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Chow DJX, Tan TCY, Upadhya A, Lim M, Dholakia K, Dunning KR. Viewing early life without labels: optical approaches for imaging the early embryo†. Biol Reprod 2024; 110:1157-1174. [PMID: 38647415 PMCID: PMC11180623 DOI: 10.1093/biolre/ioae062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 03/26/2024] [Accepted: 04/18/2024] [Indexed: 04/25/2024] Open
Abstract
Embryo quality is an important determinant of successful implantation and a resultant live birth. Current clinical approaches for evaluating embryo quality rely on subjective morphology assessments or an invasive biopsy for genetic testing. However, both approaches can be inherently inaccurate and crucially, fail to improve the live birth rate following the transfer of in vitro produced embryos. Optical imaging offers a potential non-invasive and accurate avenue for assessing embryo viability. Recent advances in various label-free optical imaging approaches have garnered increased interest in the field of reproductive biology due to their ability to rapidly capture images at high resolution, delivering both morphological and molecular information. This burgeoning field holds immense potential for further development, with profound implications for clinical translation. Here, our review aims to: (1) describe the principles of various imaging systems, distinguishing between approaches that capture morphological and molecular information, (2) highlight the recent application of these technologies in the field of reproductive biology, and (3) assess their respective merits and limitations concerning the capacity to evaluate embryo quality. Additionally, the review summarizes challenges in the translation of optical imaging systems into routine clinical practice, providing recommendations for their future development. Finally, we identify suitable imaging approaches for interrogating the mechanisms underpinning successful embryo development.
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Affiliation(s)
- Darren J X Chow
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
| | - Tiffany C Y Tan
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
| | - Avinash Upadhya
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
- School of Biological Sciences, The University of Adelaide, Adelaide, Australia
| | - Megan Lim
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
- School of Biological Sciences, The University of Adelaide, Adelaide, Australia
| | - Kishan Dholakia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
- School of Biological Sciences, The University of Adelaide, Adelaide, Australia
- Scottish Universities Physics Alliance, School of Physics and Astronomy, University of St Andrews, St Andrews, United Kingdom
| | - Kylie R Dunning
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
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2
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Papamentzelopoulou MS, Prifti IN, Mavrogianni D, Tseva T, Soyhan N, Athanasiou A, Athanasiou A, Athanasiou A, Vogiatzi P, Konomos G, Loutradis D, Sakellariou M. Assessment of artificial intelligence model and manual morphokinetic annotation system as embryo grading methods for successful live birth prediction: a retrospective monocentric study. Reprod Biol Endocrinol 2024; 22:27. [PMID: 38443941 PMCID: PMC10913268 DOI: 10.1186/s12958-024-01198-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/22/2024] [Indexed: 03/07/2024] Open
Abstract
PURPOSE The introduction of the time-lapse monitoring system (TMS) and the development of predictive algorithms could contribute to the optimal embryos selection for transfer. Therefore, the present study aims at investigating the efficiency of KIDScore and iDAScore systems for blastocyst stage embryos in predicting live birth events. METHODS The present retrospective study was conducted in a private IVF Unit setting throughout a 10-month period from October 2021 to July 2022, and included the analysis of 429 embryos deriving from 91 IVF/ICSI cycles conducted due to infertility of various etiologies. Embryos incubated at the Embryoscope+ timelapse incubator were analyzed through the established scoring systems: KIDScore and iDAScore®. The main outcome measure was the comparison of the two scoring systems in terms of live birth prediction. Embryos with the higher scores at day 5 (KID5 score/iDA5 score) were transferred or cryopreserved for later use. RESULTS Embryos with high KID5 and iDA5 scores positively correlated with the probability of successful live birth, with KID5 score yielding a higher efficiency in predicting a successful reproductive outcome compared to a proportionally high iDA5 score. KID5 demonstrated conservative performance in successfully predicting live birth compared to iDA5 score, indicating that an efficient prediction can be either provided by a relatively lower KID5 score or a relatively higher iDA5 score. CONCLUSION The developed artificial intelligence tools should be implemented in clinical practice in conjunction with the conventional morphological assessment for the conduction of optimized embryo transfer in terms of a successful live birth.
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Affiliation(s)
- Myrto-Sotiria Papamentzelopoulou
- Molecular Biology Unit, Division of Human Reproduction, 1st Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, 80, Vasilissis Sofias Ave., Athens, 11528, Greece.
| | | | - Despoina Mavrogianni
- Molecular Biology Unit, Division of Human Reproduction, 1st Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, 80, Vasilissis Sofias Ave., Athens, 11528, Greece
| | - Thomais Tseva
- IVF Athens Reproduction Center V. Athanassiou, Maroussi, Greece
| | - Ntilay Soyhan
- IVF Athens Reproduction Center V. Athanassiou, Maroussi, Greece
| | - Aikaterini Athanasiou
- IVF Athens Reproduction Center V. Athanassiou, Maroussi, Greece
- HUG (Hôpitaux universitaires de Genève), Rue Gabrielle-Perret-Gentil 4, Genève 14, Genève, 1211, Switzerland
| | - Antonia Athanasiou
- IVF Athens Reproduction Center V. Athanassiou, Maroussi, Greece
- RHNe (Réseau hospitalier neuchâtelois), Chasseral 20, La Chaux-de-Fonds, 2303, Switzerland
| | - Adamantios Athanasiou
- IVF Athens Reproduction Center V. Athanassiou, Maroussi, Greece
- Department of Gynecology Oncology, Agios Savvas, General Anti-Cancer Hospital, Athens, Greece
| | - Paraskevi Vogiatzi
- IVF Athens Reproduction Center V. Athanassiou, Maroussi, Greece
- Andromed Health & Reproduction, Fertility Diagnostics Laboratory, Maroussi, Greece
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Goswami N, Winston N, Choi W, Lai NZE, Arcanjo RB, Chen X, Sobh N, Nowak RA, Anastasio MA, Popescu G. EVATOM: an optical, label-free, machine learning assisted embryo health assessment tool. Commun Biol 2024; 7:268. [PMID: 38443460 PMCID: PMC10915136 DOI: 10.1038/s42003-024-05960-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 02/22/2024] [Indexed: 03/07/2024] Open
Abstract
The combination of a good quality embryo and proper maternal health factors promise higher chances of a successful in vitro fertilization (IVF) procedure leading to clinical pregnancy and live birth. Of these two factors, selection of a good embryo is a controllable aspect. The current gold standard in clinical practice is visual assessment of an embryo based on its morphological appearance by trained embryologists. More recently, machine learning has been incorporated into embryo selection "packages". Here, we report EVATOM: a machine-learning assisted embryo health assessment tool utilizing an optical quantitative phase imaging technique called artificial confocal microscopy (ACM). We present a label-free nucleus detection method with, to the best of our knowledge, novel quantitative embryo health biomarkers. Two viability assessment models are presented for grading embryos into two classes: healthy/intermediate (H/I) or sick (S) class. The models achieve a weighted F1 score of 1.0 and 0.99 respectively on the in-distribution test set of 72 fixed embryos and a weighted F1 score of 0.9 and 0.95 respectively on the out-of-distribution test dataset of 19 time-instances from 8 live embryos.
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Affiliation(s)
- Neha Goswami
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
- Beckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Nicola Winston
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, University of Illinois at Chicago College of Medicine, Chicago, IL, 60612, USA
| | - Wonho Choi
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Nastasia Z E Lai
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Rachel B Arcanjo
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Animal Science, University of California, Davis, CA, 95616, USA
| | - Xi Chen
- Beckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- School of Applied and Engineering Physics, Cornell University, Ithaca, NY, 14850, USA
| | - Nahil Sobh
- NCSA Center for Artificial Intelligence Innovation, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Romana A Nowak
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Mark A Anastasio
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
- Beckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Gabriel Popescu
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Beckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
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He C, Karpavičiūtė N, Hariharan R, Lees L, Jacques C, Ferrand T, Chambost J, Wouters K, Malmsten J, Miller R, Zaninovic N, Vasconcelos F, Hickman C. Seeking arrangements: cell contact as a cleavage-stage biomarker. Reprod Biomed Online 2024; 48:103654. [PMID: 38246064 PMCID: PMC11139661 DOI: 10.1016/j.rbmo.2023.103654] [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: 06/15/2023] [Revised: 10/10/2023] [Accepted: 10/30/2023] [Indexed: 01/23/2024]
Abstract
RESEARCH QUESTION What can three-dimensional cell contact networks tell us about the developmental potential of cleavage-stage human embryos? DESIGN This pilot study was a retrospective analysis of two Embryoscope imaging datasets from two clinics. An artificial intelligence system was used to reconstruct the three-dimensional structure of embryos from 11-plane focal stacks. Networks of cell contacts were extracted from the resulting embryo three-dimensional models and each embryo's mean contacts per cell was computed. Unpaired t-tests and receiver operating characteristic curve analysis were used to statistically analyse mean cell contact outcomes. Cell contact networks from different embryos were compared with identical embryos with similar cell arrangements. RESULTS At t4, a higher mean number of contacts per cell was associated with greater rates of blastulation and blastocyst quality. No associations were found with biochemical pregnancy, live birth, miscarriage or ploidy. At t8, a higher mean number of contacts was associated with increased blastocyst quality, biochemical pregnancy and live birth. No associations were found with miscarriage or aneuploidy. Mean contacts at t4 weakly correlated with those at t8. Four-cell embryos fell into nine distinct cell arrangements; the five most common accounted for 97% of embryos. Eight-cell embryos, however, displayed a greater degree of variation with 59 distinct cell arrangements. CONCLUSIONS Evidence is provided for the clinical relevance of cleavage-stage cell arrangement in the human preimplantation embryo beyond the four-cell stage, which may improve selection techniques for day-3 transfers. This pilot study provides a strong case for further investigation into spatial biomarkers and three-dimensional morphokinetics.
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Affiliation(s)
- Chloe He
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London 43-45 Foley St, London, W1W 7TY, UK.; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK.; AI Team, Apricity, 14 Grays Inn Rd, London WC1 X 8HN, UK..
| | | | | | - Lilly Lees
- AI Team, Apricity, 14 Grays Inn Rd, London WC1 X 8HN, UK
| | | | | | | | - Koen Wouters
- Brussels IVF, University Hospital Brussels, Jette Bldg R, Laarbeeklaan 101 1090 Jette, Belgium, Brussels
| | - Jonas Malmsten
- Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, 1305 York Ave 6th floor, New York, NY 10021, USA
| | - Ryan Miller
- Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, 1305 York Ave 6th floor, New York, NY 10021, USA
| | - Nikica Zaninovic
- Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, 1305 York Ave 6th floor, New York, NY 10021, USA
| | - Francisco Vasconcelos
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London 43-45 Foley St, London, W1W 7TY, UK.; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK
| | - Cristina Hickman
- AI Team, Apricity, 14 Grays Inn Rd, London WC1 X 8HN, UK.; Institute of Reproductive and Developmental Biology, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0HS, UK
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5
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Güell E. Criteria for implementing artificial intelligence systems in reproductive medicine. Clin Exp Reprod Med 2024; 51:1-12. [PMID: 38035589 PMCID: PMC10914497 DOI: 10.5653/cerm.2023.06009] [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: 03/15/2023] [Accepted: 08/31/2023] [Indexed: 12/02/2023] Open
Abstract
This review article discusses the integration of artificial intelligence (AI) in assisted reproductive technology and provides key concepts to consider when introducing AI systems into reproductive medicine practices. The article highlights the various applications of AI in reproductive medicine and discusses whether to use commercial or in-house AI systems. This review also provides criteria for implementing new AI systems in the laboratory and discusses the factors that should be considered when introducing AI in the laboratory, including the user interface, scalability, training, support, follow-up, cost, ethics, and data quality. The article emphasises the importance of ethical considerations, data quality, and continuous algorithm updates to ensure the accuracy and safety of AI systems.
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Affiliation(s)
- Enric Güell
- CONSULTFIV, Valls, Spain
- Procrear, Reus, Spain
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6
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Hanassab S, Abbara A, Yeung AC, Voliotis M, Tsaneva-Atanasova K, Kelsey TW, Trew GH, Nelson SM, Heinis T, Dhillo WS. The prospect of artificial intelligence to personalize assisted reproductive technology. NPJ Digit Med 2024; 7:55. [PMID: 38429464 PMCID: PMC10907618 DOI: 10.1038/s41746-024-01006-x] [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: 01/25/2023] [Accepted: 01/10/2024] [Indexed: 03/03/2024] Open
Abstract
Infertility affects 1-in-6 couples, with repeated intensive cycles of assisted reproductive technology (ART) required by many to achieve a desired live birth. In ART, typically, clinicians and laboratory staff consider patient characteristics, previous treatment responses, and ongoing monitoring to determine treatment decisions. However, the reproducibility, weighting, and interpretation of these characteristics are contentious, and highly operator-dependent, resulting in considerable reliance on clinical experience. Artificial intelligence (AI) is ideally suited to handle, process, and analyze large, dynamic, temporal datasets with multiple intermediary outcomes that are generated during an ART cycle. Here, we review how AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART.
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Affiliation(s)
- Simon Hanassab
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
- Department of Computing, Imperial College London, London, UK
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK
| | - Ali Abbara
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Arthur C Yeung
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Margaritis Voliotis
- Department of Mathematics and Statistics, University of Exeter, Exeter, UK
- Living Systems Institute, University of Exeter, Exeter, UK
- EPSRC Hub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK
| | - Krasimira Tsaneva-Atanasova
- Department of Mathematics and Statistics, University of Exeter, Exeter, UK
- Living Systems Institute, University of Exeter, Exeter, UK
- EPSRC Hub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK
| | - Tom W Kelsey
- School of Computer Science, University of St Andrews, St Andrews, UK
| | - Geoffrey H Trew
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
- The Fertility Partnership, Oxford, UK
| | - Scott M Nelson
- The Fertility Partnership, Oxford, UK
- School of Medicine, University of Glasgow, Glasgow, UK
- Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Thomas Heinis
- Department of Computing, Imperial College London, London, UK
| | - Waljit S Dhillo
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK.
- Imperial College Healthcare NHS Trust, London, UK.
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Chien CW, Tang YA, Jeng SL, Pan HA, Sun HS. Blastocyst telomere length predicts successful implantation after frozen-thawed embryo transfer. Hum Reprod Open 2024; 2024:hoae012. [PMID: 38515829 PMCID: PMC10955253 DOI: 10.1093/hropen/hoae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 02/04/2024] [Indexed: 03/23/2024] Open
Abstract
STUDY QUESTION Do embryos with longer telomere length (TL) at the blastocyst stage have a higher capacity to survive after frozen-thawed embryo transfer (FET)? SUMMARY ANSWER Digitally estimated TL using low-pass whole genome sequencing (WGS) data from the preimplantation genetic testing for aneuploidy (PGT-A) process demonstrates that blastocyst TL is the most essential factor associated with likelihood of implantation. WHAT IS KNOWN ALREADY The lifetime TL is established in the early cleavage cycles following fertilization through a recombination-based lengthening mechanism and starts erosion beyond the blastocyst stage. In addition, a telomerase-mediated slow erosion of TL in human fetuses has been observed from a gestational age of 6-11 weeks. Finally, an abnormal shortening of telomeres is likely involved in embryo loss during early development. STUDY DESIGN SIZE DURATION Blastocyst samples were obtained from patients who underwent PGT-A and FET in an IVF center from March 2015 to May 2018. Digitally estimated mitochondrial copy number (mtCN) and TL were used to study associations with the implantation potential of each embryo. PARTICIPANTS/MATERIALS SETTING AND METHODS In total, 965 blastocysts from 232 cycles (164 patients) were available to investigate the biological and clinical relevance of TL. A WGS-based workflow was applied to determine the ploidy of each embryo. Data from low-pass WGS-PGT-A were used to estimate the mtCN and TL for each embryo. Single-variant and multi-variant logistic regression, decision tree, and random forest models were applied to study various factors in association with the implantation potential of each embryo. MAIN RESULTS AND THE ROLE OF CHANCE Of the 965 blastocysts originally available, only 216 underwent FET. While mtCN from the transferred embryos is significantly associated with the ploidy call of each embryo, mtCN has no role in impacting IVF outcomes after an embryo transfer in these women. The results indicate that mtCN is a marker of embryo aneuploidy. On the other hand, digitally estimated TL is the most prominent univariant factor and showed a significant positive association with pregnancy outcomes (P < 0.01, odds ratio 79.1). We combined several maternal and embryo parameters to study the joint effects on successful implantation. The machine learning models, namely decision tree and random forest, were trained and yielded classification accuracy of 0.82 and 0.91, respectively. Taken together, these results support the vital role of TL in governing implantation potential, perhaps through the ability to control embryo survival after transfer. LIMITATIONS REASONS FOR CAUTION The small sample size limits our study as only 216 blastocysts were transferred. The number was further reduced to 153 blastocysts, where pregnancy outcomes could be accurately traced. The other limitation of this study is that all data were collected from a single IVF center. The uniform and controlled operation of IVF cycles in a single center may cause selection bias. WIDER IMPLICATIONS OF THE FINDINGS We present novel findings to show that digitally estimated TL at the blastocyst stage is a predictor of pregnancy capacity after a FET cycle. As elective single-embryo transfer has become the mainstream direction in reproductive medicine, prioritizing embryos based on their implantation potential is crucial for clinical infertility treatment in order to reduce twin pregnancy rate and the time to pregnancy in an IVF center. The AI-powered, random forest prediction model established in this study thus provides a way to improve clinical practice and optimize the chances for people with fertility problems to achieve parenthood. STUDY FUNDING/COMPETING INTERESTS This study was supported by a grant from the National Science and Technology Council, Taiwan (MOST 108-2321-B-006-013 -). There were no competing interests. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- Chun-Wei Chien
- Center for Genomic Medicine, Innovation Headquarters, National Cheng Kung University, Tainan, Taiwan
| | - Yen-An Tang
- Center for Genomic Medicine, Innovation Headquarters, National Cheng Kung University, Tainan, Taiwan
- Institute of Molecular Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Shuen-Lin Jeng
- Department of Statistics, Institute of Data Science, National Cheng Kung University, Tainan, Taiwan
- Center for Innovative FinTech Business Models, National Cheng Kung University, Tainan, Taiwan
| | - Hsien-An Pan
- IVF center, An-An Women and Children Clinic, Tainan, Taiwan
- Department of Obstetrics and Gynecology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - H Sunny Sun
- Center for Genomic Medicine, Innovation Headquarters, National Cheng Kung University, Tainan, Taiwan
- Institute of Molecular Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
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8
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Luong TMT, Le NQK. Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine. J Assist Reprod Genet 2024; 41:239-252. [PMID: 37880512 PMCID: PMC10894798 DOI: 10.1007/s10815-023-02973-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] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/13/2023] [Indexed: 10/27/2023] Open
Abstract
With the rising demand for in vitro fertilization (IVF) cycles, there is a growing need for innovative techniques to optimize procedure outcomes. One such technique is time-lapse system (TLS) for embryo incubation, which minimizes environmental changes in the embryo culture process. TLS also significantly advances predicting embryo quality, a crucial determinant of IVF cycle success. However, the current subjective nature of embryo assessments is due to inter- and intra-observer subjectivity, resulting in highly variable results. To address this challenge, reproductive medicine has gradually turned to artificial intelligence (AI) to establish a standardized and objective approach, aiming to achieve higher success rates. Extensive research is underway investigating the utilization of AI in TLS to predict multiple outcomes. These studies explore the application of popular AI algorithms, their specific implementations, and the achieved advancements in TLS. This review aims to provide an overview of the advances in AI algorithms and their particular applications within the context of TLS and the potential challenges and opportunities for further advancements in reproductive medicine.
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Affiliation(s)
- Thi-My-Trang Luong
- International Master Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei, 110, Taiwan
| | - Nguyen Quoc Khanh Le
- AIBioMed Research Group, Taipei Medical University, Taipei, 110, Taiwan.
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan.
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 110, Taiwan.
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, 110, Taiwan.
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9
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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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Cimadomo D, Forman EJ, Morbeck DE, Liperis G, Miller K, Zaninovic N, Sturmey R, Rienzi L. Day7 and low-quality blastocysts: opt in or opt out? A dilemma with important clinical implications. Fertil Steril 2023; 120:1151-1159. [PMID: 38008467 DOI: 10.1016/j.fertnstert.2023.10.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/28/2023]
Affiliation(s)
| | - Eric J Forman
- Columbia University Fertility Center, New York, New York
| | - Dean E Morbeck
- Morbeck Consulting Ltd., Auckland, New Zealand; Department of Obstetrics and Gynecology, Monash University, Melbourne, Australia
| | - Georgios Liperis
- Westmead Fertility Centre, Institute of Reproductive Medicine, University of Sydney, Westmead, New South Wales, Australia
| | | | - Nikica Zaninovic
- The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York
| | - Roger Sturmey
- Biomedical Institute for Multimorbidity, Hull York Medical School, University of Hull, Hull, United Kingdom
| | - Laura Rienzi
- IVIRMA Global Research Alliance, GENERA, Rome, Italy; Department of Biomolecular Sciences, University of Urbino "Carlo Bo", Urbino, Italy.
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11
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Wang S, Chen L, Sun H. Interpretable artificial intelligence-assisted embryo selection improved single-blastocyst transfer outcomes: a prospective cohort study. Reprod Biomed Online 2023; 47:103371. [PMID: 37839212 DOI: 10.1016/j.rbmo.2023.103371] [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: 05/16/2023] [Revised: 08/01/2023] [Accepted: 08/23/2023] [Indexed: 10/17/2023]
Abstract
RESEARCH QUESTION What is the pregnancy and neonatal outcomes of an interpretable artificial intelligence (AI) model for embryo selection in a prospective clinical trial? DESIGN This single-centre prospective cohort study was carried out from October 2021 to March 2022. A total of 330 eligible patients were assigned to their preferred groups, with 250 patients undergoing a fresh single-blastocyst transfer cycle after the exclusion criteria had been applied. For the AI-assisted group (AAG), embryologists selected the embryos for transfer based on the ranking recommendations provided by an interpretable AI system, while with the manual group, embryologists used the Gardner grading system to make their decisions. RESULTS The implantation rate was significantly higher in the AAG than the manual group (80.87% versus 68.15%, P = 0.022). No significant difference was found in terms of monozygotic twin rate, miscarriage rate, live birth rate and ectopic pregnancy rate between the groups. Furthermore, there was no significant difference in terms of neonatal outcomes, including gestational weeks, premature birth rate, birth height, birthweight, sex ratio at birth and newborn malformation rate. The consensus rate between the AI and retrospective analysis by the embryologists was significantly higher for good-quality embryos (i.e. grade 4BB or higher) versus poor-quality embryos (i.e. less than 4BB) (84.71% versus 25%, P < 0.001). CONCLUSIONS These prospective trial results suggest that the proposed AI system could effectively help embryologists to improve the implantation rate with single-blastocyst transfer compared with traditional manual evaluation methods.
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Affiliation(s)
- Shanshan Wang
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Lei Chen
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Haixiang Sun
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
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12
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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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13
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Chiappetta V, Innocenti F, Coticchio G, Ahlström A, Albricci L, Badajoz V, Hebles M, Gallardo M, Benini F, Canosa S, Kumpošt J, Milton K, Montanino Oliva D, Maggiulli R, Rienzi L, Cimadomo D. Discard or not discard, that is the question: an international survey across 117 embryologists on the clinical management of borderline quality blastocysts. Hum Reprod 2023; 38:1901-1909. [PMID: 37649342 DOI: 10.1093/humrep/dead174] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/02/2023] [Indexed: 09/01/2023] Open
Abstract
STUDY QUESTION Do embryologists from different European countries agree on embryo disposition decisions ('use' or 'discard') about Day 7 (>144 h post-insemination) and/or low-quality blastocysts (LQB; SUMMARY ANSWER The prevalence of 'discard' answers was 38.7%; nevertheless, embryologists' agreement was overall just fair (Fleiss-k = 0.26). WHAT IS KNOWN ALREADY The utilization of LQBs and adoption of culture beyond 144 h post-insemination is increasing worldwide. Although morphology and morphokinetics are associated with embryo developmental competence, previous studies demonstrated significant interobserver variability among embryologists regarding embryo quality assessment and disposition decisions for borderline quality blastocysts. STUDY DESIGN, SIZE, DURATION An anonymous survey was run in a large network of IVF centers. A total of 117 embryologists from 6 European countries and 29 IVF centers filled in the survey. Randomly selected anonymous time-lapse videos of 50 Day 7 and/or LQB whole embryo preimplantation development were assessed by the embryologists. The key information on patients/cycles was provided along with each video. All cycles entailed preimplantation genetic testing for aneuploidies. Each embryologist specified whether he/she would have discarded or used ('transfer-fresh'/'cryopreserve'/'biopsy') any embryo. Inter-rater agreement was measured with Fleiss-k. PARTICIPANTS/MATERIALS, SETTING, METHODS Examiners were asked about their years of experience, center location, average number of cycles and average maternal age, number of colleagues, and use of time-lapse incubators at their centers. All participants were blinded to artificial intelligence (AI) scores generated by two commercially available software packages, chromosomal diagnosis (all blastocysts were tested for aneuploidies), and clinical outcomes after vitrified-warmed euploid single blastocyst transfer. These data were known only by one embryologist not involved in the survey. MAIN RESULTS AND THE ROLE OF CHANCE Participants were Italian (40%, N = 47), Spanish (24%, N = 28), Portuguese (5%, N = 6), Czech (5%, N = 6), Swedish (23%, N = 27), and Icelandic (3%, N = 3). In total, 2263 (38.7%) 'discard' and 3587 (61.3%) 'use' decisions were recorded. Czech, Portuguese, and Italian embryologists expressed lower 'discard' decision rates (mean ± SD 17 ± 7%, range 8-24%; 23 ± 14% range 4-46%; and 27 ± 18% range 2-72%, respectively), while Spanish gave intermediate (37 ± 16% range 4-66%) and Nordic gave higher (67 ± 11% range 40-90%) rates. The prevalence of 'discard' answers was 38.7% out of 5850 choices (mean per embryologist: 39 ± 23% range 2-90%). Only embryologists' country and IVF group were associated with this rate. Overall agreement among embryologists was fair (Fleiss-k = 0.26). The prevalence of 'discard' responses per embryo was 37 ± 24% (range 2-87%). Only the number of sibling blastocysts influenced this rate (i.e. the larger the cohort, the higher the inclination to 'discard'). No difference was shown for the two scores between euploid and aneuploid borderline quality blastocysts, while the embryologists were, by chance, more prone to 'discard' the latter (28.3 ± 21% range 9-71% versus 41.6 ± 24.8% range 2-87%, respectively). LIMITATIONS, REASONS FOR CAUTION The survey included only private IVF clinics located in Europe. Moreover, a key variable is missing, namely patients' access to care. Indeed, all embryologists involved in the survey were part of the same network of private IVF clinics, while the embryo disposition decisions might be different in a public setting. WIDER IMPLICATIONS OF THE FINDINGS Decision-making by European embryologists regarding Day 7 embryos or LQBs is inconsistent with putative clinical consequences, especially in patients with low prognosis. Although the embryologists could make decisions independent from their local regulations, their mindset and clinical background influenced their choices. In the future, AI tools should be trained to assess borderline quality embryos and empowered with cost-effectiveness information to support embryologists' decisions with more objective assessments. STUDY FUNDING/COMPETING INTEREST(S) No external funding was obtained for this study. The authors have no conflict of interest to declare. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- Viviana Chiappetta
- IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
| | - Federica Innocenti
- IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
| | | | | | - Laura Albricci
- IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
| | | | - Maria Hebles
- IVIRMA Global Research Alliance, GINEMED, Sevilla, Spain
| | | | | | | | - Jiří Kumpošt
- IVIRMA Global Research Alliance, FERTICARE, Prague, Czech Republic
| | - Katarina Milton
- IVIRMA Global Research Alliance, CARL VON LINNÈ KLINIKEN, Uppsala, Sweden
| | - Diletta Montanino Oliva
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Pavia, Italy
| | - Roberta Maggiulli
- IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
| | - Laura Rienzi
- IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
- Department of Biomolecular Sciences, University of Urbino "Carlo Bo", Urbino, Italy
| | - Danilo Cimadomo
- IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
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Si K, Huang B, Jin L. Application of artificial intelligence in gametes and embryos selection. HUM FERTIL 2023; 26:757-777. [PMID: 37705466 DOI: 10.1080/14647273.2023.2256980] [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: 02/28/2023] [Accepted: 07/22/2023] [Indexed: 09/15/2023]
Abstract
Gamete and embryo quality are critical to the success rate of Assisted Reproductive Technology (ART) cycles, but there remains a lack of methods to accurately measure the quality of sperm, oocytes and embryos. The ability of Artificial Intelligence (AI) technology to analyze large amounts of data, especially video and images, is particularly useful in gamete and embryo assessment and selection. The well-trained model has fast calculation speed and high accuracy, which can help embryologists to perform more objective gamete and embryo selection. Various artificial intelligence models have been developed for gamete and embryo assessment, some of which exhibit good performance. In this review, we summarize the latest applications of AI technology in semen analysis, as well as selection for sperm, oocyte and embryo, and discuss the existing problems and development directions of artificial intelligence in this field.
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Affiliation(s)
- Keyi Si
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Bo Huang
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Lei Jin
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
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15
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Wang J, Guo Y, Zhang N, Li T. Research progress of time-lapse imaging technology and embryonic development potential: A review. Medicine (Baltimore) 2023; 102:e35203. [PMID: 37746957 PMCID: PMC10519478 DOI: 10.1097/md.0000000000035203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Cultivation and selection of high-quality human embryos are critical for the success of in vitro fertilization-embryo transfer. Time-lapse imaging technology (TLI) provides a stable culture environment for embryos, which can continuously observe and record the development process of early embryos, so that doctors can record embryo development time parameters more accurately. In this study, we review the current observation and research on the main embryo dynamics parameters in TLI and discusses their significance and development for embryo development potential. To analysis and summary, the application and research situation of TLI, we searched PubMed, Web of Science, and China National Knowledge Infrastructure, using TLI, embryo dynamics parameters, embryo development potential as Keywords, cited 50 out of the initial 89 selected literatures and summarized. With comparative analysis and research, we found that the embryo dynamic parameters provided by TLI has been intensively studied in clinical empirical and observational research, extensive experimental data verified its effectiveness and advantages in embryo development potential assessment. TLI provides technical support of embryo dynamic parameters, which may become the quantitative indicators for superior embryos and pregnancy prediction as well. Existing studies have shown that certain kinetic parameters provided by TLI culture can predict embryo implantation, but no parameter has been confirmed as the absolute correlation biological indicators yet. In this review we believe that further research is needed to verify these preliminary and sometimes contradictory results, and explore the predictive significance of various embryo kinetic parameters relying on TLI technology for embryo development potential.
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Affiliation(s)
- JinLuan Wang
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ying Guo
- Reproductive and Genetic Center, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China
| | - Ning Zhang
- Reproductive and Genetic Center, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China
| | - TingTing Li
- Shandong University of Traditional Chinese Medicine, Jinan, China
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16
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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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17
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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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18
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Curti PDF, Selli A, Pinto DL, Merlos-Ruiz A, Balieiro JCDC, Ventura RV. Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview. Anim Reprod 2023; 20:e20230077. [PMID: 37700909 PMCID: PMC10494883 DOI: 10.1590/1984-3143-ar2023-0077] [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: 05/22/2023] [Accepted: 07/10/2023] [Indexed: 09/14/2023] Open
Abstract
Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations.
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Affiliation(s)
- Paula de Freitas Curti
- Departamento de Nutrição e Produção Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, Pirassununga, SP, Brasil
| | - Alana Selli
- Departamento de Nutrição e Produção Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, Pirassununga, SP, Brasil
| | - Diógenes Lodi Pinto
- Departamento de Nutrição e Produção Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, Pirassununga, SP, Brasil
| | - Alexandre Merlos-Ruiz
- Departamento de Nutrição e Produção Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, Pirassununga, SP, Brasil
| | - Julio Cesar de Carvalho Balieiro
- Departamento de Nutrição e Produção Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, Pirassununga, SP, Brasil
| | - Ricardo Vieira Ventura
- Departamento de Nutrição e Produção Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, Pirassununga, SP, Brasil
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Danardono GB, Handayani N, Louis CM, Polim AA, Sirait B, Periastiningrum G, Afadlal S, Boediono A, Sini I. Embryo ploidy status classification through computer-assisted morphology assessment. AJOG GLOBAL REPORTS 2023; 3:100209. [PMID: 37645653 PMCID: PMC10461251 DOI: 10.1016/j.xagr.2023.100209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Preimplantation genetic testing for aneuploidy has been proven to be effective in determining the embryo's chromosomal or ploidy status. The test requires a biopsy of embryonic cells on day 3, 5, or 6 from which complete information on the chromosomes would be obtained. The main drawbacks of preimplantation genetic testing for aneuploidy include its relatively invasive approach and the lack of research studies on the long-term effects of preimplantation genetic testing for aneuploidy. OBJECTIVE Computer-assisted predictive modeling through machine learning and deep learning algorithms has been proposed to minimize the use of invasive preimplantation genetic testing for aneuploidy. The capability to predict morphologic characteristics of embryo ploidy status creates a meaningful support system for decision-making before further treatment. STUDY DESIGN Image processing is a component in developing a predictive model specialized in image classification through which a model is able to differentiate images based on unique features. Image processing is obtained through image augmentation to capture segmented embryos and perform feature extraction. Furthermore, multiple machine learning and deep learning algorithms were used to create prediction-based modeling, and all of the prediction models undergo similar model performance assessments to determine the best model prediction algorithm. RESULTS An efficient artificial intelligence model that can predict embryo ploidy status was developed using image processing through a histogram of oriented gradient and then followed by principal component analysis. The gradient boosting algorithm showed an advantage against other algorithms and yielded an accuracy of 0.74, an aneuploid precision of 0.83, and an aneuploid predictive value (recall) of 0.84. CONCLUSION This research study proved that machine-assisted technology perceives the embryo differently than human observation and determined that further research on in vitro fertilization is needed. The study finding serves as a basis for developing a better computer-assisted prediction model.
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Affiliation(s)
- Gunawan Bondan Danardono
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
| | - Nining Handayani
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
| | - Claudio Michael Louis
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
| | - Arie Adrianus Polim
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Morula IVF Jakarta Clinic, Jakarta, Indonesia (Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Department of Obstetrics and Gynecology, School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia (Dr Polim)
| | - Batara Sirait
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Morula IVF Jakarta Clinic, Jakarta, Indonesia (Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Faculty of Medicine, Department of Obstetrics and Gynaecology, Universitas Kristen Indonesia, Jakarta, Indonesia (Dr Sirait)
| | - Gusti Periastiningrum
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Morula IVF Jakarta Clinic, Jakarta, Indonesia (Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
| | - Szeifoul Afadlal
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Morula IVF Jakarta Clinic, Jakarta, Indonesia (Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
| | - Arief Boediono
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Morula IVF Jakarta Clinic, Jakarta, Indonesia (Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Department of Anatomy, Physiology, and Pharmacology, Bogor Agricultural Institute University, Bogor, Indonesia (Dr Boediono)
| | - Ivan Sini
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Morula IVF Jakarta Clinic, Jakarta, Indonesia (Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
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Baumgartner C, Rittner L, Deserno TM. Machine and Deep Learning Dominate Recent Innovations in Sensors, Signals and Imaging Informatics. Yearb Med Inform 2023; 32:282-285. [PMID: 38147870 PMCID: PMC10751153 DOI: 10.1055/s-0043-1768743] [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] [Indexed: 12/28/2023] Open
Abstract
OBJECTIVES This review presents research papers highlighting notable developments and trends in sensors, signals, and imaging informatics (SSII) in 2022. METHOD We performed a bibliographic search in PubMed combining Medical Subject Heading (MeSH) terms and keywords to create particular queries for sensors, signals, and imaging informatics. Only papers published in journals containing greater than three articles in the search query were considered. Using a three-point Likert scale (1 = not include, 2 = perhaps include, 3 = include), we reviewed the titles and abstracts of all database results. Only articles that scored three times Likert scale 3, or two times Likert scale 3, and one time Likert scale 2 were considered for full paper review. On this pre-selection, only papers with a total of at least eight points of the three section co-editors were considered for external review. Based on the external reviewers, we selected the top two papers representing significant research in SSII. RESULTS Among the 469 returned papers published in 2022 in the various areas of SSII, 90, 31, and 348 papers for sensors, signals, and imaging informatics, and then, the full review process selected the two best papers. From the 469 papers, the section co-editors identified 29 candidate papers with at least 8 Likert points in total, of which 9 were nominated as the best contributions after a full paper assessment. Five external reviewers evaluated the nominated papers, and the two highest-scoring papers were selected based on the overall scores of all external reviewers. A consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board finally approved the nominated papers. Machine and deep learning-based techniques continue to be the dominant theme in this field. CONCLUSIONS Sensors, signals, and imaging informatics is a dynamic field of intensive research with increasing practical applications to support medical decision-making on a personalized basis.
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Affiliation(s)
- Christian Baumgartner
- Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Austria
| | - Leticia Rittner
- Medical Imaging Computing Lab (MICLab), School of Electrical and Computer Engineering University of Campinas, Brazil
| | - Thomas M. Deserno
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
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21
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Goswami N, Winston N, Choi W, Lai NZE, Arcanjo RB, Chen X, Sobh N, Nowak RA, Anastasio MA, Popescu G. Machine learning assisted health viability assay for mouse embryos with artificial confocal microscopy (ACM). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.30.550591. [PMID: 37547014 PMCID: PMC10402120 DOI: 10.1101/2023.07.30.550591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
The combination of a good quality embryo and proper maternal health factors promise higher chances of a successful in vitro fertilization (IVF) procedure leading to clinical pregnancy and live birth. Of these two factors, selection of a good embryo is a controllable aspect. The current gold standard in clinical practice is visual assessment of an embryo based on its morphological appearance by trained embryologists. More recently, machine learning has been incorporated into embryo selection "packages". Here, we report a machine-learning assisted embryo health assessment tool utilizing a quantitative phase imaging technique called artificial confocal microscopy (ACM). We present a label-free nucleus detection method with novel quantitative embryo health biomarkers. Two viability assessment models are presented for grading embryos into two classes: healthy/intermediate (H/I) or sick (S) class. The models achieve a weighted F1 score of 1.0 and 0.99 respectively on the in-distribution test set of 72 fixed embryos and a weighted F1 score of 0.9 and 0.95 respectively on the out-of-distribution test dataset of 19 time-instances from 8 live embryos.
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22
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Joo K, Nemes A, Dudas B, Berkes-Bara E, Murber A, Urbancsek J, Fancsovits P. The importance of cytoplasmic strings during early human embryonic development. Front Cell Dev Biol 2023; 11:1177279. [PMID: 37497477 PMCID: PMC10366360 DOI: 10.3389/fcell.2023.1177279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 06/19/2023] [Indexed: 07/28/2023] Open
Abstract
Objectives: During human in vitro fertilisation (IVF) treatments, embryologists attempt to select the most viable embryos for embryo transfer (ET). Previously, embryos were evaluated based on light microscopic morphological parameters. However, this is currently accomplished by morphokinetic analysis of time-lapse recordings. This technique provides us the opportunity to observe cytoplasmic strings at the blastocyst stage. The aim of this work was to examine the relationship between the presence of cytoplasmic strings (CS) and the embryo viability in human in vitro fertilised embryos. Study design: Herein, we present an evaluation of the morphokinetic data on the development of embryos obtained during IVF treatments performed at the Division of Assisted Reproduction between December 2020 and March 2021. The dynamics of embryo development, embryo morphology, and morphokinetic scores generated by a time-lapse system were compared between the presence of cytoplasmic strings (CS+) and their absence (CS-) at the blastocyst stage. Results: The development of 208 embryos from 78 patients was examined. Moreover, 81.2% of the embryos had CS in the blastocyst stage; 77% of CS existed in embryos created by conventional IVF, while 86% of CS existed in embryos fertilised by intracytoplasmic sperm injection (ICSI) (p = 0.08). A greater number of CS+ embryos developed into a higher quality blastocyst (52.1% vs. 20.5%, p = 0.02). The morphokinetic score values characterising the development of embryos, such as Known Implantation Data Score (KIDScore) and Intelligent Data Analysis (iDAScore), were higher in CS+ groups (KID: 6.1 ± 2.1 vs. 4.7 ± 2.07; iDA: 8.0 ± 1.9 vs. 6.8 ± 2.3, p < 0.01). The dynamics of the early embryo development were similar between the two groups; however, CS+ embryos reached the blastocyst stage significantly earlier (tB: 103.9 h vs. tB: 107.6 h; p = 0.001). Conclusion: Based on our results, the number of embryos with cytoplasmic strings was higher than that without cytoplasmic strings, and its presence is not related to the fertilisation method. These embryos reached the blastocyst stage earlier, and their morphokinetic (KIDScore and iDAScore) parameters were better. All these results suggest that the presence of CS indicates higher embryo viability. The examination of this feature may help us make decisions about the embryos with higher implantation potential.
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23
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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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Towards Automation in IVF: Pre-Clinical Validation of a Deep Learning-Based Embryo Grading System during PGT-A Cycles. J Clin Med 2023; 12:jcm12051806. [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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26
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Duval A, Nogueira D, Dissler N, Maskani Filali M, Delestro Matos F, Chansel-Debordeaux L, Ferrer-Buitrago M, Ferrer E, Antequera V, Ruiz-Jorro M, Papaxanthos A, Ouchchane H, Keppi B, Prima PY, Regnier-Vigouroux G, Trebesses L, Geoffroy-Siraudin C, Zaragoza S, Scalici E, Sanguinet P, Cassagnard N, Ozanon C, De La Fuente A, Gómez E, Gervoise Boyer M, Boyer P, Ricciarelli E, Pollet-Villard X, Boussommier-Calleja A. A hybrid artificial intelligence model leverages multi-centric clinical data to improve fetal heart rate pregnancy prediction across time-lapse systems. Hum Reprod 2023; 38:596-608. [PMID: 36763673 PMCID: PMC10068266 DOI: 10.1093/humrep/dead023] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 01/10/2023] [Indexed: 02/12/2023] Open
Abstract
STUDY QUESTION Can artificial intelligence (AI) algorithms developed to assist embryologists in evaluating embryo morphokinetics be enriched with multi-centric clinical data to better predict clinical pregnancy outcome? SUMMARY ANSWER Training algorithms on multi-centric clinical data significantly increased AUC compared to algorithms that only analyzed the time-lapse system (TLS) videos. WHAT IS KNOWN ALREADY Several AI-based algorithms have been developed to predict pregnancy, most of them based only on analysis of the time-lapse recording of embryo development. It remains unclear, however, whether considering numerous clinical features can improve the predictive performances of time-lapse based embryo evaluation. STUDY DESIGN, SIZE, DURATION A dataset of 9986 embryos (95.60% known clinical pregnancy outcome, 32.47% frozen transfers) from 5226 patients from 14 European fertility centers (in two countries) recorded with three different TLS was used to train and validate the algorithms. A total of 31 clinical factors were collected. A separate test set (447 videos) was used to compare performances between embryologists and the algorithm. PARTICIPANTS/MATERIALS, SETTING, METHODS Clinical pregnancy (defined as a pregnancy leading to a fetal heartbeat) outcome was first predicted using a 3D convolutional neural network that analyzed videos of the embryonic development up to 2 or 3 days of development (33% of the database) or up to 5 or 6 days of development (67% of the database). The output video score was then fed as input alongside clinical features to a gradient boosting algorithm that generated a second score corresponding to the hybrid model. AUC was computed across 7-fold of the validation dataset for both models. These predictions were compared to those of 13 senior embryologists made on the test dataset. MAIN RESULTS AND THE ROLE OF CHANCE The average AUC of the hybrid model across all 7-fold was significantly higher than that of the video model (0.727 versus 0.684, respectively, P = 0.015; Wilcoxon test). A SHapley Additive exPlanations (SHAP) analysis of the hybrid model showed that the six first most important features to predict pregnancy were morphokinetics of the embryo (video score), oocyte age, total gonadotrophin dose intake, number of embryos generated, number of oocytes retrieved, and endometrium thickness. The hybrid model was shown to be superior to embryologists with respect to different metrics, including the balanced accuracy (P ≤ 0.003; Wilcoxon test). The likelihood of pregnancy was linearly linked to the hybrid score, with increasing odds ratio (maximum P-value = 0.001), demonstrating the ranking capacity of the model. Training individual hybrid models did not improve predictive performance. A clinic hold-out experiment was conducted and resulted in AUCs ranging between 0.63 and 0.73. Performance of the hybrid model did not vary between TLS or between subgroups of embryos transferred at different days of embryonic development. The hybrid model did fare better for patients older than 35 years (P < 0.001; Mann-Whitney test), and for fresh transfers (P < 0.001; Mann-Whitney test). LIMITATIONS, REASONS FOR CAUTION Participant centers were located in two countries, thus limiting the generalization of our conclusion to wider subpopulations of patients. Not all clinical features were available for all embryos, thus limiting the performances of the hybrid model in some instances. WIDER IMPLICATIONS OF THE FINDINGS Our study suggests that considering clinical data improves pregnancy predictive performances and that there is no need to retrain algorithms at the clinic level unless they follow strikingly different practices. This study characterizes a versatile AI algorithm with similar performance on different time-lapse microscopes and on embryos transferred at different development stages. It can also help with patients of different ages and protocols used but with varying performances, presumably because the task of predicting fetal heartbeat becomes more or less hard depending on the clinical context. This AI model can be made widely available and can help embryologists in a wide range of clinical scenarios to standardize their practices. STUDY FUNDING/COMPETING INTEREST(S) Funding for the study was provided by ImVitro with grant funding received in part from BPIFrance (Bourse French Tech Emergence (DOS0106572/00), Paris Innovation Amorçage (DOS0132841/00), and Aide au Développement DeepTech (DOS0152872/00)). A.B.-C. is a co-owner of, and holds stocks in, ImVitro SAS. A.B.-C. and F.D.M. hold a patent for 'Devices and processes for machine learning prediction of in vitro fertilization' (EP20305914.2). A.D., N.D., M.M.F., and F.D.M. are or have been employees of ImVitro and have been granted stock options. X.P.-V. has been paid as a consultant to ImVitro and has been granted stocks options of ImVitro. L.C.-D. and C.G.-S. have undertaken paid consultancy for ImVitro SAS. The remaining authors have no conflicts to declare. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
| | - D Nogueira
- INOVIE Fertilité, Institut de Fertilité La Croix Du Sud, Toulouse, France
- Art Fertility Clinics, IVF laboratory, Abu Dhabi, United Arab Emirate
| | | | | | | | - L Chansel-Debordeaux
- Service de la biologie et de la reproduction et CECOS, CHU Bordeaux Groupe Hospitalier Pellegrin, Bordeaux, France
| | - M Ferrer-Buitrago
- Crea Centro Médico de Fertilidad y Reproducción Asistida, Valencia, Spain
| | - E Ferrer
- Crea Centro Médico de Fertilidad y Reproducción Asistida, Valencia, Spain
| | - V Antequera
- Crea Centro Médico de Fertilidad y Reproducción Asistida, Valencia, Spain
| | - M Ruiz-Jorro
- Crea Centro Médico de Fertilidad y Reproducción Asistida, Valencia, Spain
| | - A Papaxanthos
- Service de la biologie et de la reproduction et CECOS, CHU Bordeaux Groupe Hospitalier Pellegrin, Bordeaux, France
| | - H Ouchchane
- INOVIE Fertilité, Gen-Bio, Clermont-Ferrand, France
| | - B Keppi
- INOVIE Fertilité, Gen-Bio, Clermont-Ferrand, France
| | - P-Y Prima
- Laboratoire FIV PMAtlantique - Clinique Santé Atlantique, Nantes, France
| | | | | | - C Geoffroy-Siraudin
- Hopital Saint Joseph, Service Médicine et Biologie de la Reproduction, Marseille, France
| | - S Zaragoza
- INOVIE Fertilité, Bioaxiome, Avignon, France
| | - E Scalici
- INOVIE Fertilité, Bioaxiome, Avignon, France
| | - P Sanguinet
- INOVIE Fertilité, LaboSud, Montpellier, France
| | - N Cassagnard
- INOVIE Fertilité, Institut de Fertilité La Croix Du Sud, Toulouse, France
| | - C Ozanon
- Clinique Hôtel Privé Natecia, Centre Assistance Médicale à la Procréation, Lyon, France
| | | | - E Gómez
- Next Fertility, Murcia, Spain
| | - M Gervoise Boyer
- Hopital Saint Joseph, Service Médicine et Biologie de la Reproduction, Marseille, France
| | - P Boyer
- Hopital Saint Joseph, Service Médicine et Biologie de la Reproduction, Marseille, France
| | | | - X Pollet-Villard
- Nataliance, Centre Assistance Médicale à la Procréation, Saran, France
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Vani V, Vasan SS, Adiga SK, Varsha SR, Seshagiri PB. Molecular regulators of human blastocyst development and hatching: Their significance in implantation and pregnancy outcome. Am J Reprod Immunol 2023; 89:e13635. [PMID: 36254379 DOI: 10.1111/aji.13635] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 09/10/2022] [Accepted: 10/04/2022] [Indexed: 02/01/2023] Open
Abstract
In humans, blastocyst hatching and implantation events are two sequential, critically linked and rate-limiting events for a prospective pregnancy. These events are regulated by embryo-endometrium derived molecular factors which include hormones, growth factors, cytokines, immune-modulators, cell adhesion molecules and proteases. Due to poor viability of blastocysts, they fail to hatch and implant, leading to a low 'Live Birth Rates', majorly contributing to infertility. Here, embryo-derived biomarkers analysis plays a key role to assess potential biological viability of blastocysts which are capable of implantation and prospective pregnancy. Thus far, embryo-derived biomarkers examined are mostly immune-modulators which are thought to be associated with blastocyst development-implantation and progression of pregnancy, leading to live births. There is an urgent need to develop a quantitative and a reliable non-invasive approach aiding embryo selection for elective single embryo transfer and to minimize recurrent pregnancy loss and multiple pregnancies. In this article, we provide a comprehensive review on our current knowledge and understanding of potential embryo-derived molecular regulators, that is, biomarkers, of development of human blastocysts, their hatching and implantation. We discuss their potential implications in the assessment of blastocyst implantation potential and pregnancy outcome in terms of live births in humans.
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Affiliation(s)
- Venkatappa Vani
- Indian Institute of Science, Department of Molecular Reproduction, Development and Genetics, Sir C.V. Raman Road, Bangalore, Karnataka, India
| | | | - Satish K Adiga
- Kasturba Medical College, Department of Clinical Embryology, Manipal, Karnataka, India
| | | | - Polani B Seshagiri
- Indian Institute of Science, Department of Molecular Reproduction, Development and Genetics, Sir C.V. Raman Road, Bangalore, Karnataka, India
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Curchoe CL. Proceedings of the first world conference on AI in fertility. J Assist Reprod Genet 2023; 40:215-222. [PMID: 36598733 PMCID: PMC9935785 DOI: 10.1007/s10815-022-02704-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 01/05/2023] Open
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Glatstein I, Chavez-Badiola A, Curchoe CL. New frontiers in embryo selection. J Assist Reprod Genet 2023; 40:223-234. [PMID: 36609943 PMCID: PMC9935769 DOI: 10.1007/s10815-022-02708-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 12/23/2022] [Indexed: 01/09/2023] Open
Abstract
Human infertility is a major global public health issue estimated to affect one out of six couples, while the number of assisted reproduction cycles grows impressively year over year. Efforts to alleviate infertility using advanced technology are gaining traction rapidly as infertility has an enormous impact on couples and the potential to destabilize entire societies if replacement birthrates are not achieved. Artificial intelligence (AI) technologies, leveraged by the highly advanced assisted reproductive technology (ART) industry, are a promising addition to the armamentarium of tools available to combat global infertility. This review provides a background for current methodologies in embryo selection, which is a manual, time-consuming, and poorly reproducible task. AI has the potential to improve this process (among many others) in both the clinician's office and the IVF laboratory. Embryo selection is evolving through digital methodologies into an automated procedure, with superior reliability and reproducibility, that is likely to result in higher pregnancy rates for patients. There is an emerging body of data demonstrating the utility of AI applications in multiple areas in the IVF laboratory. AI platforms have been developed to evaluate individual embryologist performance; to provide quality assurance for culture systems; to correlate embryologist's assessments and AI systems; to predict embryo ploidy, implantation, fetal heartbeat, and live birth outcome; and to replace the current "analogue" system of embryo selection with a digital paradigm. AI capability will distinguish high performing, high profit margin, low-cost, and highly successful IVF clinic business models. We think it will become the standard, "new normal" in IVF labs, as rapidly and thoroughly as vitrification, blastocyst culture, and intracytoplasmic sperm injection replaced their predecessor technologies. At the time of this review, the AI technology to automate embryo evaluation and selection has robustly matured, and therefore, it is the main focus of this review.
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Affiliation(s)
| | - Alejandro Chavez-Badiola
- IVF 2.0 LTD, 1 Liverpool Road, Maghull, L31 2HB, Merseyside, UK
- New Hope Fertility Center, Av. Prado Norte 135, Lomas de Chapultepec, CP11000, Mexico City, Mexico
- Reproductive Genetics, School of Biosciences, University of Kent, Canterbury, CT2 7NZ, Kent, UK
| | - Carol Lynn Curchoe
- ART Compass, a Fertility Guidance Technology, Newport Beach, CA, 92660, USA
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Kato K, Ueno S, Berntsen J, Kragh MF, Okimura T, Kuroda T. Does embryo categorization by existing artificial intelligence, morphokinetic or morphological embryo selection models correlate with blastocyst euploidy rates? Reprod Biomed Online 2023; 46:274-281. [PMID: 36470714 DOI: 10.1016/j.rbmo.2022.09.010] [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: 05/26/2022] [Revised: 08/28/2022] [Accepted: 09/12/2022] [Indexed: 02/07/2023]
Abstract
RESEARCH QUESTION Does embryo categorization by existing artificial intelligence (AI), morphokinetic or morphological embryo selection models correlate with blastocyst euploidy? DESIGN A total of 834 patients (mean maternal age 40.5 ± 3.4 years) who underwent preimplantation genetic testing for aneuploidies (PGT-A) on a total of 3573 tested blastocysts were included in this retrospective study. The cycles were stratified into five maternal age groups according to the Society for Assisted Reproductive Technology age groups (<35, 35-37, 38-40, 41-42 and >42 years). The main outcome of this study was the correlation of euploidy rates in stratified maternal age groups and an automated AI model (iDAScore® v1.0), a morphokinetic embryo selection model (KIDScore Day 5 ver 3, KS-D5) and a traditional morphological grading model (Gardner criteria), respectively. RESULTS Euploidy rates were significantly correlated with iDAScore (P = 0.0035 to <0.001) in all age groups, and expect for the youngest age group, with KS-D5 and Gardner criteria (all P < 0.0001). Additionally, multivariate logistic regression analysis showed that for all models, higher scores were significantly correlated with euploidy (all P < 0.0001). CONCLUSION These results show that existing blastocyst scoring models correlate with ploidy status. However, as these models were developed to indicate implantation potential, they cannot accurately diagnose if an embryo is euploid or aneuploid. Instead, they may be used to support the decision of how many and which blastocysts to biopsy, thus potentially reducing patient costs.
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Affiliation(s)
- Keiichi Kato
- Kato Ladies Clinic, 7-20-3, Nishishinjuku, Shinjuku Tokyo 160-0023, Japan.
| | - Satoshi Ueno
- Kato Ladies Clinic, 7-20-3, Nishishinjuku, Shinjuku Tokyo 160-0023, Japan
| | | | | | - Tadashi Okimura
- Kato Ladies Clinic, 7-20-3, Nishishinjuku, Shinjuku Tokyo 160-0023, Japan
| | - Tomoko Kuroda
- Kato Ladies Clinic, 7-20-3, Nishishinjuku, Shinjuku Tokyo 160-0023, Japan
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Kim J, Lee J, Jun JH. Non-invasive evaluation of embryo quality for the selection of transferable embryos in human in vitro fertilization-embryo transfer. Clin Exp Reprod Med 2022; 49:225-238. [PMID: 36482497 PMCID: PMC9732075 DOI: 10.5653/cerm.2022.05575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 07/28/2023] Open
Abstract
The ultimate goal of human assisted reproductive technology is to achieve a healthy pregnancy and birth, ideally from the selection and transfer of a single competent embryo. Recently, techniques for efficiently evaluating the state and quality of preimplantation embryos using time-lapse imaging systems have been applied. Artificial intelligence programs based on deep learning technology and big data analysis of time-lapse monitoring system during in vitro culture of preimplantation embryos have also been rapidly developed. In addition, several molecular markers of the secretome have been successfully analyzed in spent embryo culture media, which could easily be obtained during in vitro embryo culture. It is also possible to analyze small amounts of cell-free nucleic acids, mitochondrial nucleic acids, miRNA, and long non-coding RNA derived from embryos using real-time polymerase chain reaction (PCR) or digital PCR, as well as next-generation sequencing. Various efforts are being made to use non-invasive evaluation of embryo quality (NiEEQ) to select the embryo with the best developmental competence. However, each NiEEQ method has some limitations that should be evaluated case by case. Therefore, an integrated analysis strategy fusing several NiEEQ methods should be urgently developed and confirmed by proper clinical trials.
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Affiliation(s)
- Jihyun Kim
- Department of Obstetrics and Gynaecology, Seoul Medical Center, Seoul, Republic of Korea
| | - Jaewang Lee
- Department of Biomedical Laboratory Science, College of Health Science, Eulji University, Seongnam, Republic of Korea
| | - Jin Hyun Jun
- Department of Biomedical Laboratory Science, College of Health Science, Eulji University, Seongnam, Republic of Korea
- Department of Senior Healthcare, Graduate School, Eulji University, Seongnam, Republic of Korea
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Fordham DE, Rosentraub D, Polsky AL, Aviram T, Wolf Y, Perl O, Devir A, Rosentraub S, Silver DH, Gold Zamir Y, Bronstein AM, Lara Lara M, Ben Nagi J, Alvarez A, Munné S. Embryologist agreement when assessing blastocyst implantation probability: is data-driven prediction the solution to embryo assessment subjectivity? Hum Reprod 2022; 37:2275-2290. [PMID: 35944167 DOI: 10.1093/humrep/deac171] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/21/2022] [Indexed: 11/13/2022] Open
Abstract
STUDY QUESTION What is the accuracy and agreement of embryologists when assessing the implantation probability of blastocysts using time-lapse imaging (TLI), and can it be improved with a data-driven algorithm? SUMMARY ANSWER The overall interobserver agreement of a large panel of embryologists was moderate and prediction accuracy was modest, while the purpose-built artificial intelligence model generally resulted in higher performance metrics. WHAT IS KNOWN ALREADY Previous studies have demonstrated significant interobserver variability amongst embryologists when assessing embryo quality. However, data concerning embryologists' ability to predict implantation probability using TLI is still lacking. Emerging technologies based on data-driven tools have shown great promise for improving embryo selection and predicting clinical outcomes. STUDY DESIGN, SIZE, DURATION TLI video files of 136 embryos with known implantation data were retrospectively collected from two clinical sites between 2018 and 2019 for the performance assessment of 36 embryologists and comparison with a deep neural network (DNN). PARTICIPANTS/MATERIALS, SETTING, METHODS We recruited 39 embryologists from 13 different countries. All participants were blinded to clinical outcomes. A total of 136 TLI videos of embryos that reached the blastocyst stage were used for this experiment. Each embryo's likelihood of successfully implanting was assessed by 36 embryologists, providing implantation probability grades (IPGs) from 1 to 5, where 1 indicates a very low likelihood of implantation and 5 indicates a very high likelihood. Subsequently, three embryologists with over 5 years of experience provided Gardner scores. All 136 blastocysts were categorized into three quality groups based on their Gardner scores. Embryologist predictions were then converted into predictions of implantation (IPG ≥ 3) and no implantation (IPG ≤ 2). Embryologists' performance and agreement were assessed using Fleiss kappa coefficient. A 10-fold cross-validation DNN was developed to provide IPGs for TLI video files. The model's performance was compared to that of the embryologists. MAIN RESULTS AND THE ROLE OF CHANCE Logistic regression was employed for the following confounding variables: country of residence, academic level, embryo scoring system, log years of experience and experience using TLI. None were found to have a statistically significant impact on embryologist performance at α = 0.05. The average implantation prediction accuracy for the embryologists was 51.9% for all embryos (N = 136). The average accuracy of the embryologists when assessing top quality and poor quality embryos (according to the Gardner score categorizations) was 57.5% and 57.4%, respectively, and 44.6% for fair quality embryos. Overall interobserver agreement was moderate (κ = 0.56, N = 136). The best agreement was achieved in the poor + top quality group (κ = 0.65, N = 77), while the agreement in the fair quality group was lower (κ = 0.25, N = 59). The DNN showed an overall accuracy rate of 62.5%, with accuracies of 62.2%, 61% and 65.6% for the poor, fair and top quality groups, respectively. The AUC for the DNN was higher than that of the embryologists overall (0.70 DNN vs 0.61 embryologists) as well as in all of the Gardner groups (DNN vs embryologists-Poor: 0.69 vs 0.62; Fair: 0.67 vs 0.53; Top: 0.77 vs 0.54). LIMITATIONS, REASONS FOR CAUTION Blastocyst assessment was performed using video files acquired from time-lapse incubators, where each video contained data from a single focal plane. Clinical data regarding the underlying cause of infertility and endometrial thickness before the transfer was not available, yet may explain implantation failure and lower accuracy of IPGs. Implantation was defined as the presence of a gestational sac, whereas the detection of fetal heartbeat is a more robust marker of embryo viability. The raw data were anonymized to the extent that it was not possible to quantify the number of unique patients and cycles included in the study, potentially masking the effect of bias from a limited patient pool. Furthermore, the lack of demographic data makes it difficult to draw conclusions on how representative the dataset was of the wider population. Finally, embryologists were required to assess the implantation potential, not embryo quality. Although this is not the traditional approach to embryo evaluation, morphology/morphokinetics as a means of assessing embryo quality is believed to be strongly correlated with viability and, for some methods, implantation potential. WIDER IMPLICATIONS OF THE FINDINGS Embryo selection is a key element in IVF success and continues to be a challenge. Improving the predictive ability could assist in optimizing implantation success rates and other clinical outcomes and could minimize the financial and emotional burden on the patient. This study demonstrates moderate agreement rates between embryologists, likely due to the subjective nature of embryo assessment. In particular, we found that average embryologist accuracy and agreement were significantly lower for fair quality embryos when compared with that for top and poor quality embryos. Using data-driven algorithms as an assistive tool may help IVF professionals increase success rates and promote much needed standardization in the IVF clinic. Our results indicate a need for further research regarding technological advancement in this field. STUDY FUNDING/COMPETING INTEREST(S) Embryonics Ltd is an Israel-based company. Funding for the study was partially provided by the Israeli Innovation Authority, grant #74556. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
| | | | | | - Talia Aviram
- Embryonics, Embryonics R&D Center, Haifa, Israel
| | - Yotam Wolf
- Embryonics, Embryonics R&D Center, Haifa, Israel
| | - Oriel Perl
- Embryonics, Embryonics R&D Center, Haifa, Israel
| | - Asnat Devir
- Embryonics, Embryonics R&D Center, Haifa, Israel
| | | | | | | | - Alex M Bronstein
- Embryonics, Embryonics R&D Center, Haifa, Israel.,Computer Science, Technion-Israel Institute of Technology, Haifa, Israel
| | | | - Jara Ben Nagi
- Centre for Reproductive and Genetic Health, London, UK
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Ezoe K, Shimazaki K, Miki T, Takahashi T, Tanimura Y, Amagai A, Sawado A, Akaike H, Mogi M, Kaneko S, Okimura T, Kato K. Association of a deep learning-based scoring system with morphokinetics and morphological alterations in human embryos. Reprod Biomed Online 2022; 45:1124-1132. [DOI: 10.1016/j.rbmo.2022.08.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 07/19/2022] [Accepted: 08/08/2022] [Indexed: 11/25/2022]
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Diakiw SM, Hall JM, VerMilyea M, Lim AY, Quangkananurug W, Chanchamroen S, Bankowski B, Stones R, Storr A, Miller A, Adaniya G, van Tol R, Hanson R, Aizpurua J, Giardini L, Johnston A, Van Nguyen T, Dakka MA, Perugini D, Perugini M. An artificial intelligence model correlated with morphological and genetic features of blastocyst quality demonstrates superior ranking of viable embryos. Reprod Biomed Online 2022; 45:1105-1117. [DOI: 10.1016/j.rbmo.2022.07.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/30/2022] [Accepted: 07/25/2022] [Indexed: 11/25/2022]
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Ueno S, Berntsen J, Ito M, Okimura T, Kato K. Correlation between an annotation-free embryo scoring system based on deep learning and live birth/neonatal outcomes after single vitrified-warmed blastocyst transfer: a single-centre, large-cohort retrospective study. J Assist Reprod Genet 2022; 39:2089-2099. [PMID: 35881272 PMCID: PMC9475010 DOI: 10.1007/s10815-022-02562-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/04/2022] [Indexed: 11/30/2022] Open
Abstract
Propose Does an annotation-free embryo scoring system based on deep learning and time-lapse sequence images correlate with live birth (LB) and neonatal outcomes? Methods Patients who underwent SVBT cycles (3010 cycles, mean age: 39.3 ± 4.0). Scores were calculated using the iDAScore software module in the Vitrolife Technology Hub (Vitrolife, Gothenburg, Sweden). The correlation between iDAScore, LB rates, and total miscarriage (TM), including 1st- and 2nd-trimester miscarriage, was analysed using a trend test and multivariable logistic regression analysis. Furthermore, the correlation between the iDAScore and neonatal outcomes was analysed. Results LB rates decreased as iDAScore decreased (P < 0.05), and a similar inverse trend was observed for the TM rates. Additionally, multivariate logistic regression analysis showed that iDAScore significantly correlated with increased LB (adjusted odds ratio: 1.811, 95% CI: 1.666–1.976, P < 0.05) and decreased TM (adjusted odds ratio: 0.799, 95% CI: 0.706–0.905, P < 0.05). There was no significant correlation between iDAScore and neonatal outcomes, including congenital malformations, sex, gestational age, and birth weight. Multivariate logistic regression analysis, which included maternal and paternal age, maternal body mass index, parity, smoking, and presence or absence of caesarean section as confounding factors, revealed no significant difference in any neonatal characteristics. Conclusion Automatic embryo scoring using iDAScore correlates with decreased miscarriage and increased LB and has no correlation with neonatal outcomes. Supplementary information The online version contains supplementary material available at 10.1007/s10815-022-02562-5.
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Affiliation(s)
- Satoshi Ueno
- Kato Ladies Clinic, 7-20-3, Nishi-shinjuku, Shinjuku, Tokyo, 160-0023, Japan
| | | | - Motoki Ito
- 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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