higher than Gardener grading (0.718 versus 0.694, P = 0.015); group aged 35-37 years (n = 821), the AUC of iDA-V2 was significantly higher than iDA-V1 (0.712 versus 0.696, P = 0.035); group aged 41-42 years (n = 715, the AUC of iDA-V2 was significantly higher than Gardener grading (0.745 versus 0.696, P = 0.007); group > 42 years (n = 660) and group aged 38-40 years (n = 1007), no significant differences were found between the groups. CONCLUSION The performance of deep learning models for pregnancy prediction will be improved by increasing the size of the training data.
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Affiliation(s)
- Satoshi Ueno
- Kato Ladies Clinic, 7-20-3, Nishi-shinjuku, Shinjuku, Tokyo 160-0023, Japan
| | | | - Tadashi Okimura
- Kato Ladies Clinic, 7-20-3, Nishi-shinjuku, Shinjuku, Tokyo 160-0023, Japan
| | - Keiichi Kato
- Kato Ladies Clinic, 7-20-3, Nishi-shinjuku, Shinjuku, Tokyo 160-0023, Japan..
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Ahlström A, Berntsen J, Johansen M, Bergh C, Cimadomo D, Hardarson T, Lundin K. Correlations between a deep learning-based algorithm for embryo evaluation with cleavage-stage cell numbers and fragmentation. Reprod Biomed Online 2023; 47:103408. [PMID: 37866216 DOI: 10.1016/j.rbmo.2023.103408] [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: 06/07/2023] [Revised: 08/29/2023] [Accepted: 09/04/2023] [Indexed: 10/24/2023]
Abstract
RESEARCH QUESTION Do cell numbers and degree of fragmentation in cleavage-stage embryos, assessed manually, correlate with evaluations made by deep learning algorithm model iDAScore v2.0? DESIGN Retrospective observational study (n = 5040 embryos; 1786 treatments) conducted at two Swedish assisted reproductive technology centres between 2016 and 2021. Fresh single embryo transfer was carried out on days 2 or 3 after fertilization. Embryo evaluation using iDAScore v2.0 was compared with manual assessment of numbers of cells and grade of fragmentation, analysed by video sequences. RESULTS Data from embryos transferred on days 2 and 3 showed that having three or fewer cells compared with four or fewer cells on day 2, and six or fewer cells versus seven to eight cells on day 3, correlated significantly with a difference in iDAScore (medians 2.4 versus 4.0 and 2.6 versus 4.6 respectively; both P < 0.001). The iDAScore for 0-10% fragmentation was significantly higher compared with the groups with higher fragmentation (P < 0.001). When combining cell numbers and fragmentation, iDAScore values decreased as fragmentation increased, regardless of cell number. iDAScore discriminated between embryos that resulted in live birth or no live birth (AUC of 0.627 and 0.607), compared with the morphological model (AUC of 0.618 and 0.585) for day 2 and day 3, respectively. CONCLUSIONS The iDAScore v2.0 values correlated significantly with cell numbers and fragmentation scored manually for cleavage-stage embryos on days 2 and 3. iDAScore had some predictive value for live birth, conditional that embryo selection was based on morphology.
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Affiliation(s)
| | | | | | - Christina Bergh
- Reproductive Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Danilo Cimadomo
- IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
| | | | - Kersti Lundin
- Reproductive Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
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Giménez-Rodríguez C, Meseguer M. The patient or the blastocyst; which leads to the perfect outcome prediction? Fertil Steril 2023; 120:811-812. [PMID: 37572788 DOI: 10.1016/j.fertnstert.2023.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/06/2023] [Accepted: 08/07/2023] [Indexed: 08/14/2023]
Affiliation(s)
- Carla Giménez-Rodríguez
- IVIRMA Global Research Alliance, IVI Foundation, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain; IVIRMA Global Research Alliance, IVIRMA Valencia, Valencia, Spain
| | - Marcos Meseguer
- IVIRMA Global Research Alliance, IVI Foundation, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain; IVIRMA Global Research Alliance, IVIRMA Valencia, Valencia, Spain
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8
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Johansen MN, Parner ET, Kragh MF, Kato K, Ueno S, Palm S, Kernbach M, Balaban B, Keleş İ, Gabrielsen AV, Iversen LH, Berntsen J. Comparing performance between clinics of an embryo evaluation algorithm based on time-lapse images and machine learning. J Assist Reprod Genet 2023; 40:2129-2137. [PMID: 37423932 PMCID: PMC10440335 DOI: 10.1007/s10815-023-02871-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 06/20/2023] [Indexed: 07/11/2023] Open
Abstract
PURPOSE This article aims to assess how differences in maternal age distributions between IVF clinics affect the performance of an artificial intelligence model for embryo viability prediction and proposes a method to account for such differences. METHODS Using retrospectively collected data from 4805 fresh and frozen single blastocyst transfers of embryos incubated for 5 to 6 days, the discriminative performance was assessed based on fetal heartbeat outcomes. The data was collected from 4 clinics, and the discrimination was measured in terms of the area under ROC curves (AUC) for each clinic. To account for the different age distributions between clinics, a method for age-standardizing the AUCs was developed in which the clinic-specific AUCs were standardized using weights for each embryo according to the relative frequency of the maternal age in the relevant clinic compared to the age distribution in a common reference population. RESULTS There was substantial variation in the clinic-specific AUCs with estimates ranging from 0.58 to 0.69 before standardization. The age-standardization of the AUCs reduced the between-clinic variance by 16%. Most notably, three of the clinics had quite similar AUCs after standardization, while the last clinic had a markedly lower AUC both with and without standardization. CONCLUSION The method of using age-standardization of the AUCs that is proposed in this article mitigates some of the variability between clinics. This enables a comparison of clinic-specific AUCs where the difference in age distributions is accounted for.
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Affiliation(s)
| | - Erik T Parner
- Section for Biostatistics, Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Mikkel F Kragh
- Vitrolife A/S, Jens Juuls Vej 18-20, 8260, Viby J, Denmark
- The AI Lab Aps, Aarhus, Denmark
| | | | | | | | | | | | - İpek Keleş
- Koc University Hospital, Istanbul, Turkey
| | | | - Lea H Iversen
- Fertility Clinic, Horsens Regional Hospital, Horsens, Denmark
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Zhu J, Wu L, Liu J, Liang Y, Zou J, Hao X, Huang G, Han W. External validation of a model for selecting day 3 embryos for transfer based upon deep learning and time-lapse imaging. Reprod Biomed Online 2023; 47:103242. [PMID: 37429765 DOI: 10.1016/j.rbmo.2023.05.014] [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: 10/18/2022] [Revised: 05/26/2023] [Accepted: 05/30/2023] [Indexed: 07/12/2023]
Abstract
RESEARCH QUESTION Could objective embryo assessment using iDAScore Version 2.0 perform as well as conventional morphological assessment? DESIGN A retrospective cohort study of fresh day 3 embryo transfer cycles was conducted at a large reproductive medicine centre. In total, 7786 embryos from 4328 cycles with known implantation data were cultured in a time-lapse incubator and included in the study. Fetal heartbeat (FHB) rate was analysed retrospectively using iDAScore Version 2.0 and conventional morphological assessment associated with the transferred embryos. The pregnancy-prediction performance of the two assessment methods was compared using area under the curve (AUC) values for predicting FHB. RESULTS AUC values were significantly higher for iDAScore compared with morphological assessment for all cycles (0.62 versus 0.60; P = 0.005), single-embryo transfer cycles (0.63 versus 0.60; P = 0.043) and double-embryo transfer cycles (0.61 versus 0.59; P = 0.012). For the age subgroups, AUC values were significantly higher for iDAScore compared with morphological assessment in the <35 years subgroup (0.62 versus 0.60; P = 0.009); however, no significant difference was found in the ≥35 years subgroup. In terms of the number of blastomeres, AUC values were significantly higher for iDAScore compared with morphological assessment for both the <8c subgroup (0.67 versus 0.56; P < 0.001) and the ≥8c subgroup (0.58 versus 0.55; P = 0.012). CONCLUSIONS iDAScore Version 2.0 performed as well as, or better than, conventional morphological assessment in fresh day 3 embryo transfer cycles. iDAScore Version 2.0 may therefore constitute a promising tool for selecting embryos with the highest likelihood of implantation.
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Affiliation(s)
- Jiahong Zhu
- Chongqing Clinical Research Centre for Reproductive Medicine, Chongqing Health Centre for Women and Children, Chongqing, China; Chongqing Key Laboratory of Human Embryo Engineering, Centre for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Lihong Wu
- Chongqing Clinical Research Centre for Reproductive Medicine, Chongqing Health Centre for Women and Children, Chongqing, China; Chongqing Key Laboratory of Human Embryo Engineering, Centre for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Junxia Liu
- Chongqing Clinical Research Centre for Reproductive Medicine, Chongqing Health Centre for Women and Children, Chongqing, China; Chongqing Key Laboratory of Human Embryo Engineering, Centre for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Yanfeng Liang
- Chongqing Clinical Research Centre for Reproductive Medicine, Chongqing Health Centre for Women and Children, Chongqing, China; Chongqing Key Laboratory of Human Embryo Engineering, Centre for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Jiayi Zou
- Chongqing Clinical Research Centre for Reproductive Medicine, Chongqing Health Centre for Women and Children, Chongqing, China; Chongqing Key Laboratory of Human Embryo Engineering, Centre for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Xiangwei Hao
- Chongqing Clinical Research Centre for Reproductive Medicine, Chongqing Health Centre for Women and Children, Chongqing, China; Chongqing Key Laboratory of Human Embryo Engineering, Centre for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Guoning Huang
- Chongqing Clinical Research Centre for Reproductive Medicine, Chongqing Health Centre for Women and Children, Chongqing, China; Chongqing Key Laboratory of Human Embryo Engineering, Centre for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Chongqing, China.
| | - Wei Han
- Chongqing Clinical Research Centre for Reproductive Medicine, Chongqing Health Centre for Women and Children, Chongqing, China; Chongqing Key Laboratory of Human Embryo Engineering, Centre for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Chongqing, China.
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10
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Sarandi S, Boumerdassi Y, O'Neill L, Puy V, Sifer C. [Interest of iDAScore (intelligent Data Analysis Score) for embryo selection in routine IVF laboratory practice: Results of a preliminary study]. GYNECOLOGIE, OBSTETRIQUE, FERTILITE & SENOLOGIE 2023; 51:372-377. [PMID: 37271479 DOI: 10.1016/j.gofs.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 04/11/2023] [Accepted: 05/15/2023] [Indexed: 06/06/2023]
Abstract
INTRODUCTION Embryo selection is a major challenge in ART, especially since the generalization of single embryo transfer, and its optimization could lead to the improvement of clinical results in IVF. Recently, several Artificial Intelligence (AI) models, based on deep-learning such as iDAScore, have been developed. These models, trained on time-lapse videos of embryos with known implantation data, can predict the probability of pregnancy for a given embryo, allowing automatization and standardization in embryo selection. MATERIAL AND METHODS In this study, we have compared the hierarchical categorization of 311 D5 blastocysts of iDAScore v1.0 and the embryologists of our unit. These 311 D5 blastocysts have been classified as top (70.1%), good (Q+: 10.6%) and poor (Q-: 19.3%) quality by embryologists according to Gardner classification. Median iDAScores were [9.9-8.4],]8.4-7.5] and]7.5-2.1] for top, good and poor-quality blastocysts respectively. RESULTS We observed a significantly concordant categorization between iDAScore and embryologists for top, good and poor-quality blastocysts (respectively, 89.5, 36.4 and 48.3%, P < 10-4). Moreover, the hierarchical categorization of the three best blastocysts between iDAScore and the embryologists was as follow: 1st rank: 71.9%; 2nd rank: 61.6%; 3rd rank: 56.8% (P=0.07). One hundred and fifty-one blastocysts with known implantation data were analyzed. The iDAScore of blastocysts that implanted was significantly higher than those that did not implant (implantation+: 9.10±0.57; implantation-: 8.70±0.95, P=0.003). CONCLUSION This preliminary study shows that iDAScore is able to perform a reproducible, reliable and immediate hierarchical classification of blastocysts. Moreover, this tool can identify the blastocysts with the highest implantation potential. If these results confirmed on a larger scale of embryos and patients, IA could revolutionize IVF laboratories by standardizing embryo hierarchical selection.
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Affiliation(s)
- S Sarandi
- Service d'histologie-embryologie-cytogénétique-CECOS, centre hospitalier universitaire Jean-Verdier, AP-HP, avenue du 14-Juillet, 93140 Bondy, France
| | - Y Boumerdassi
- Service d'histologie-embryologie-cytogénétique-CECOS, centre hospitalier universitaire Jean-Verdier, AP-HP, avenue du 14-Juillet, 93140 Bondy, France; Université Sorbonne Paris Nord, 93000 Bobigny, France
| | - L O'Neill
- Service d'histologie-embryologie-cytogénétique-CECOS, centre hospitalier universitaire Jean-Verdier, AP-HP, avenue du 14-Juillet, 93140 Bondy, France
| | - V Puy
- Service d'histologie-embryologie-cytogénétique-CECOS, centre hospitalier universitaire Jean-Verdier, AP-HP, avenue du 14-Juillet, 93140 Bondy, France; Université Sorbonne Paris Nord, 93000 Bobigny, France
| | - C Sifer
- Service d'histologie-embryologie-cytogénétique-CECOS, centre hospitalier universitaire Jean-Verdier, AP-HP, avenue du 14-Juillet, 93140 Bondy, France.
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Theilgaard Lassen J, Fly Kragh M, Rimestad J, Nygård Johansen M, Berntsen J. Development and validation of deep learning based embryo selection across multiple days of transfer. Sci Rep 2023; 13:4235. [PMID: 36918648 PMCID: PMC10015019 DOI: 10.1038/s41598-023-31136-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 03/07/2023] [Indexed: 03/16/2023] Open
Abstract
This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of human embryos incubated for 2, 3, and 5 or more days. We trained and evaluated the model on an extensive and diverse dataset including 181,428 embryos from 22 IVF clinics across the world. To discriminate the transferred embryos with known outcome, we show areas under the receiver operating curve ranging from 0.621 to 0.707 depending on the day of transfer. Predictive performance increased over time and showed a strong correlation with morphokinetic parameters. The model's performance is equivalent to the KIDScore D3 model on day 3 embryos while it significantly surpasses the performance of KIDScore D5 v3 on day 5+ embryos. This model provides an analysis of time-lapse sequences without the need for user input, and provides a reliable method for ranking embryos for their likelihood of implantation, at both cleavage and blastocyst stages. This greatly improves embryo grading consistency and saves time compared to traditional embryo evaluation methods.
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12
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Cimadomo D, Chiappetta V, Innocenti F, Saturno G, Taggi M, Marconetto A, Casciani V, Albricci L, Maggiulli R, Coticchio G, Ahlström A, Berntsen J, Larman M, Borini A, Vaiarelli A, Ubaldi FM, Rienzi L. Towards Automation in IVF: Pre-Clinical Validation of a Deep Learning-Based Embryo Grading System during PGT-A Cycles. J Clin Med 2023; 12:1806. [PMID: 36902592 PMCID: PMC10002983 DOI: 10.3390/jcm12051806] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/13/2023] [Accepted: 02/21/2023] [Indexed: 02/26/2023] Open
Abstract
Preimplantation genetic testing for aneuploidies (PGT-A) is arguably the most effective embryo selection strategy. Nevertheless, it requires greater workload, costs, and expertise. Therefore, a quest towards user-friendly, non-invasive strategies is ongoing. Although insufficient to replace PGT-A, embryo morphological evaluation is significantly associated with embryonic competence, but scarcely reproducible. Recently, artificial intelligence-powered analyses have been proposed to objectify and automate image evaluations. iDAScore v1.0 is a deep-learning model based on a 3D convolutional neural network trained on time-lapse videos from implanted and non-implanted blastocysts. It is a decision support system for ranking blastocysts without manual input. This retrospective, pre-clinical, external validation included 3604 blastocysts and 808 euploid transfers from 1232 cycles. All blastocysts were retrospectively assessed through the iDAScore v1.0; therefore, it did not influence embryologists' decision-making process. iDAScore v1.0 was significantly associated with embryo morphology and competence, although AUCs for euploidy and live-birth prediction were 0.60 and 0.66, respectively, which is rather comparable to embryologists' performance. Nevertheless, iDAScore v1.0 is objective and reproducible, while embryologists' evaluations are not. In a retrospective simulation, iDAScore v1.0 would have ranked euploid blastocysts as top quality in 63% of cases with one or more euploid and aneuploid blastocysts, and it would have questioned embryologists' ranking in 48% of cases with two or more euploid blastocysts and one or more live birth. Therefore, iDAScore v1.0 may objectify embryologists' evaluations, but randomized controlled trials are required to assess its clinical value.
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Affiliation(s)
- Danilo Cimadomo
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | - Viviana Chiappetta
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | - Federica Innocenti
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | - Gaia Saturno
- Department of Biology and Biotechnology “Lazzaro Spallanzani”, University of Pavia, 27100 Pavia, Italy
| | - Marilena Taggi
- Department of Biology and Biotechnology “Lazzaro Spallanzani”, University of Pavia, 27100 Pavia, Italy
| | - Anabella Marconetto
- University Institute of Reproductive Medicine, National University of Cordoba, Cordoba 5187, Argentina
| | - Valentina Casciani
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | - Laura Albricci
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | - Roberta Maggiulli
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | | | | | | | - Mark Larman
- Vitrolife Sweden AB, 421 32 Göteborg, Sweden
| | | | - Alberto Vaiarelli
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | | | - Laura Rienzi
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
- Department of Biomolecular Sciences, University of Urbino “Carlo Bo”, 61029 Urbino, Italy
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