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Pavlovic ZJ, Jiang VS, Hariton E. Current applications of artificial intelligence in assisted reproductive technologies through the perspective of a patient's journey. Curr Opin Obstet Gynecol 2024; 36:211-217. [PMID: 38597425 DOI: 10.1097/gco.0000000000000951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
PURPOSE OF REVIEW This review highlights the timely relevance of artificial intelligence in enhancing assisted reproductive technologies (ARTs), particularly in-vitro fertilization (IVF). It underscores artificial intelligence's potential in revolutionizing patient outcomes and operational efficiency by addressing challenges in fertility diagnoses and procedures. RECENT FINDINGS Recent advancements in artificial intelligence, including machine learning and predictive modeling, are making significant strides in optimizing IVF processes such as medication dosing, scheduling, and embryological assessments. Innovations include artificial intelligence augmented diagnostic testing, predictive modeling for treatment outcomes, scheduling optimization, dosing and protocol selection, follicular and hormone monitoring, trigger timing, and improved embryo selection. These developments promise to refine treatment approaches, enhance patient engagement, and increase the accuracy and scalability of fertility treatments. SUMMARY The integration of artificial intelligence into reproductive medicine offers profound implications for clinical practice and research. By facilitating personalized treatment plans, standardizing procedures, and improving the efficiency of fertility clinics, artificial intelligence technologies pave the way for value-based, accessible, and efficient fertility services. Despite the promise, the full potential of artificial intelligence in ART will require ongoing validation and ethical considerations to ensure equitable and effective implementation.
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Affiliation(s)
- Zoran J Pavlovic
- Department of Obstetrics and Gynecology/Reproductive Endocrinology and Infertility, University of South Florida, Morsani College of Medicine, Tampa, Florida
| | - Victoria S Jiang
- Division of Reproductive Endocrinology & Infertility, Vincent Department of Obstetrics and Gynecology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Eduardo Hariton
- Reproductive Science Center of the San Francisco Bay Area, San Ramon, California, USA
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Chavez-Badiola A, Farías AFS, Mendizabal-Ruiz G, Silvestri G, Griffin DK, Valencia-Murillo R, Drakeley AJ, Cohen J. Use of artificial intelligence embryo selection based on static images to predict first-trimester pregnancy loss. Reprod Biomed Online 2024; 49:103934. [PMID: 38824762 DOI: 10.1016/j.rbmo.2024.103934] [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: 10/18/2023] [Revised: 02/16/2024] [Accepted: 02/27/2024] [Indexed: 06/04/2024]
Abstract
RESEARCH QUESTION Can an artificial intelligence embryo selection assistant predict the incidence of first-trimester spontaneous abortion using static images of IVF embryos? DESIGN In a blind, retrospective study, a cohort of 172 blastocysts from IVF cases with single embryo transfer and a positive biochemical pregnancy test was ranked retrospectively by the artificial intelligence morphometric algorithm ERICA. Making use of static embryo images from a light microscope, each blastocyst was assigned to one of four possible groups (optimal, good, fair or poor), and linear regression was used to correlate the results with the presence or absence of a normal fetal heart beat as an indicator of ongoing pregnancy or spontaneous abortion, respectively. Additional analyses included modelling for recipient age and chromosomal status established by preimplantation genetic testing for aneuploidy (PGT-A). RESULTS Embryos classified as optimal/good had a lower incidence of spontaneous abortion (16.1%) compared with embryos classified as fair/poor (25%; OR = 0.46, P = 0.005). The incidence of spontaneous abortion in chromosomally normal embryos (determined by PGT-A) was 13.3% for optimal/good embryos and 20.0% for fair/poor embryos, although the difference was not significant (P = 0.531). There was a significant association between embryo rank and recipient age (P = 0.018), in that the incidence of spontaneous abortion was unexpectedly lower in older recipients (21.3% for age ≤35 years, 17.9% for age 36-38 years, 16.4% for age ≥39 years; OR = 0.354, P = 0.0181). Overall, these results support correlation between risk of spontaneous abortion and embryo rank as determined by artificial intelligence; classification accuracy was calculated to be 67.4%. CONCLUSIONS This preliminary study suggests that artificial intelligence (ERICA), which was designed as a ranking system to assist with embryo transfer decisions and ploidy prediction, may also be useful to provide information for couples on the risk of spontaneous abortion. Future work will include a larger sample size and karyotyping of miscarried pregnancy tissue.
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Affiliation(s)
- Alejandro Chavez-Badiola
- University of Kent, School of Biosciences, Canterbury, UK; IVF 2.0 Ltd, London, UK; New Hope Fertility Center, Guadalajara, Mexico; Conceivable Life Sciences, New York, NY, USA
| | | | - Gerardo Mendizabal-Ruiz
- Conceivable Life Sciences, New York, NY, USA; Departamento de Ciencias Computacionales, Universidad de Guadalajara, Guadalajara, Mexico
| | - Giuseppe Silvestri
- University of Kent, School of Biosciences, Canterbury, UK; Conceivable Life Sciences, New York, NY, USA
| | | | | | - Andrew J Drakeley
- IVF 2.0 Ltd, London, UK; Hewitt Fertility Centre, Liverpool Women's NHS Foundation Trust, Liverpool, UK
| | - Jacques Cohen
- IVF 2.0 Ltd, London, UK; Conceivable Life Sciences, New York, NY, USA
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Luong TMT, Ho NT, Hwu YM, Lin SY, Ho JYP, Wang RS, Lee YX, Tan SJ, Lee YR, Huang YL, Hsu YC, Le NQK, Tzeng CR. Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation. J Assist Reprod Genet 2024:10.1007/s10815-024-03178-7. [PMID: 38963605 DOI: 10.1007/s10815-024-03178-7] [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: 02/27/2024] [Accepted: 06/13/2024] [Indexed: 07/05/2024] Open
Abstract
PURPOSE To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data. METHODS This retrospective study utilized a dataset of 1908 blastocyst embryos. The dataset includes ploidy status, morphokinetic features, morphology grades, and 11 clinical variables. Six machine learning (ML) models including Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost (ADA), and Light Gradient-Boosting Machine (LGBM) were trained to predict ploidy status probabilities across three distinct datasets: high-grade embryos (HGE, n = 1107), low-grade embryos (LGE, n = 364), and all-grade embryos (AGE, n = 1471). The model's performance was interpreted using XAI, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) techniques. RESULTS The mean maternal age was 38.5 ± 3.85 years. The Random Forest (RF) model exhibited superior performance compared to the other five ML models, achieving an accuracy of 0.749 and an AUC of 0.808 for AGE. In the external test set, the RF model achieved an accuracy of 0.714 and an AUC of 0.750 (95% CI, 0.702-0.796). SHAP's feature impact analysis highlighted that maternal age, paternal age, time to blastocyst (tB), and day 5 morphology grade significantly impacted the predictive model. In addition, LIME offered specific case-ploidy prediction probabilities, revealing the model's assigned values for each variable within a finite range. CONCLUSION The model highlights the potential of using XAI algorithms to enhance ploidy prediction, optimize embryo selection as patient-centric consultation, and provides reliability and transparent insights into the decision-making process.
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Affiliation(s)
- Thi-My-Trang Luong
- International Master Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan
- Taipei Fertility Centre, Taipei, Taiwan
| | - Nguyen-Tuong Ho
- Taipei Fertility Centre, Taipei, Taiwan
- IVFMD, My Duc Hospital, Ho Chi Minh, Vietnam
| | | | | | | | | | | | | | | | | | | | - Nguyen-Quoc-Khanh Le
- AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan.
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan.
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
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Dissler N, Nogueira D, Keppi B, Sanguinet P, Ozanon C, Geoffroy-Siraudin C, Pollet-Villard X, Boussommier-Calleja A. Artificial intelligence-powered assisted ranking of sibling embryos to increase first cycle pregnancy rate. Reprod Biomed Online 2024; 49:103887. [PMID: 38701632 DOI: 10.1016/j.rbmo.2024.103887] [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: 09/07/2023] [Revised: 01/15/2024] [Accepted: 02/05/2024] [Indexed: 05/05/2024]
Abstract
RESEARCH QUESTION Could EMBRYOLY, an artificial intelligence embryo evaluation tool, assist embryologists to increase first cycle pregnancy rate and reduce cycles to pregnancy for patients? DESIGN Data from 11,988 embryos were collected via EMBRYOLY from 2666 egg retrievals (2019-2022) across 11 centres in France, Spain and Morocco using three time-lapse systems (TLS). Data from two independent clinics were also examined. EMBRYOLY's transformer-based model was applied to transferred embryos to evaluate ranking performances against pregnancy and birth outcomes. It was applied to cohorts to rank sibling embryos (including non-transferred) according to their likelihood of clinical pregnancy and to compute the agreement with the embryologist's highest ranked embryo. Its effect on time to pregnancy and first cycle pregnancy rate was evaluated on cohorts with multiple single blastocyst transfers, assuming the embryologist would have considered EMBRYOLY's ranking on the embryos favoured for transfer. RESULTS EMBRYOLY's score correlated significantly with clinical pregnancies and live births for cleavage and blastocyst transfers. This held true for clinical pregnancies from blastocyst transfers in two independent clinics. In cases of multiple single embryo transfers, embryologists achieved a 19.8% first cycle pregnancy rate, which could have been improved to 44.1% with the adjunctive use of EMBRYOLY (McNemar's test: P < 0.001). This could have reduced cycles to clinical pregnancy from 2.01 to 1.66 (Wilcoxon test: P < 0.001). CONCLUSIONS EMBRYOLY's potential to enhance first cycle pregnancy rates when combined with embryologists' expertise is highlighted. It reduces the number of unsuccessful cycles for patients across TLS and IVF centres.
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Affiliation(s)
- Nina Dissler
- ImVitro, Paris, France, 130 Rue de Lourmel, 75015 Paris
| | - Daniela Nogueira
- INOVIE Fertilité, Institut de Fertilité La Croix Du Sud, Clinique la Croix du Sud, Toulouse, France.; Art Fertility Clinics, IVF laboratory, Abu Dhabi, United Arab Emirates
| | - Bertrand Keppi
- INOVIE Group, INOVIE Fertilié, Gen-Bio, 63100 Clermont-Ferrand, France
| | - Pierre Sanguinet
- INOVIE Group, INOVIE Fertilié, LaboSud, 34000 Montpellier, France
| | | | | | - Xavier Pollet-Villard
- MLAB Groupe, Centre d'Assistance Médicale à la Procréation Nataliance, Pôle Santé Oréliance, Saran, France
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Lee CI, Tzeng CR, Li M, Lai HH, Chen CH, Huang Y, Chang TA, Chen CH, Huang CC, Lee MS, Liu M. Leveraging federated learning for boosting data privacy and performance in IVF embryo selection. J Assist Reprod Genet 2024; 41:1811-1820. [PMID: 38834757 PMCID: PMC11263320 DOI: 10.1007/s10815-024-03148-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 05/18/2024] [Indexed: 06/06/2024] Open
Abstract
PURPOSE To study the effectiveness of federated learning in in vitro fertilization on embryo evaluation tasks. METHODS This is a retrospective cohort analysis. Two datasets were used in this study. The ploidy status dataset consisted of 10,065 embryo records, 3760 treatments, and 2479 infertile couples from 5 hospitals. The clinical pregnancy dataset consisted of 4495 embryo records, 4495 treatments, and 3704 infertile couples from 4 hospitals. Federated learning and the gradient boosting decision tree algorithm were utilized for modeling. RESULTS On the ploidy status dataset, the areas under the receiver operating characteristic curves of our model trained with federated learning were 71.78%, 73.10%, 69.39%, 69.72%, and 73.46% for 5 hospitals respectively, showing an average increase of 2.5% compared to those of our model trained without federated learning. On the clinical pregnancy dataset, the areas under the receiver operating characteristic curves of our model trained with federated learning were 72.03%, 56.77%, 61.63%, and 58.58% for 4 hospitals respectively, showing an average increase of 3.08%. CONCLUSIONS Federated learning can improve data privacy and data security and meanwhile improve the performance of embryo selection tasks by leveraging data from multiple sources. This study demonstrates the effectiveness of federated learning in embryo evaluation, and the results show the promise for future application.
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Affiliation(s)
- Chun-I Lee
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Obstetrics and Gynecology, Chung Shan Medical University, Taichung, Taiwan
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | | | - Monty Li
- Becoming Reproductive Center, Taipei, Taiwan
| | - Hsing-Hua Lai
- Stork Fertility Center, Stork Ladies Clinic, Hsinchu, Taiwan
| | - Chi-Huang Chen
- Division of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Obstetrics and Gynecology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yulun Huang
- Binflux, Inc, 4F.-1, No. 9, Dehui St., Zhongshan Dist, Taipei, 10461, Taiwan
| | - T Arthur Chang
- Department of Obstetrics and Gynecology, University of Texas Health Science Center, San Antonio, TX, USA
| | - Chien-Hong Chen
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | - Chun-Chia Huang
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | - Maw-Sheng Lee
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Obstetrics and Gynecology, Chung Shan Medical University, Taichung, Taiwan
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | - Mark Liu
- Binflux, Inc, 4F.-1, No. 9, Dehui St., Zhongshan Dist, Taipei, 10461, Taiwan.
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Goss DM, Vasilescu SA, Vasilescu PA, Cooke S, Kim SH, Sacks GP, Gardner DK, Warkiani ME. Evaluation of an artificial intelligence-facilitated sperm detection tool in azoospermic samples for use in ICSI. Reprod Biomed Online 2024; 49:103910. [PMID: 38652944 DOI: 10.1016/j.rbmo.2024.103910] [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: 09/07/2023] [Revised: 01/31/2024] [Accepted: 02/09/2024] [Indexed: 04/25/2024]
Abstract
RESEARCH QUESTION Can artificial intelligence (AI) improve the efficiency and efficacy of sperm searches in azoospermic samples? DESIGN This two-phase proof-of-concept study began with a training phase using eight azoospermic patients (>10,000 sperm images) to provide a variety of surgically collected samples for sperm morphology and debris variation to train a convolutional neural network to identify spermatozoa. Second, side-by-side testing was undertaken on two cohorts of non-obstructive azoospermia patient samples: an embryologist versus the AI identifying all the spermatozoa in the still images (cohort 1, n = 4), and a side-by-side test with a simulated clinical deployment of the AI model with an intracytoplasmic sperm injection microscope and the embryologist performing a search with and without the aid of the AI (cohort 2, n = 4). RESULTS In cohort 1, the AI model showed an improvement in the time taken to identify all the spermatozoa per field of view (0.02 ± 0.30 × 10-5s versus 36.10 ± 1.18s, P < 0.0001) and improved recall (91.95 ± 0.81% versus 86.52 ± 1.34%, P < 0.001) compared with an embryologist. From a total of 2660 spermatozoa to find in all the samples combined, 1937 were found by an embryologist and 1997 were found by the AI in less than 1000th of the time. In cohort 2, the AI-aided embryologist took significantly less time per droplet (98.90 ± 3.19 s versus 168.7 ± 7.84 s, P < 0.0001) and found 1396 spermatozoa, while 1274 were found without AI, although no significant difference was observed. CONCLUSIONS AI-powered image analysis has the potential for seamless integration into laboratory workflows, to reduce the time to identify and isolate spermatozoa from surgical sperm samples from hours to minutes, thus increasing success rates from these treatments.
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Affiliation(s)
- Dale M Goss
- School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.; NeoGenix Biosciences Pty Ltd, Sydney, New South Wales, Australia.; IVFAustralia, Sydney, New South Wales, Australia
| | - Steven A Vasilescu
- School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.; NeoGenix Biosciences Pty Ltd, Sydney, New South Wales, Australia
| | | | - Simon Cooke
- IVFAustralia, Sydney, New South Wales, Australia
| | - Shannon Hk Kim
- IVFAustralia, Sydney, New South Wales, Australia.; University of New South Wales, Sydney, New South Wales, Australia
| | - Gavin P Sacks
- School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.; IVFAustralia, Sydney, New South Wales, Australia.; University of New South Wales, Sydney, New South Wales, Australia
| | - David K Gardner
- NeoGenix Biosciences Pty Ltd, Sydney, New South Wales, Australia.; Melbourne IVF, Melbourne, Victoria, Australia
| | - Majid E Warkiani
- School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.; NeoGenix Biosciences Pty Ltd, Sydney, New South Wales, Australia.; Institute for Biomedical Materials & Devices (IBMD), University of Technology Sydney, Sydney, New South Wales, Australia..
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Cao P, Derhaag J, Coonen E, Brunner H, Acharya G, Salumets A, Zamani Esteki M. Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images. Hum Reprod 2024; 39:1197-1207. [PMID: 38600621 PMCID: PMC11145014 DOI: 10.1093/humrep/deae064] [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: 12/19/2023] [Revised: 02/13/2024] [Indexed: 04/12/2024] Open
Abstract
STUDY QUESTION Can generative artificial intelligence (AI) models produce high-fidelity images of human blastocysts? SUMMARY ANSWER Generative AI models exhibit the capability to generate high-fidelity human blastocyst images, thereby providing substantial training datasets crucial for the development of robust AI models. WHAT IS KNOWN ALREADY The integration of AI into IVF procedures holds the potential to enhance objectivity and automate embryo selection for transfer. However, the effectiveness of AI is limited by data scarcity and ethical concerns related to patient data privacy. Generative adversarial networks (GAN) have emerged as a promising approach to alleviate data limitations by generating synthetic data that closely approximate real images. STUDY DESIGN, SIZE, DURATION Blastocyst images were included as training data from a public dataset of time-lapse microscopy (TLM) videos (n = 136). A style-based GAN was fine-tuned as the generative model. PARTICIPANTS/MATERIALS, SETTING, METHODS We curated a total of 972 blastocyst images as training data, where frames were captured within the time window of 110-120 h post-insemination at 1-h intervals from TLM videos. We configured the style-based GAN model with data augmentation (AUG) and pretrained weights (Pretrained-T: with translation equivariance; Pretrained-R: with translation and rotation equivariance) to compare their optimization on image synthesis. We then applied quantitative metrics including Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) to assess the quality and fidelity of the generated images. Subsequently, we evaluated qualitative performance by measuring the intelligence behavior of the model through the visual Turing test. To this end, 60 individuals with diverse backgrounds and expertise in clinical embryology and IVF evaluated the quality of synthetic embryo images. MAIN RESULTS AND THE ROLE OF CHANCE During the training process, we observed consistent improvement of image quality that was measured by FID and KID scores. Pretrained and AUG + Pretrained initiated with remarkably lower FID and KID values compared to both Baseline and AUG + Baseline models. Following 5000 training iterations, the AUG + Pretrained-R model showed the highest performance of the evaluated five configurations with FID and KID scores of 15.2 and 0.004, respectively. Subsequently, we carried out the visual Turing test, such that IVF embryologists, IVF laboratory technicians, and non-experts evaluated the synthetic blastocyst-stage embryo images and obtained similar performance in specificity with marginal differences in accuracy and sensitivity. LIMITATIONS, REASONS FOR CAUTION In this study, we primarily focused the training data on blastocyst images as IVF embryos are primarily assessed in blastocyst stage. However, generation of an array of images in different preimplantation stages offers further insights into the development of preimplantation embryos and IVF success. In addition, we resized training images to a resolution of 256 × 256 pixels to moderate the computational costs of training the style-based GAN models. Further research is needed to involve a more extensive and diverse dataset from the formation of the zygote to the blastocyst stage, e.g. video generation, and the use of improved image resolution to facilitate the development of comprehensive AI algorithms and to produce higher-quality images. WIDER IMPLICATIONS OF THE FINDINGS Generative AI models hold promising potential in generating high-fidelity human blastocyst images, which allows the development of robust AI models as it can provide sufficient training datasets while safeguarding patient data privacy. Additionally, this may help to produce sufficient embryo imaging training data with different (rare) abnormal features, such as embryonic arrest, tripolar cell division to avoid class imbalances and reach to even datasets. Thus, generative models may offer a compelling opportunity to transform embryo selection procedures and substantially enhance IVF outcomes. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by a Horizon 2020 innovation grant (ERIN, grant no. EU952516) and a Horizon Europe grant (NESTOR, grant no. 101120075) of the European Commission to A.S. and M.Z.E., the Estonian Research Council (grant no. PRG1076) to A.S., and the EVA (Erfelijkheid Voortplanting & Aanleg) specialty program (grant no. KP111513) of Maastricht University Medical Centre (MUMC+) to M.Z.E. TRIAL REGISTRATION NUMBER Not applicable.
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Affiliation(s)
- Ping Cao
- Department of Clinical Genetics, Maastricht University Medical Center+ (MUMC+), Maastricht, The Netherlands
- Department of Genetics and Cell Biology, GROW Research Institute for Oncology and Reproduction, Faculty of Health, Medicine and Life Sciences (FHML), Maastricht University, Maastricht, The Netherlands
| | - Josien Derhaag
- Department of Reproductive Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, The Netherlands
| | - Edith Coonen
- Department of Clinical Genetics, Maastricht University Medical Center+ (MUMC+), Maastricht, The Netherlands
- Department of Reproductive Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, The Netherlands
| | - Han Brunner
- Department of Clinical Genetics, Maastricht University Medical Center+ (MUMC+), Maastricht, The Netherlands
- Department of Genetics and Cell Biology, GROW Research Institute for Oncology and Reproduction, Faculty of Health, Medicine and Life Sciences (FHML), Maastricht University, Maastricht, The Netherlands
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ganesh Acharya
- Division of Obstetrics and Gynecology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, and Karolinska University Hospital, Stockholm, Sweden
- Women’s Health and Perinatology Research Group, Department of Clinical Medicine, UiT—The Arctic University of Norway, Tromsø, Norway
| | - Andres Salumets
- Division of Obstetrics and Gynecology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, and Karolinska University Hospital, Stockholm, Sweden
- Competence Centre on Health Technologies, Tartu, Estonia
- Department of Obstetrics and Gynecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
| | - Masoud Zamani Esteki
- Department of Clinical Genetics, Maastricht University Medical Center+ (MUMC+), Maastricht, The Netherlands
- Department of Genetics and Cell Biology, GROW Research Institute for Oncology and Reproduction, Faculty of Health, Medicine and Life Sciences (FHML), Maastricht University, Maastricht, The Netherlands
- Division of Obstetrics and Gynecology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, and Karolinska University Hospital, Stockholm, Sweden
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Kim HM, Kang H, Lee C, Park JH, Chung MK, Kim M, Kim NY, Lee HJ. Evaluation of the Clinical Efficacy and Trust in AI-Assisted Embryo Ranking: Survey-Based Prospective Study. J Med Internet Res 2024; 26:e52637. [PMID: 38830209 PMCID: PMC11184268 DOI: 10.2196/52637] [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: 09/12/2023] [Revised: 10/23/2023] [Accepted: 05/06/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Current embryo assessment methods for in vitro fertilization depend on subjective morphological assessments. Recently, artificial intelligence (AI) has emerged as a promising tool for embryo assessment; however, its clinical efficacy and trustworthiness remain unproven. Simulation studies may provide additional evidence, provided that they are meticulously designed to mitigate bias and variance. OBJECTIVE The primary objective of this study was to evaluate the benefits of an AI model for predicting clinical pregnancy through well-designed simulations. The secondary objective was to identify the characteristics of and potential bias in the subgroups of embryologists with varying degrees of experience. METHODS This simulation study involved a questionnaire-based survey conducted on 61 embryologists with varying levels of experience from 12 in vitro fertilization clinics. The survey was conducted via Google Forms (Google Inc) in three phases: (1) phase 1, an initial assessment (December 23, 2022, to January 22, 2023); (2) phase 2, a validation assessment (March 6, 2023, to April 5, 2023); and (3) phase 3 an AI-guided assessment (March 6, 2023, to April 5, 2023). Inter- and intraobserver assessments and the accuracy of embryo selection from 360 day-5 embryos before and after AI guidance were analyzed for all embryologists and subgroups of senior and junior embryologists. RESULTS With AI guidance, the interobserver agreement increased from 0.355 to 0.527 and from 0.440 to 0.524 for junior and senior embryologists, respectively, thus reaching similar levels of agreement. In a test of accurate embryo selection with 90 questions, the numbers of correct responses by the embryologists only, embryologists with AI guidance, and AI only were 34 (38%), 45 (50%), and 59 (66%), respectively. Without AI, the average score (accuracy) of the junior group was 33.516 (37%), while that of the senior group was 35.967 (40%), with P<.001 in the t test. With AI guidance, the average score (accuracy) of the junior group increased to 46.581 (52%), reaching a level similar to that of the senior embryologists of 44.833 (50%), with P=.34. Junior embryologists had a higher level of trust in the AI score. CONCLUSIONS This study demonstrates the potential benefits of AI in selecting embryos with high chances of pregnancy, particularly for embryologists with 5 years or less of experience, possibly due to their trust in AI. Thus, using AI as an auxiliary tool in clinical practice has the potential to improve embryo assessment and increase the probability of a successful pregnancy.
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Affiliation(s)
| | - Hyoeun Kang
- AI Lab, Kai Health, Seoul, Republic of Korea
| | | | - Jong Hyuk Park
- IVF Clinic, Miraewaheemang Hospital, Seoul, Republic of Korea
| | - Mi Kyung Chung
- IVF Clinic, Seoul Rachel Fertility Center, Seoul, Republic of Korea
| | - Miran Kim
- Department of Obstetrics & Gynecology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Na Young Kim
- IVF Clinic, HI Fertility Center, Seoul, Republic of Korea
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Zou H, Wang R, Morbeck DE. Diagnostic or prognostic? Decoding the role of embryo selection on in vitro fertilization treatment outcomes. Fertil Steril 2024; 121:730-736. [PMID: 38185198 DOI: 10.1016/j.fertnstert.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/22/2023] [Accepted: 01/03/2024] [Indexed: 01/09/2024]
Abstract
In this review, we take a fresh look at embryo assessment and selection methods from the perspective of diagnosis and prognosis. On the basis of a systematic search in the literature, we examined the evidence on the prognostic value of different embryo assessment methods, including morphological assessment, blastocyst culture, time-lapse imaging, artificial intelligence, and preimplantation genetic testing for aneuploidy.
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Affiliation(s)
- Haowen Zou
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Victoria, Australia
| | - Rui Wang
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Victoria, Australia
| | - Dean E Morbeck
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Victoria, Australia; Principle, Morbeck Consulting Ltd, Auckland, New Zealand.
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Liu M, Lee CI, Tzeng CR, Lai HH, Huang Y, Chang TA. WISE: whole-scenario embryo identification using self-supervised learning encoder in IVF. J Assist Reprod Genet 2024; 41:967-978. [PMID: 38470553 PMCID: PMC11052951 DOI: 10.1007/s10815-024-03080-2] [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: 08/07/2023] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
PURPOSE To study the effectiveness of whole-scenario embryo identification using a self-supervised learning encoder (WISE) in in vitro fertilization (IVF) on time-lapse, cross-device, and cryo-thawed scenarios. METHODS WISE was based on the vision transformer (ViT) architecture and masked autoencoders (MAE), a self-supervised learning (SSL) method. To train WISE, we prepared three datasets including the SSL pre-training dataset, the time-lapse identification dataset, and the cross-device identification dataset. To identify whether pairs of images were from the same embryos in different scenarios in the downstream identification tasks, embryo images including time-lapse and microscope images were first pre-processed through object detection, cropping, padding, and resizing, and then fed into WISE to get predictions. RESULTS WISE could accurately identify embryos in the three scenarios. The accuracy was 99.89% on the time-lapse identification dataset, and 83.55% on the cross-device identification dataset. Besides, we subdivided a cryo-thawed evaluation set from the cross-device test set to have a better estimation of how WISE performs in the real-world, and it reached an accuracy of 82.22%. There were approximately 10% improvements in cross-device and cryo-thawed identification tasks after the SSL method was applied. Besides, WISE demonstrated improvements in the accuracy of 9.5%, 12%, and 18% over embryologists in the three scenarios. CONCLUSION SSL methods can improve embryo identification accuracy even when dealing with cross-device and cryo-thawed paired images. The study is the first to apply SSL in embryo identification, and the results show the promise of WISE for future application in embryo witnessing.
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Affiliation(s)
- Mark Liu
- Binflux, Inc., 4F.-1, No. 9, Dehui St., Zhongshan Dist., Taipei City, 10461, Taiwan.
| | - Chun-I Lee
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Obstetrics and Gynecology, Chung Shan Medical University, Taichung, Taiwan
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | | | - Hsing-Hua Lai
- Stork Fertility Center, Stork Ladies Clinic, Hsinchu City, Taiwan
| | - Yulun Huang
- Binflux, Inc., 4F.-1, No. 9, Dehui St., Zhongshan Dist., Taipei City, 10461, Taiwan
| | - T Arthur Chang
- Department of Obstetrics and Gynecology, University of Texas Health Science Center, San Antonio, TX, USA
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11
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Chen F, Xie X, Cai D, Yan P, Ding C, Wen Y, Xu Y, Gao F, Zhou C, Li G, Mai Q. Knowledge-embedded spatio-temporal analysis for euploidy embryos identification in couples with chromosomal rearrangements. Chin Med J (Engl) 2024; 137:694-703. [PMID: 37640743 PMCID: PMC10950137 DOI: 10.1097/cm9.0000000000002803] [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: 12/11/2022] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND The goal of the assisted reproductive treatment is to transfer one euploid blastocyst and to help infertile women giving birth one healthy neonate. Some algorithms have been used to assess the ploidy status of embryos derived from couples with normal chromosome, who subjected to preimplantation genetic testing for aneuploidy (PGT-A) treatment. However, it is currently unknown whether artificial intelligence model can be used to assess the euploidy status of blastocyst derived from populations with chromosomal rearrangement. METHODS From February 2020 to May 2021, we collected the whole raw time-lapse videos at multiple focal planes from in vitro cultured embryos, the clinical information of couples, and the comprehensive chromosome screening results of those blastocysts that had received PGT treatment. Initially, we developed a novel deep learning model called the Attentive Multi-Focus Selection Network (AMSNet) to analyze time-lapse videos in real time and predict blastocyst formation. Building upon AMSNet, we integrated additional clinically predictive variables and created a second deep learning model, the Attentive Multi-Focus Video and Clinical Information Fusion Network (AMCFNet), to assess the euploidy status of embryos. The efficacy of the AMCFNet was further tested in embryos with parental chromosomal rearrangements. The receiver operating characteristic curve (ROC) was used to evaluate the superiority of the model. RESULTS A total of 4112 embryos with complete time-lapse videos were enrolled for the blastocyst formation prediction task, and 1422 qualified blastocysts received PGT-A ( n = 589) or PGT for chromosomal structural rearrangement (PGT-SR, n = 833) were enrolled for the euploidy assessment task in this study. The AMSNet model using seven focal raw time-lapse videos has the best real-time accuracy. The real-time accuracy for AMSNet to predict blastocyst formation reached above 70% on the day 2 of embryo culture, and then increased to 80% on the day 4 of embryo culture. Combing with 4 clinical features of couples, the AUC of AMCFNet with 7 focal points increased to 0.729 in blastocysts derived from couples with chromosomal rearrangement. CONCLUSION Integrating seven focal raw time-lapse images of embryos and parental clinical information, AMCFNet model have the capability of assessing euploidy status in blastocysts derived from couples with chromosomal rearrangement.
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Affiliation(s)
- Fangying Chen
- Reproductive Medicine Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Reproductive Medicine, Guangzhou, Guangdong 510080, China
| | - Xiang Xie
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510006, China
| | - Du Cai
- Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510655, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China
| | - Pengxiang Yan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510006, China
| | - Chenhui Ding
- Reproductive Medicine Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Reproductive Medicine, Guangzhou, Guangdong 510080, China
| | - Yangxing Wen
- Reproductive Medicine Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Reproductive Medicine, Guangzhou, Guangdong 510080, China
| | - Yanwen Xu
- Reproductive Medicine Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Reproductive Medicine, Guangzhou, Guangdong 510080, China
| | - Feng Gao
- Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510655, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
| | - Canquan Zhou
- Reproductive Medicine Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Reproductive Medicine, Guangzhou, Guangdong 510080, China
| | - Guanbin Li
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510006, China
| | - Qingyun Mai
- Reproductive Medicine Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Reproductive Medicine, Guangzhou, Guangdong 510080, China
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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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13
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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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Lee T, Natalwala J, Chapple V, Liu Y. A brief history of artificial intelligence embryo selection: from black-box to glass-box. Hum Reprod 2024; 39:285-292. [PMID: 38061074 PMCID: PMC11016335 DOI: 10.1093/humrep/dead254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 11/21/2023] [Indexed: 02/02/2024] Open
Abstract
With the exponential growth of computing power and accumulation of embryo image data in recent years, artificial intelligence (AI) is starting to be utilized in embryo selection in IVF. Amongst different AI technologies, machine learning (ML) has the potential to reduce operator-related subjectivity in embryo selection while saving labor time on this task. However, as modern deep learning (DL) techniques, a subcategory of ML, are increasingly used, its integrated black-box attracts growing concern owing to the well-recognized issues regarding lack of interpretability. Currently, there is a lack of randomized controlled trials to confirm the effectiveness of such black-box models. Recently, emerging evidence has shown underperformance of black-box models compared to the more interpretable traditional ML models in embryo selection. Meanwhile, glass-box AI, such as interpretable ML, is being increasingly promoted across a wide range of fields and is supported by its ethical advantages and technical feasibility. In this review, we propose a novel classification system for traditional and AI-driven systems from an embryology standpoint, defining different morphology-based selection approaches with an emphasis on subjectivity, explainability, and interpretability.
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Affiliation(s)
- Tammy Lee
- Fertility North, Joondalup Private Hospital, Joondalup, Western Australia, Australia
- School of Human Sciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Jay Natalwala
- Fertility North, Joondalup Private Hospital, Joondalup, Western Australia, Australia
| | - Vincent Chapple
- Fertility North, Joondalup Private Hospital, Joondalup, Western Australia, Australia
| | - Yanhe Liu
- Fertility North, Joondalup Private Hospital, Joondalup, Western Australia, Australia
- School of Human Sciences, University of Western Australia, Crawley, Western Australia, Australia
- Faculty of Health Sciences and Medicine, Bond University, Robina, Queensland, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
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15
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Bamford T, Smith R, Young S, Evans A, Lockwood M, Easter C, Montgomery S, Barrie A, Dhillon-Smith R, Coomarasamy A, Campbell A. A comparison of morphokinetic models and morphological selection for prioritizing euploid embryos: a multicentre cohort study. Hum Reprod 2024; 39:53-61. [PMID: 37963011 DOI: 10.1093/humrep/dead237] [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: 08/20/2023] [Revised: 10/24/2023] [Indexed: 11/16/2023] Open
Abstract
STUDY QUESTION Are morphokinetic models better at prioritizing a euploid embryo for transfer over morphological selection by an embryologist? SUMMARY ANSWER Morphokinetic algorithms lead to an improved prioritization of euploid embryos when compared to embryologist selection. WHAT IS KNOWN ALREADY PREFER (predicting euploidy for embryos in reproductive medicine) is a previously published morphokinetic model associated with live birth and miscarriage. The second model uses live birth as the target outcome (LB model). STUDY DESIGN, SIZE, DURATION Data for this cohort study were obtained from 1958 biopsied blastocysts at nine IVF clinics across the UK from January 2021 to December 2022. PARTICIPANTS/MATERIALS, SETTING, METHODS The ability of the PREFER and LB models to prioritize a euploid embryo was compared against arbitrary selection and the prediction of four embryologists using the timelapse video, blinded to the morphokinetic time stamp. The comparisons were made using calculated percentages and normalized discounted cumulative gain (NDCG), whereby an NDCG score of 1 would equate to all euploid embryos being ranked first. In arbitrary selection, the ploidy status was randomly assigned within each cycle and the NDGC calculated, and this was then repeated 100 times and the mean obtained. MAIN RESULTS AND THE ROLE OF CHANCE Arbitrary embryo selection would rank a euploid embryo first 37% of the time, embryologist selection 39%, and the LB and PREFER ploidy morphokinetic models 46% and 47% of the time, respectively. The AUC for LB and PREFER model was 0.62 and 0.63, respectively. Morphological selection did not significantly improve the performance of both morphokinetic models when used in combination. There was a significant difference between the NDGC metric of the PREFER model versus embryologist selection at 0.96 and 0.87, respectively (t = 14.1, P < 0.001). Similarly, there was a significant difference between the LB model and embryologist selection with an NDGC metric of 0.95 and 0.87, respectively (t = 12.0, P < 0.001). All four embryologists ranked embryos similarly, with an intraclass coefficient of 0.91 (95% CI 0.82-0.95, P < 0.001). LIMITATIONS, REASONS FOR CAUTION Aside from the retrospective study design, limitations include allowing the embryologist to watch the time lapse video, potentially providing more information than a truly static morphological assessment. Furthermore, the embryologists at the participating centres were familiar with the significant variables in time lapse, which could bias the results. WIDER IMPLICATIONS OF THE FINDINGS The present study shows that the use of morphokinetic models, namely PREFER and LB, translates into improved euploid embryo selection. STUDY FUNDING/COMPETING INTEREST(S) This study received no specific grant funding from any funding agency in the public, commercial or not-for-profit sectors. Dr Alison Campbell is minor share holder of Care Fertility. All other authors have no conflicts of interest to declare. Time lapse is a technology for which patients are charged extra at participating centres. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- Thomas Bamford
- Tommy's National Centre for Miscarriage Research, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, UK
| | - Rachel Smith
- Care Fertility, John Webster House, Nottingham, UK
| | - Selina Young
- Care Fertility, John Webster House, Nottingham, UK
| | - Amy Evans
- Care Fertility, John Webster House, Nottingham, UK
| | | | | | | | - Amy Barrie
- Care Fertility, John Webster House, Nottingham, UK
| | - Rima Dhillon-Smith
- Tommy's National Centre for Miscarriage Research, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, UK
| | - Arri Coomarasamy
- Tommy's National Centre for Miscarriage Research, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, UK
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Serdarogullari M, Liperis G, Sharma K, Ammar OF, Uraji J, Cimadomo D, Alteri A, Popovic M, Fraire-Zamora JJ. Unpacking the artificial intelligence toolbox for embryo ploidy prediction. Hum Reprod 2023; 38:2538-2542. [PMID: 37877410 DOI: 10.1093/humrep/dead223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023] Open
Affiliation(s)
- Munevver Serdarogullari
- Department of Histology and Embryology, Faculty of Medicine, Cyprus International University, Northern Cyprus, Turkey
| | - George Liperis
- Westmead Fertility Centre, Institute of Reproductive Medicine, University of Sydney, Westmead, NSW, Australia
| | - Kashish Sharma
- HealthPlus Fertility Center, HealthPlus Network of Specialty Centers, Abu Dhabi, United Arab Emirates
| | - Omar F Ammar
- Biomaterials Cluster, Bernal Institute, University of Limerick, Limerick, Ireland
- School of Engineering, Faculty of Science and Engineering, University of Limerick, Limerick, Ireland
| | - Julia Uraji
- IVF Laboratory, TFP Düsseldorf GmbH, Düsseldorf, Germany
| | - Danilo Cimadomo
- IVIRMA Global Research Alliance, Genera, Clinica Valle Giulia, Rome, Italy
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Viñals Gonzalez X, Thrasivoulou C, Naja RP, Seshadri S, Serhal P, Gupta SS. Integrating imaging-based classification and transcriptomics for quality assessment of human oocytes according to their reproductive efficiency. J Assist Reprod Genet 2023; 40:2545-2556. [PMID: 37610606 PMCID: PMC10643756 DOI: 10.1007/s10815-023-02911-y] [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: 02/24/2023] [Accepted: 08/09/2023] [Indexed: 08/24/2023] Open
Abstract
PURPOSE Utilising non-invasive imaging parameters to assess human oocyte fertilisation, development and implantation; and their influence on transcriptomic profiles. METHODS A ranking tool was designed using imaging data from 957 metaphase II stage oocytes retrieved from 102 patients undergoing ART. Hoffman modulation contrast microscopy was conducted with an Olympus IX53 microscope. Images were acquired prior to ICSI and processed using ImageJ for optical density and grey-level co-occurrence matrices texture analysis. Single-cell RNA sequencing of twenty-three mature oocytes classified according to their competence was performed. RESULT(S) Overall fertilisation, blastulation and implantation rates were 73.0%, 62.6% and 50.8%, respectively. Three different algorithms were produced using binary logistic regression methods based on "optimal" quartiles, resulting in an accuracy of prediction of 76.6%, 67% and 80.7% for fertilisation, blastulation and implantation. Optical density, gradient, inverse difference moment (homogeneity) and entropy (structural complexity) were the parameters with highest predictive properties. The ranking tool showed high sensitivity (68.9-90.8%) but with limited specificity (26.5-62.5%) for outcome prediction. Furthermore, five differentially expressed genes were identified when comparing "good" versus "poor" competent oocytes. CONCLUSION(S) Imaging properties can be used as a tool to assess differences in the ooplasm and predict laboratory and clinical outcomes. Transcriptomic analysis suggested that oocytes with lower competence may have compromised cell cycle either by non-reparable DNA damage or insufficient ooplasmic maturation. Further development of algorithms based on image parameters is encouraged, with an increased balanced cohort and validated prospectively in multicentric studies.
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Affiliation(s)
- Xavier Viñals Gonzalez
- Preimplantation Genetics Group, Institute for Women's Health, University College London, 84-86 Chenies Mews, Bloomsbury, London, WC1E 6HU, UK.
| | - Christopher Thrasivoulou
- Research Department of Cell and Developmental Biology, University College London, Rockefeller Building, London, WC1E 6DE, UK
| | - Roy Pascal Naja
- Preimplantation Genetics Group, Institute for Women's Health, University College London, 84-86 Chenies Mews, Bloomsbury, London, WC1E 6HU, UK
| | - Srividya Seshadri
- The Centre for Reproductive and Genetic Health, 230-232 Great Portland St, Fitzrovia, W1W 5QS, London, UK
| | - Paul Serhal
- The Centre for Reproductive and Genetic Health, 230-232 Great Portland St, Fitzrovia, W1W 5QS, London, UK
| | - Sioban Sen Gupta
- Preimplantation Genetics Group, Institute for Women's Health, University College London, 84-86 Chenies Mews, Bloomsbury, London, WC1E 6HU, UK
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18
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Bamford T, Smith R, Easter C, Dhillon-Smith R, Barrie A, Montgomery S, Campbell A, Coomarasamy A. Association between a morphokinetic ploidy prediction model risk score and miscarriage and live birth: a multicentre cohort study. Fertil Steril 2023; 120:834-843. [PMID: 37307891 DOI: 10.1016/j.fertnstert.2023.06.006] [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: 02/24/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/14/2023]
Abstract
OBJECTIVE To determine whether the aneuploidy risk score from a morphokinetic ploidy prediction model, Predicting Euploidy for Embryos in Reproductive Medicine (PREFER), is associated with miscarriage and live birth outcomes. DESIGN Multicentre cohort study. SETTING Nine in vitro fertilization clinics in the United Kingdom. PATIENTS Data were obtained from the treatment of patients from 2016-2019. A total of 3587 fresh single embryo transfers were included; preimplantation genetic testing for aneuploidy) cycles were excluded. INTERVENTION PREFER is a model developed using 8,147 biopsied blastocyst specimens to predict ploidy status using morphokinetic and clinical biodata. A second model using only morphokinetic (MK) predictors was developed, P PREFER-MK. The models will categorize embryos into the following three risk score categories for aneuploidy: "high risk," "medium risk," and "low risk." MAIN OUTCOME MEASURES The primary outcomes are miscarriage and live birth. Secondary outcomes include biochemical clinical pregnancy per single embryo transfer. RESULTS When applying PREFER, the miscarriage rates were 12%, 14%, and 22% in the "low risk," "moderate risk," and "high risk" categories, respectively. Those embryos deemed "high risk" had a significantly higher egg provider age compared with "low risk," and there was little variation in risk categories in patients of the same age. The trend in miscarriage rate was not seen when using PREFER-MK; however, there was an association with live birth, increasing from 38%-49% and 50% in the "high risk," "moderate risk," and "low risk" groups, respectively. An adjusted logistic regression analysis demonstrated that PREFER-MK was not associated with miscarriage when comparing "high risk" to "moderate risk" embryos (odds ratio [OR], 0.87; 95% confidence interval [CI], 0.63-1.63) or "high risk" to "low risk" embryos (OR, 1.07; 95% CI, 0.79-1.46). An embryo deemed "low risk" by PREFER-MK was significantly more likely to result in a live birth than those embryos graded "high risk" (OR, 1.95; 95% CI, 1.65-2.25). CONCLUSION The PREFER model's risk scores were significantly associated with live births and miscarriages. Importantly, this study also found that this model applied too much weight to clinical factors, such that it could no longer rank a patient's embryos effectively. Therefore, a model including only MKs would be preferred; this was similarly associated with live birth but not miscarriage.
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Affiliation(s)
- Thomas Bamford
- Tommy's National Centre for Miscarriage Research, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom, CARE Fertility Manchester, Manchester, Greater Manchester, United Kingdom; CARE Fertility Headquarters, Nottingham, United kingdom.
| | - Rachel Smith
- CARE Fertility Headquarters, Nottingham, United kingdom
| | - Christina Easter
- Tommy's National Centre for Miscarriage Research, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom, CARE Fertility Manchester, Manchester, Greater Manchester, United Kingdom
| | - Rima Dhillon-Smith
- Tommy's National Centre for Miscarriage Research, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom, CARE Fertility Manchester, Manchester, Greater Manchester, United Kingdom
| | - Amy Barrie
- CARE Fertility Headquarters, Nottingham, United kingdom
| | | | | | - Arri Coomarasamy
- Tommy's National Centre for Miscarriage Research, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom, CARE Fertility Manchester, Manchester, Greater Manchester, United Kingdom
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19
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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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20
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Fruchter-Goldmeier Y, Kantor B, Ben-Meir A, Wainstock T, Erlich I, Levitas E, Shufaro Y, Sapir O, Har-Vardi I. An artificial intelligence algorithm for automated blastocyst morphometric parameters demonstrates a positive association with implantation potential. Sci Rep 2023; 13:14617. [PMID: 37669976 PMCID: PMC10480200 DOI: 10.1038/s41598-023-40923-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/18/2023] [Indexed: 09/07/2023] Open
Abstract
Blastocyst selection is primarily based on morphological scoring systems and morphokinetic data. These methods involve subjective grading and time-consuming techniques. Artificial intelligence allows for objective and quick blastocyst selection. In this study, 608 blastocysts were selected for transfer using morphokinetics and Gardner criteria. Retrospectively, morphometric parameters of blastocyst size, inner cell mass (ICM) size, ICM-to-blastocyst size ratio, and ICM shape were automatically measured by a semantic segmentation neural network model. The model was trained on 1506 videos with 102 videos for validation with no overlap between the ICM and trophectoderm models. Univariable logistic analysis found blastocyst size and ICM-to-blastocyst size ratio to be significantly associated with implantation potential. Multivariable regression analysis, adjusted for woman age, found blastocyst size to be significantly associated with implantation potential. The odds of implantation increased by 1.74 for embryos with a blastocyst size greater than the mean (147 ± 19.1 μm). The performance of the algorithm was represented by an area under the curve of 0.70 (p < 0.01). In conclusion, this study supports the association of a large blastocyst size with higher implantation potential and suggests that automatically measured blastocyst morphometrics can be used as a precise, consistent, and time-saving tool for improving blastocyst selection.
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Affiliation(s)
- Yael Fruchter-Goldmeier
- The Medical School for International Health and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | | | - Assaf Ben-Meir
- Fairtility Ltd., Tel Aviv, Israel
- Fertility and IVF Unit, Department of Obstetrics and Gynecology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Tamar Wainstock
- School of Public Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | | | - Eliahu Levitas
- The Medical School for International Health and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Fertility and IVF Unit, Department of Obstetrics and Gynecology, Soroka University Medical Center, Beer-Sheva, Israel
| | - Yoel Shufaro
- Infertility and IVF Unit, Beilinson Women's Hospital, Rabin Medical Center, Petach-Tikva, Israel
- The Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Onit Sapir
- Infertility and IVF Unit, Beilinson Women's Hospital, Rabin Medical Center, Petach-Tikva, Israel
- The Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Iris Har-Vardi
- The Medical School for International Health and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
- Fairtility Ltd., Tel Aviv, Israel.
- Fertility and IVF Unit, Department of Obstetrics and Gynecology, Soroka University Medical Center, Beer-Sheva, Israel.
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21
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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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22
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Salih M, Austin C, Warty RR, Tiktin C, Rolnik DL, Momeni M, Rezatofighi H, Reddy S, Smith V, Vollenhoven B, Horta F. Embryo selection through artificial intelligence versus embryologists: a systematic review. Hum Reprod Open 2023; 2023:hoad031. [PMID: 37588797 PMCID: PMC10426717 DOI: 10.1093/hropen/hoad031] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 07/17/2023] [Indexed: 08/18/2023] Open
Abstract
STUDY QUESTION What is the present performance of artificial intelligence (AI) decision support during embryo selection compared to the standard embryo selection by embryologists? SUMMARY ANSWER AI consistently outperformed the clinical teams in all the studies focused on embryo morphology and clinical outcome prediction during embryo selection assessment. WHAT IS KNOWN ALREADY The ART success rate is ∼30%, with a worrying trend of increasing female age correlating with considerably worse results. As such, there have been ongoing efforts to address this low success rate through the development of new technologies. With the advent of AI, there is potential for machine learning to be applied in such a manner that areas limited by human subjectivity, such as embryo selection, can be enhanced through increased objectivity. Given the potential of AI to improve IVF success rates, it remains crucial to review the performance between AI and embryologists during embryo selection. STUDY DESIGN SIZE DURATION The search was done across PubMed, EMBASE, Ovid Medline, and IEEE Xplore from 1 June 2005 up to and including 7 January 2022. Included articles were also restricted to those written in English. Search terms utilized across all databases for the study were: ('Artificial intelligence' OR 'Machine Learning' OR 'Deep learning' OR 'Neural network') AND ('IVF' OR 'in vitro fertili*' OR 'assisted reproductive techn*' OR 'embryo'), where the character '*' refers the search engine to include any auto completion of the search term. PARTICIPANTS/MATERIALS SETTING METHODS A literature search was conducted for literature relating to AI applications to IVF. Primary outcomes of interest were accuracy, sensitivity, and specificity of the embryo morphology grade assessments and the likelihood of clinical outcomes, such as clinical pregnancy after IVF treatments. Risk of bias was assessed using the Modified Down and Black Checklist. MAIN RESULTS AND THE ROLE OF CHANCE Twenty articles were included in this review. There was no specific embryo assessment day across the studies-Day 1 until Day 5/6 of embryo development was investigated. The types of input for training AI algorithms were images and time-lapse (10/20), clinical information (6/20), and both images and clinical information (4/20). Each AI model demonstrated promise when compared to an embryologist's visual assessment. On average, the models predicted the likelihood of successful clinical pregnancy with greater accuracy than clinical embryologists, signifying greater reliability when compared to human prediction. The AI models performed at a median accuracy of 75.5% (range 59-94%) on predicting embryo morphology grade. The correct prediction (Ground Truth) was defined through the use of embryo images according to post embryologists' assessment following local respective guidelines. Using blind test datasets, the embryologists' accuracy prediction was 65.4% (range 47-75%) with the same ground truth provided by the original local respective assessment. Similarly, AI models had a median accuracy of 77.8% (range 68-90%) in predicting clinical pregnancy through the use of patient clinical treatment information compared to 64% (range 58-76%) when performed by embryologists. When both images/time-lapse and clinical information inputs were combined, the median accuracy by the AI models was higher at 81.5% (range 67-98%), while clinical embryologists had a median accuracy of 51% (range 43-59%). LIMITATIONS REASONS FOR CAUTION The findings of this review are based on studies that have not been prospectively evaluated in a clinical setting. Additionally, a fair comparison of all the studies were deemed unfeasible owing to the heterogeneity of the studies, development of the AI models, database employed and the study design and quality. WIDER IMPLICATIONS OF THE FINDINGS AI provides considerable promise to the IVF field and embryo selection. However, there needs to be a shift in developers' perception of the clinical outcome from successful implantation towards ongoing pregnancy or live birth. Additionally, existing models focus on locally generated databases and many lack external validation. STUDY FUNDING/COMPETING INTERESTS This study was funded by Monash Data Future Institute. All authors have no conflicts of interest to declare. REGISTRATION NUMBER CRD42021256333.
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Affiliation(s)
- M Salih
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - C Austin
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - R R Warty
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
| | - C Tiktin
- School of Engineering, RMIT University, Melbourne, Victoria, Australia
| | - D L Rolnik
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Women’s and Newborn Program, Monash Health, Melbourne, Victoria, Australia
| | - M Momeni
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
| | - H Rezatofighi
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
- Monash Data Future Institute, Monash University, Clayton, Victoria, Australia
| | - S Reddy
- School of Medicine, Deakin University, Geelong, Victoria, Australia
| | - V Smith
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
| | - B Vollenhoven
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Women’s and Newborn Program, Monash Health, Melbourne, Victoria, Australia
- Monash IVF, Melbourne, Victoria, Australia
| | - F Horta
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Monash Data Future Institute, Monash University, Clayton, Victoria, Australia
- City Fertility, Melbourne, Victoria, Australia
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23
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Paya E, Pulgarín C, Bori L, Colomer A, Naranjo V, Meseguer M. Deep learning system for classification of ploidy status using time-lapse videos. F&S SCIENCE 2023; 4:211-218. [PMID: 37394179 DOI: 10.1016/j.xfss.2023.06.002] [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: 02/23/2023] [Revised: 06/27/2023] [Accepted: 06/27/2023] [Indexed: 07/04/2023]
Abstract
OBJECTIVE To develop a spatiotemporal model for de prediction of euploid and aneuploid embryos using time-lapse videos from 10-115 hours after insemination (hpi). DESIGN Retrospective study. MAIN OUTCOME MEASURES The research used an end-to-end approach to develop an automated artificial intelligence system capable of extracting features from images and classifying them, considering spatiotemporal dependencies. A convolutional neural network extracted the most relevant features from each video frame. A bidirectional long short-term memory layer received this information and analyzed the temporal dependencies, obtaining a low-dimensional feature vector that characterized each video. A multilayer perceptron classified them into 2 groups, euploid and noneuploid. RESULTS The model performance in accuracy fell between 0.6170 and 0.7308. A multi-input model with a gate recurrent unit module performed better than others; the precision (or positive predictive value) is 0.8205 for predicting euploidy. Sensitivity, specificity, F1-Score and accuracy are 0.6957, 0.7813, 0.7042, and 0.7308, respectively. CONCLUSIONS This article proposes an artificial intelligence solution for prioritizing euploid embryo transfer. We can highlight the identification of a noninvasive method for chromosomal status diagnosis using a deep learning approach that analyzes raw data provided by time-lapse incubators. This method demonstrated potential automation of the evaluation process, allowing spatial and temporal information to encode.
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Affiliation(s)
- Elena Paya
- Instituto de Investigación e Innovación en Bioingeniería (I3B), Universitat Politècnica de Valencia, Spain; IVIRMA Valencia, Spain.
| | - Cristian Pulgarín
- Instituto de Investigación e Innovación en Bioingeniería (I3B), Universitat Politècnica de Valencia, Spain
| | | | - Adrián Colomer
- Instituto de Investigación e Innovación en Bioingeniería (I3B), Universitat Politècnica de Valencia, Spain
| | - Valery Naranjo
- Instituto de Investigación e Innovación en Bioingeniería (I3B), Universitat Politècnica de Valencia, Spain
| | - Marcos Meseguer
- IVIRMA Valencia, Spain; Health Research Institute la Fe, Valencia, Spain
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24
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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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25
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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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26
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Abdullah KAL, Atazhanova T, Chavez-Badiola A, Shivhare SB. Automation in ART: Paving the Way for the Future of Infertility Treatment. Reprod Sci 2023; 30:1006-1016. [PMID: 35922741 PMCID: PMC10160149 DOI: 10.1007/s43032-022-00941-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/09/2022] [Indexed: 01/11/2023]
Abstract
In vitro fertilisation (IVF) is estimated to account for the birth of more than nine million babies worldwide, perhaps making it one of the most intriguing as well as commoditised and industrialised modern medical interventions. Nevertheless, most IVF procedures are currently limited by accessibility, affordability and most importantly multistep, labour-intensive, technically challenging processes undertaken by skilled professionals. Therefore, in order to sustain the exponential demand for IVF on one hand, and streamline existing processes on the other, innovation is essential. This may not only effectively manage clinical time but also reduce cost, thereby increasing accessibility, affordability and efficiency. Recent years have seen a diverse range of technologies, some integrated with artificial intelligence, throughout the IVF pathway, which promise personalisation and, at least, partial automation in the not-so-distant future. This review aims to summarise the rapidly evolving state of these innovations in automation, with or without the integration of artificial intelligence, encompassing the patient treatment pathway, gamete/embryo selection, endometrial evaluation and cryopreservation of gametes/embryos. Additionally, it shall highlight the resulting prospective change in the role of IVF professionals and challenges of implementation of some of these technologies, thereby aiming to motivate continued research in this field.
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Affiliation(s)
- Kadrina Abdul Latif Abdullah
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Level 3, Women's Centre, John Radcliffe Hospital, Oxford, OX3 9DU, England
| | - Tomiris Atazhanova
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Level 3, Women's Centre, John Radcliffe Hospital, Oxford, OX3 9DU, England
| | | | - Sourima Biswas Shivhare
- TFP Simply Fertility, W Hanningfield Rd, Great Baddow, Chelmsford, CM2 8HN, England.
- The Centre for Reproductive and Genetic Health, London, UK.
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27
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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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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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Utilization of standardized preimplantation genetic testing for aneuploidy (PGT-A) via artificial intelligence (AI) technology is correlated with improved pregnancy outcomes in single thawed euploid embryo transfer (STEET) cycles. J Assist Reprod Genet 2023; 40:289-299. [PMID: 36609941 PMCID: PMC9935782 DOI: 10.1007/s10815-022-02695-7] [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: 06/02/2022] [Accepted: 12/13/2022] [Indexed: 01/09/2023] Open
Abstract
PURPOSE To investigate the role of standardized preimplantation genetic testing for aneuploidy (PGT-A) using artificial intelligence (AI) in patients undergoing single thawed euploid embryo transfer (STEET) cycles. METHODS Retrospective cohort study at a single, large university-based fertility center with patients undergoing in vitro fertilization (IVF) utilizing PGT-A from February 2015 to April 2020. Controls included embryos tested using subjective NGS. The first experimental group included embryos analyzed by NGS utilizing AI and machine learning (PGTaiSM Technology Platform, AI 1.0). The second group included embryos analyzed by AI 1.0 and SNP analysis (PGTai2.0, AI 2.0). Primary outcomes included rates of euploidy, aneuploidy and simple mosaicism. Secondary outcomes included rates of implantation (IR), clinical pregnancy (CPR), biochemical pregnancy (BPR), spontaneous abortion (SABR) and ongoing pregnancy and/or live birth (OP/LBR). RESULTS A total of 24,908 embryos were analyzed, and classification rates using AI platforms were compared to subjective NGS. Overall, those tested via AI 1.0 showed a significantly increased euploidy rate (36.6% vs. 28.9%), decreased simple mosaicism rate (11.3% vs. 14.0%) and decreased aneuploidy rate (52.1% vs. 57.0%). Overall, those tested via AI 2.0 showed a significantly increased euploidy rate (35.0% vs. 28.9%) and decreased simple mosaicism rate (10.1% vs. 14.0%). Aneuploidy rate was insignificantly decreased when comparing AI 2.0 to NGS (54.8% vs. 57.0%). A total of 1,174 euploid embryos were transferred. The OP/LBR was significantly higher in the AI 2.0 group (70.3% vs. 61.7%). The BPR was significantly lower in the AI 2.0 group (4.6% vs. 11.8%). CONCLUSION Standardized PGT-A via AI significantly increases euploidy classification rates and OP/LBR, and decreases BPR when compared to standard NGS.
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Jiang VS, Kandula H, Thirumalaraju P, Kanakasabapathy MK, Cherouveim P, Souter I, Dimitriadis I, Bormann CL, Shafiee H. The use of voting ensembles to improve the accuracy of deep neural networks as a non-invasive method to predict embryo ploidy status. J Assist Reprod Genet 2023; 40:301-308. [PMID: 36640251 PMCID: PMC9935776 DOI: 10.1007/s10815-022-02707-6] [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: 06/16/2022] [Accepted: 12/23/2022] [Indexed: 01/15/2023] Open
Abstract
PURPOSE To determine if creating voting ensembles combining convolutional neural networks (CNN), support vector machine (SVM), and multi-layer neural networks (NN) alongside clinical parameters improves the accuracy of artificial intelligence (AI) as a non-invasive method for predicting aneuploidy. METHODS A cohort of 699 day 5 PGT-A tested blastocysts was used to train, validate, and test a CNN to classify embryos as euploid/aneuploid. All embryos were analyzed using a modified FAST-SeqS next-generation sequencing method. Patient characteristics such as maternal age, AMH level, paternal sperm quality, and total number of normally fertilized (2PN) embryos were processed using SVM and NN. To improve model performance, we created voting ensembles using CNN, SVM, and NN to combine our imaging data with clinical parameter variations. Statistical significance was evaluated with a one-sample t-test with 2 degrees of freedom. RESULTS When assessing blastocyst images alone, the CNN test accuracy was 61.2% (± 1.32% SEM, n = 3 models) in correctly classifying euploid/aneuploid embryos (n = 140 embryos). When the best CNN model was assessed as a voting ensemble, the test accuracy improved to 65.0% (AMH; p = 0.1), 66.4% (maternal age; p = 0.06), 65.7% (maternal age, AMH; p = 0.08), 66.4% (maternal age, AMH, number of 2PNs; p = 0.06), and 71.4% (maternal age, AMH, number of 2PNs, sperm quality; p = 0.02) (n = 140 embryos). CONCLUSIONS By combining CNNs with patient characteristics, voting ensembles can be created to improve the accuracy of classifying embryos as euploid/aneuploid from CNN alone, allowing for AI to serve as a potential non-invasive method to aid in karyotype screening and selection of embryos.
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Affiliation(s)
- Victoria S Jiang
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, VincentBoston, MA, 02114, USA
| | - Hemanth Kandula
- Division of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Prudhvi Thirumalaraju
- Division of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Manoj Kumar Kanakasabapathy
- Division of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Panagiotis Cherouveim
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, VincentBoston, MA, 02114, USA
| | - Irene Souter
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, VincentBoston, MA, 02114, USA
| | - Irene Dimitriadis
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, VincentBoston, MA, 02114, USA
| | - Charles L Bormann
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, VincentBoston, MA, 02114, USA.
| | - Hadi Shafiee
- Division of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA.
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Automated identification of blastocyst regions at different development stages. Sci Rep 2023; 13:15. [PMID: 36593239 PMCID: PMC9807603 DOI: 10.1038/s41598-022-26386-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 12/14/2022] [Indexed: 01/04/2023] Open
Abstract
The selection of the best single blastocyst for transfer is typically based on the assessment of the morphological characteristics of the zona pellucida (ZP), trophectoderm (TE), blastocoel (BC), and inner cell-mass (ICM), using subjective and observer-dependent grading protocols. We propose the first automatic method for segmenting all morphological structures during the different developmental stages of the blastocyst (i.e., expansion, hatching, and hatched). Our database contains 592 original raw images that were augmented to 2132 for training and 55 for validation. The mean Dice similarity coefficient (DSC) was 0.87 for all pixels, and for the BC, BG (background), ICM, TE, and ZP was 0.85, 0.96, 0.54, 0.63, and 0.71, respectively. Additionally, we tested our method against a public repository of 249 images resulting in accuracies of 0.96 and 0.93 and DSC of 0.67 and 0.67 for ICM and TE, respectively. A sensitivity analysis demonstrated that our method is robust, especially for the BC, BG, TE, and ZP. It is concluded that our approach can automatically segment blastocysts from different laboratory settings and developmental phases of the blastocysts, all within a single pipeline. This approach could increase the knowledge base for embryo selection.
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Barnes J, Brendel M, Gao VR, Rajendran S, Kim J, Li Q, Malmsten JE, Sierra JT, Zisimopoulos P, Sigaras A, Khosravi P, Meseguer M, Zhan Q, Rosenwaks Z, Elemento O, Zaninovic N, Hajirasouliha I. A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study. Lancet Digit Health 2023; 5:e28-e40. [PMID: 36543475 PMCID: PMC10193126 DOI: 10.1016/s2589-7500(22)00213-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 10/19/2022] [Accepted: 10/28/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND One challenge in the field of in-vitro fertilisation is the selection of the most viable embryos for transfer. Morphological quality assessment and morphokinetic analysis both have the disadvantage of intra-observer and inter-observer variability. A third method, preimplantation genetic testing for aneuploidy (PGT-A), has limitations too, including its invasiveness and cost. We hypothesised that differences in aneuploid and euploid embryos that allow for model-based classification are reflected in morphology, morphokinetics, and associated clinical information. METHODS In this retrospective study, we used machine-learning and deep-learning approaches to develop STORK-A, a non-invasive and automated method of embryo evaluation that uses artificial intelligence to predict embryo ploidy status. Our method used a dataset of 10 378 embryos that consisted of static images captured at 110 h after intracytoplasmic sperm injection, morphokinetic parameters, blastocyst morphological assessments, maternal age, and ploidy status. Independent and external datasets, Weill Cornell Medicine EmbryoScope+ (WCM-ES+; Weill Cornell Medicine Center of Reproductive Medicine, NY, USA) and IVI Valencia (IVI Valencia, Health Research Institute la Fe, Valencia, Spain) were used to test the generalisability of STORK-A and were compared measuring accuracy and area under the receiver operating characteristic curve (AUC). FINDINGS Analysis and model development included the use of 10 378 embryos, all with PGT-A results, from 1385 patients (maternal age range 21-48 years; mean age 36·98 years [SD 4·62]). STORK-A predicted aneuploid versus euploid embryos with an accuracy of 69·3% (95% CI 66·9-71·5; AUC 0·761; positive predictive value [PPV] 76·1%; negative predictive value [NPV] 62·1%) when using images, maternal age, morphokinetics, and blastocyst score. A second classification task trained to predict complex aneuploidy versus euploidy and single aneuploidy produced an accuracy of 74·0% (95% CI 71·7-76·1; AUC 0·760; PPV 54·9%; NPV 87·6%) using an image, maternal age, morphokinetic parameters, and blastocyst grade. A third classification task trained to predict complex aneuploidy versus euploidy had an accuracy of 77·6% (95% CI 75·0-80·0; AUC 0·847; PPV 76·7%; NPV 78·0%). STORK-A reported accuracies of 63·4% (AUC 0·702) on the WCM-ES+ dataset and 65·7% (AUC 0·715) on the IVI Valencia dataset, when using an image, maternal age, and morphokinetic parameters, similar to the STORK-A test dataset accuracy of 67·8% (AUC 0·737), showing generalisability. INTERPRETATION As a proof of concept, STORK-A shows an ability to predict embryo ploidy in a non-invasive manner and shows future potential as a standardised supplementation to traditional methods of embryo selection and prioritisation for implantation or recommendation for PGT-A. FUNDING US National Institutes of Health.
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Affiliation(s)
- Josue Barnes
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Matthew Brendel
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Vianne R Gao
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA; Tri-Institutional Computational Biology & Medicine Program, Cornell University, NY, USA
| | - Suraj Rajendran
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA; Tri-Institutional Computational Biology & Medicine Program, Cornell University, NY, USA
| | - Junbum Kim
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Qianzi Li
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA; Tri-Institutional Computational Biology & Medicine Program, Cornell University, NY, USA
| | - Jonas E Malmsten
- Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, NY, USA
| | | | - Pantelis Zisimopoulos
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Alexandros Sigaras
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Pegah Khosravi
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA; Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marcos Meseguer
- IVI Valencia, Health Research Institute la Fe, Valencia, Spain
| | - Qiansheng Zhan
- Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Zev Rosenwaks
- Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Olivier Elemento
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA; Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA; WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA
| | - Nikica Zaninovic
- Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Iman Hajirasouliha
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA; Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
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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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Bamford T, Barrie A, Montgomery S, Dhillon-Smith R, Campbell A, Easter C, Coomarasamy A. Morphological and morphokinetic associations with aneuploidy: a systematic review and meta-analysis. Hum Reprod Update 2022; 28:656-686. [PMID: 35613016 DOI: 10.1093/humupd/dmac022] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/10/2022] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND A time lapse system (TLS) is utilized in some fertility clinics with the aim of predicting embryo viability and chance of live birth during IVF. It has been hypothesized that aneuploid embryos display altered morphokinetics as a consequence of their abnormal chromosome complement. Since aneuploidy is one of the fundamental reasons for IVF failure and miscarriage, attention has focused on utilizing morphokinetics to develop models to non-invasively risk stratify embryos for ploidy status. This could avoid or reduce the costs associated with pre-implantation genetic testing for aneuploidy (PGT-A). Furthermore, TLS have provided an understanding of the true prevalence of other dysmorphisms. Hypothetically, the incorporation of morphological features into a model could act synergistically, improving a model's discriminative ability to predict ploidy status. OBJECTIVE AND RATIONALE The aim of this systematic review and meta-analysis was to investigate associations between ploidy status and morphokinetic or morphological features commonly denoted on a TLS. This will determine the feasibility of a prediction model for euploidy and summarize the most useful prognostic markers to be included in model development. SEARCH METHODS Five separate searches were conducted in Medline, Embase, PubMed and Cinahl from inception to 1 July 2021. Search terms and word variants included, among others, PGT-A, ploidy, morphokinetics and time lapse, and the latter were successively substituted for the following morphological parameters: fragmentation, multinucleation, abnormal cleavage and contraction. Studies were limited to human studies. OUTCOMES Overall, 58 studies were included incorporating over 40 000 embryos. All except one study had a moderate risk of bias in at least one domain when assessed by the quality in prognostic studies tool. Ten morphokinetic variables were significantly delayed in aneuploid embryos. When excluding studies using less reliable genetic technologies, the most notable variables were: time to eight cells (t8, 1.13 h, 95% CI: 0.21-2.05; three studies; n = 742; I2 = 0%), t9 (2.27 h, 95% CI: 0.5-4.03; two studies; n = 671; I2 = 33%), time to formation of a full blastocyst (tB, 1.99 h, 95% CI 0.15-3.81; four studies; n = 1640; I2 = 76%) and time to expanded blastocyst (tEB, 2.35 h, 95% CI: 0.06-4.63; four studies; n = 1640; I2 = 83%). There is potentially some prognostic potential in the degree of fragmentation, multinucleation persisting to the four-cell stage and frequency of embryo contractions. Reverse cleavage was associated with euploidy in this meta-analysis; however, this article argues that these are likely spurious results requiring further investigation. There was no association with direct unequal cleavage in an embryo that progressed to a blastocyst, or with multinucleation assessed on Day 2 or at the two-cell stage. However, owing to heterogeneous results and poor-quality evidence, associations between these morphological components needs to be investigated further before conclusions can be reliably drawn. WIDER IMPLICATIONS This first systematic review and meta-analysis of morphological and morphokinetic associations with ploidy status demonstrates the most useful morphokinetic variables, namely t8, t9 and tEB to be included in future model development. There is considerable variability within aneuploid and euploid embryos making definitively classifying them impossible; however, it is feasible that embryos could be prioritized for biopsy. Furthermore, these results support the mechanism by which algorithms for live birth may have predictive ability, suggesting aneuploidy causes delayed cytokinesis. We highlight significant heterogeneity in our results secondary to local conditions and diverse patient populations, therefore calling for future models to be robustly developed and tested in-house. If successful, such a model would constitute a meaningful breakthrough when accessing PGT-A is unsuitable for couples.
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Affiliation(s)
| | | | | | - Rima Dhillon-Smith
- Tommy's National Centre for Miscarriage Research, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, UK
| | | | - Christina Easter
- Tommy's National Centre for Miscarriage Research, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, UK
| | - Arri Coomarasamy
- Tommy's National Centre for Miscarriage Research, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, UK
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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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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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Diakiw SM, Hall JMM, VerMilyea MD, Amin J, Aizpurua J, Giardini L, Briones YG, Lim AYX, Dakka MA, Nguyen TV, Perugini D, Perugini M. Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF. Hum Reprod 2022; 37:1746-1759. [PMID: 35674312 PMCID: PMC9340116 DOI: 10.1093/humrep/deac131] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 05/17/2022] [Indexed: 11/14/2022] Open
Abstract
STUDY QUESTION Can an artificial intelligence (AI) model predict human embryo ploidy status using static images captured by optical light microscopy? SUMMARY ANSWER Results demonstrated predictive accuracy for embryo euploidy and showed a significant correlation between AI score and euploidy rate, based on assessment of images of blastocysts at Day 5 after IVF. WHAT IS KNOWN ALREADY Euploid embryos displaying the normal human chromosomal complement of 46 chromosomes are preferentially selected for transfer over aneuploid embryos (abnormal complement), as they are associated with improved clinical outcomes. Currently, evaluation of embryo genetic status is most commonly performed by preimplantation genetic testing for aneuploidy (PGT-A), which involves embryo biopsy and genetic testing. The potential for embryo damage during biopsy, and the non-uniform nature of aneuploid cells in mosaic embryos, has prompted investigation of additional, non-invasive, whole embryo methods for evaluation of embryo genetic status. STUDY DESIGN, SIZE, DURATION A total of 15 192 blastocyst-stage embryo images with associated clinical outcomes were provided by 10 different IVF clinics in the USA, India, Spain and Malaysia. The majority of data were retrospective, with two additional prospectively collected blind datasets provided by IVF clinics using the genetics AI model in clinical practice. Of these images, a total of 5050 images of embryos on Day 5 of in vitro culture were used for the development of the AI model. These Day 5 images were provided for 2438 consecutively treated women who had undergone IVF procedures in the USA between 2011 and 2020. The remaining images were used for evaluation of performance in different settings, or otherwise excluded for not matching the inclusion criteria. PARTICIPANTS/MATERIALS, SETTING, METHODS The genetics AI model was trained using static 2-dimensional optical light microscope images of Day 5 blastocysts with linked genetic metadata obtained from PGT-A. The endpoint was ploidy status (euploid or aneuploid) based on PGT-A results. Predictive accuracy was determined by evaluating sensitivity (correct prediction of euploid), specificity (correct prediction of aneuploid) and overall accuracy. The Matthew correlation coefficient and receiver-operating characteristic curves and precision-recall curves (including AUC values), were also determined. Performance was also evaluated using correlation analyses and simulated cohort studies to evaluate ranking ability for euploid enrichment. MAIN RESULTS AND THE ROLE OF CHANCE Overall accuracy for the prediction of euploidy on a blind test dataset was 65.3%, with a sensitivity of 74.6%. When the blind test dataset was cleansed of poor quality and mislabeled images, overall accuracy increased to 77.4%. This performance may be relevant to clinical situations where confounding factors, such as variability in PGT-A testing, have been accounted for. There was a significant positive correlation between AI score and the proportion of euploid embryos, with very high scoring embryos (9.0-10.0) twice as likely to be euploid than the lowest-scoring embryos (0.0-2.4). When using the genetics AI model to rank embryos in a cohort, the probability of the top-ranked embryo being euploid was 82.4%, which was 26.4% more effective than using random ranking, and ∼13-19% more effective than using the Gardner score. The probability increased to 97.0% when considering the likelihood of one of the top two ranked embryos being euploid, and the probability of both top two ranked embryos being euploid was 66.4%. Additional analyses showed that the AI model generalized well to different patient demographics and could also be used for the evaluation of Day 6 embryos and for images taken using multiple time-lapse systems. Results suggested that the AI model could potentially be used to differentiate mosaic embryos based on the level of mosaicism. LIMITATIONS, REASONS FOR CAUTION While the current investigation was performed using both retrospectively and prospectively collected data, it will be important to continue to evaluate real-world use of the genetics AI model. The endpoint described was euploidy based on the clinical outcome of PGT-A results only, so predictive accuracy for genetic status in utero or at birth was not evaluated. Rebiopsy studies of embryos using a range of PGT-A methods indicated a degree of variability in PGT-A results, which must be considered when interpreting the performance of the AI model. WIDER IMPLICATIONS OF THE FINDINGS These findings collectively support the use of this genetics AI model for the evaluation of embryo ploidy status in a clinical setting. Results can be used to aid in prioritizing and enriching for embryos that are likely to be euploid for multiple clinical purposes, including selection for transfer in the absence of alternative genetic testing methods, selection for cryopreservation for future use or selection for further confirmatory PGT-A testing, as required. STUDY FUNDING/COMPETING INTEREST(S) Life Whisperer Diagnostics is a wholly owned subsidiary of the parent company, Presagen Holdings Pty Ltd. Funding for the study was provided by Presagen with grant funding received from the South Australian Government: Research, Commercialisation, and Startup Fund (RCSF). 'In kind' support and embryology expertise to guide algorithm development were provided by Ovation Fertility. 'In kind' support in terms of computational resources provided through the Amazon Web Services (AWS) Activate Program. J.M.M.H., D.P. and M.P. are co-owners of Life Whisperer and Presagen. S.M.D., M.A.D. and T.V.N. are employees or former employees of Life Whisperer. S.M.D, J.M.M.H, M.A.D, T.V.N., D.P. and M.P. are listed as inventors of patents relating to this work, and also have stock options in the parent company Presagen. M.V. sits on the advisory board for the global distributor of the technology described in this study and also received support for attending meetings. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- S M Diakiw
- Life Whisperer Diagnostics (a subsidiary of Presagen), San Francisco, CA, USA, and Adelaide, SA, Australia
| | - J M M Hall
- Life Whisperer Diagnostics (a subsidiary of Presagen), San Francisco, CA, USA, and Adelaide, SA, Australia.,Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, The University of Adelaide, Adelaide, SA, Australia.,School of Physical Sciences, Faculty of Sciences, The University of Adelaide, Adelaide, SA, Australia
| | - M D VerMilyea
- Ovation Fertility, Austin, TX, USA.,Texas Fertility Center, Austin, TX, USA
| | - J Amin
- Wings IVF Women's Hospital, Ahmedabad, Gujarat, India
| | | | | | | | - A Y X Lim
- Alpha IVF & Women's Specialists, Petaling Jaya, Selangor, Malaysia
| | - M A Dakka
- Life Whisperer Diagnostics (a subsidiary of Presagen), San Francisco, CA, USA, and Adelaide, SA, Australia
| | - T V Nguyen
- Life Whisperer Diagnostics (a subsidiary of Presagen), San Francisco, CA, USA, and Adelaide, SA, Australia
| | - D Perugini
- Life Whisperer Diagnostics (a subsidiary of Presagen), San Francisco, CA, USA, and Adelaide, SA, Australia
| | - M Perugini
- Life Whisperer Diagnostics (a subsidiary of Presagen), San Francisco, CA, USA, and Adelaide, SA, Australia.,Adelaide Medical School, Faculty of Health Sciences, The University of Adelaide, Adelaide, SA, Australia
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Zou Y, Pan Y, Ge N, Xu Y, Gu R, Li Z, Fu J, Gao J, Sun X, Sun Y. Can the combination of time-lapse parameters and clinical features predict embryonic ploidy status and implantation outcome of transferred euploid blastocysts: an artificial intelligence perspective. Reprod Biomed Online 2022; 45:643-651. [DOI: 10.1016/j.rbmo.2022.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/06/2022] [Accepted: 06/10/2022] [Indexed: 11/28/2022]
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Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis. Biomedicines 2022; 10:biomedicines10030697. [PMID: 35327499 PMCID: PMC8945147 DOI: 10.3390/biomedicines10030697] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/08/2022] [Accepted: 03/13/2022] [Indexed: 12/04/2022] Open
Abstract
Artificial intelligence (AI) has been gaining support in the field of in vitro fertilization (IVF). Despite the promising existing data, AI cannot yet claim gold-standard status, which serves as the rationale for this study. This systematic review and data synthesis aims to evaluate and report on the predictive capabilities of AI-based prediction models regarding IVF outcome. The study has been registered in PROSPERO (CRD42021242097). Following a systematic search of the literature in Pubmed/Medline, Embase, and Cochrane Central Library, 18 studies were identified as eligible for inclusion. Regarding live-birth, the Area Under the Curve (AUC) of the Summary Receiver Operating Characteristics (SROC) was 0.905, while the partial AUC (pAUC) was 0.755. The Observed: Expected ratio was 1.12 (95%CI: 0.26–2.37; 95%PI: 0.02–6.54). Regarding clinical pregnancy with fetal heartbeat, the AUC of the SROC was 0.722, while the pAUC was 0.774. The O:E ratio was 0.77 (95%CI: 0.54–1.05; 95%PI: 0.21–1.62). According to this data synthesis, the majority of the AI-based prediction models are successful in accurately predicting the IVF outcome regarding live birth, clinical pregnancy, clinical pregnancy with fetal heartbeat, and ploidy status. This review attempted to compare between AI and human prediction capabilities, and although studies do not allow for a meta-analysis, this systematic review indicates that the AI-based prediction models perform rather similarly to the embryologists’ evaluations. While AI models appear marginally more effective, they still have some way to go before they can claim to significantly surpass the clinical embryologists’ predictive competence.
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Erlich I, Ben-Meir A, Har-Vardi I, Grifo J, Wang F, Mccaffrey C, McCulloh D, Or Y, Wolf L. Pseudo contrastive labeling for predicting IVF embryo developmental potential. Sci Rep 2022; 12:2488. [PMID: 35169194 PMCID: PMC8847488 DOI: 10.1038/s41598-022-06336-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/27/2022] [Indexed: 01/04/2023] Open
Abstract
In vitro fertilization is typically associated with high failure rates per transfer,
leading to an acute need for the identification of embryos with high developmental potential. Current methods are tailored to specific times after fertilization, often require expert inspection, and have low predictive power. Automatic methods are challenged by ambiguous labels, clinical heterogeneity, and the inability to utilize multiple developmental points. In this work, we propose a novel method that trains a classifier conditioned on the time since fertilization. This classifier is then integrated over time and its output is used to assign soft labels to pairs of samples. The classifier obtained by training on these soft labels presents a significant improvement in accuracy, even as early as 30 h post-fertilization. By integrating the classification scores, the predictive power is further improved. Our results are superior to previously reported methods, including the commercial KIDScore-D3 system, and a group of eight senior professionals, in classifying multiple groups of favorable embryos into groups defined as less favorable based on implantation outcomes, expert decisions based on developmental trajectories, and/or genetic tests.
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Affiliation(s)
- I Erlich
- The Alexender Grass Center for Bioengineering, School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, Israel. .,Fairtilty Ltd., Tel Aviv, Israel.
| | - A Ben-Meir
- Fairtilty Ltd., Tel Aviv, Israel.,Infertility and IVF Unit, Department of Obstetrics and Gynecology, Hadassah Ein-Kerem Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - I Har-Vardi
- Fairtilty Ltd., Tel Aviv, Israel.,Fertility and IVF Unit, Department of Obstetrics and Gynecology, Soroka University Medical Center and the Faculty of Health Sciences Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - J Grifo
- New York University Langone Prelude Fertility Center, New York, NY, USA
| | - F Wang
- New York University Langone Prelude Fertility Center, New York, NY, USA
| | - C Mccaffrey
- New York University Langone Prelude Fertility Center, New York, NY, USA
| | - D McCulloh
- New York University Langone Prelude Fertility Center, New York, NY, USA
| | - Y Or
- Fertility and IVF Unit, Obstetrics and Gynecology Division, Kaplan Medical Center, Rehovot, Israel
| | - L Wolf
- The School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos. Fertil Steril 2022; 117:528-535. [PMID: 34998577 DOI: 10.1016/j.fertnstert.2021.11.022] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 11/09/2021] [Accepted: 11/10/2021] [Indexed: 11/21/2022]
Abstract
OBJECTIVE To perform a series of analyses characterizing an artificial intelligence (AI) model for ranking blastocyst-stage embryos. The primary objective was to evaluate the benefit of the model for predicting clinical pregnancy, whereas the secondary objective was to identify limitations that may impact clinical use. DESIGN Retrospective study. SETTING Consortium of 11 assisted reproductive technology centers in the United States. PATIENT(S) Static images of 5,923 transferred blastocysts and 2,614 nontransferred aneuploid blastocysts. INTERVENTION(S) None. MAIN OUTCOME MEASURE(S) Prediction of clinical pregnancy (fetal heartbeat). RESULT(S) The area under the curve of the AI model ranged from 0.6 to 0.7 and outperformed manual morphology grading overall and on a per-site basis. A bootstrapped study predicted improved pregnancy rates between +5% and +12% per site using AI compared with manual grading using an inverted microscope. One site that used a low-magnification stereo zoom microscope did not show predicted improvement with the AI. Visualization techniques and attribution algorithms revealed that the features learned by the AI model largely overlap with the features of manual grading systems. Two sources of bias relating to the type of microscope and presence of embryo holding micropipettes were identified and mitigated. The analysis of AI scores in relation to pregnancy rates showed that score differences of ≥0.1 (10%) correspond with improved pregnancy rates, whereas score differences of <0.1 may not be clinically meaningful. CONCLUSION(S) This study demonstrates the potential of AI for ranking blastocyst stage embryos and highlights potential limitations related to image quality, bias, and granularity of scores.
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Bormann CL, Curchoe CL. AIM in Medical Disorders in Pregnancy. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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De Gheselle S, Jacques C, Chambost J, Blank C, Declerck K, De Croo I, Hickman C, Tilleman K. Machine learning for prediction of euploidy in human embryos: in search of the best-performing model and predictive features. Fertil Steril 2022; 117:738-746. [DOI: 10.1016/j.fertnstert.2021.11.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/17/2021] [Accepted: 11/18/2021] [Indexed: 11/25/2022]
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Huang B, Tan W, Li Z, Jin L. An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data. Reprod Biol Endocrinol 2021; 19:185. [PMID: 34903224 PMCID: PMC8667440 DOI: 10.1186/s12958-021-00864-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 11/22/2021] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND For the association between time-lapse technology (TLT) and embryo ploidy status, there has not yet been fully understood. TLT has the characteristics of large amount of data and non-invasiveness. If we want to accurately predict embryo ploidy status from TLT, artificial intelligence (AI) technology is a good choice. However, the current work of AI in this field needs to be strengthened. METHODS A total of 469 preimplantation genetic testing (PGT) cycles and 1803 blastocysts from April 2018 to November 2019 were included in the study. All embryo images are captured during 5 or 6 days after fertilization before biopsy by time-lapse microscope system. All euploid embryos or aneuploid embryos are used as data sets. The data set is divided into training set, validation set and test set. The training set is mainly used for model training, the validation set is mainly used to adjust the hyperparameters of the model and the preliminary evaluation of the model, and the test set is used to evaluate the generalization ability of the model. For better verification, we used data other than the training data for external verification. A total of 155 PGT cycles from December 2019 to December 2020 and 523 blastocysts were included in the verification process. RESULTS The euploid prediction algorithm (EPA) was able to predict euploid on the testing dataset with an area under curve (AUC) of 0.80. CONCLUSIONS The TLT incubator has gradually become the choice of reproductive centers. Our AI model named EPA that can predict embryo ploidy well based on TLT data. We hope that this system can serve all in vitro fertilization and embryo transfer (IVF-ET) patients in the future, allowing embryologists to have more non-invasive aids when selecting the best embryo to transfer.
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Affiliation(s)
- Bo Huang
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, 430030, Wuhan, People's Republic of China
| | - Wei Tan
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, 430030, Wuhan, People's Republic of China
| | - Zhou Li
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, 430030, Wuhan, People's Republic of China.
| | - Lei Jin
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, 430030, Wuhan, People's Republic of China.
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Guo X. Intelligent Sports Video Classification Based on Deep Neural Network (DNN) Algorithm and Transfer Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:1825273. [PMID: 34868286 PMCID: PMC8635908 DOI: 10.1155/2021/1825273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/15/2021] [Accepted: 10/21/2021] [Indexed: 11/17/2022]
Abstract
Traditional text annotation-based video retrieval is done by manually labeling videos with text, which is inefficient and highly subjective and generally cannot accurately describe the meaning of videos. Traditional content-based video retrieval uses convolutional neural networks to extract the underlying feature information of images to build indexes and achieves similarity retrieval of video feature vectors according to certain similarity measure algorithms. In this paper, by studying the characteristics of sports videos, we propose the histogram difference method based on using transfer learning and the four-step method based on block matching for mutation detection and fading detection of video shots, respectively. By adaptive thresholding, regions with large frame difference changes are marked as candidate regions for shots, and then the shot boundaries are determined by mutation detection algorithm. Combined with the characteristics of sports video, this paper proposes a key frame extraction method based on clustering and optical flow analysis, and experimental comparison with the traditional clustering method. In addition, this paper proposes a key frame extraction algorithm based on clustering and optical flow analysis for key frame extraction of sports video. The algorithm effectively removes the redundant frames, and the extracted key frames are more representative. Through extensive experiments, the keyword fuzzy finding algorithm based on improved deep neural network and ontology semantic expansion proposed in this paper shows a more desirable retrieval performance, and it is feasible to use this method for video underlying feature extraction, annotation, and keyword finding, and one of the outstanding features of the algorithm is that it can quickly and effectively retrieve the desired video in a large number of Internet video resources, reducing the false detection rate and leakage rate while improving the fidelity, which basically meets people's daily needs.
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Affiliation(s)
- Xiaoping Guo
- Shaanxi Normal University, Xi'an, Shaanxi 710000, China
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Dimitriadis I, Zaninovic N, Badiola AC, Bormann CL. Artificial intelligence in the embryology laboratory: a review. Reprod Biomed Online 2021; 44:435-448. [PMID: 35027326 DOI: 10.1016/j.rbmo.2021.11.003] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 10/07/2021] [Accepted: 11/04/2021] [Indexed: 02/03/2023]
Abstract
The goal of an IVF cycle is a healthy live-born baby. Despite the many advances in the field of assisted reproductive technologies, accurately predicting the outcome of an IVF cycle has yet to be achieved. One reason for this is the method of selecting an embryo for transfer. Morphological assessment of embryos is the traditional method of evaluating embryo quality and selecting which embryo to transfer. However, this subjective method of assessing embryos leads to inter- and intra-observer variability, resulting in less than optimal IVF success rates. To overcome this, it is common practice to transfer more than one embryo, potentially resulting in high-risk multiple pregnancies. Although time-lapse incubators and preimplantation genetic testing for aneuploidy have been introduced to help increase the chances of live birth, the outcomes remain less than ideal. Utilization of artificial intelligence (AI) has become increasingly popular in the medical field and is increasingly being leveraged in the embryology laboratory to help improve IVF outcomes. Many studies have been published investigating the use of AI as an unbiased, automated approach to embryo assessment. This review summarizes recent AI advancements in the embryology laboratory.
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Affiliation(s)
- Irene Dimitriadis
- Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston MA, USA
| | - Nikica Zaninovic
- The Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York NY, USA
| | - Alejandro Chavez Badiola
- New Hope Fertility Center, Av. Prado Norte 135, Lomas de Chapultepec, Mexico City, Mexico; IVF 2.0 LTD, 1 Liverpool Rd, Maghull, Merseyside, UK; School of Biosciences, University of Kent Kent, UK
| | - Charles L Bormann
- Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston MA, USA.
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Barrie A, Smith R, Campbell A, Fishel S. Optimisation of the timing of fertilisation assessment for oocytes cultured in standard incubation: lessons learnt from time-lapse imaging of 78 348 embryos. Hum Reprod 2021; 36:2840-2847. [PMID: 34536006 DOI: 10.1093/humrep/deab209] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/08/2021] [Indexed: 01/12/2023] Open
Abstract
STUDY QUESTION Using time-lapse data, can the current consensus for the timing of fertilisation assessment of oocytes, cultured in standard incubation, be optimised? SUMMARY ANSWER The optimum time to perform fertilisation assessment for oocytes cultured in standard incubation is 16.5 ± 0.5 h post-insemination (hpi), and the current consensus requires modification in order to minimise the chance of fertilisation being missed. WHAT IS KNOWN ALREADY Time-lapse incubation allows the embryologist to retrospectively review collated images of oocytes and embryos to capture important embryological observations that may otherwise be missed in standard incubation. According to expert consensus, the optimum time to perform the assessment of fertilisation is 17 ± 1 hpi. STUDY DESIGN, SIZE, DURATION A retrospective, multicentre analysis utilised data obtained from 54 746 ICSI-derived embryos and 23 602 IVF-derived embryos cultured in time-lapse incubation between January 2011 and November 2019. PARTICIPANTS/MATERIALS, SETTING, METHODS Using time-lapse imaging (TLI), the precise time of pronuclei appearance and disappearance was recorded, where applicable, and the number of oocytes with two pronuclei observable during 10 30-min intervals from 15 hpi to 20 hpi was determined. MAIN RESULTS AND THE ROLE OF CHANCE Between 15 and 17.5 hpi, the average number of oocytes exhibiting normal fertilisation, elicited as two pronuclei, was 98.19% with the highest proportion of oocytes having visible pronuclei at 16-16.5 hpi (98.32%). At 18-18.5 hpi, the number of visible pronuclei reduced to 95.53% and continued to fall to 87.02% at 19.5-20 hpi. LIMITATIONS, REASONS FOR CAUTION The authors' expectation is that these findings are transferable to other settings, however it is possible that, with alternative culture media and incubation environments, calibration of this timing may be required. As data cannot readily be recorded for pronuclear appearance for IVF-derived embryos, it is not possible to determine the optimum time to perform the fertilisation assessment for IVF-derived embryos. WIDER IMPLICATIONS OF THE FINDINGS By fine-tuning the time at which fertilisation assessment takes place the accuracy of the assessment can be increased to maximise the number of fertilised oocytes identified, thereby increasing the number of usable embryos for the patient. Without TLI and following current consensus guidelines, over 11% (n = 3000) of oocytes would have been marked as unfertilised within this cohort. Further to this, depending on the time of a standard fertilisation assessment, up to 300 embryos which resulted in live births could have been categorised as unfertilised, as they presented no visible pronuclei at the conventional assessment time-point. STUDY FUNDING/COMPETING INTEREST(S) A.C. is a minor shareholder in CARE Fertility Limited. Validated algorithmic time-lapse embryo selection is offered to patients at CARE Fertility at an additional charge as an adjuvant treatment option. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- A Barrie
- CARE Chester, CARE Fertility UK, Chester, UK
| | - R Smith
- CARE Fertility UK, Sheffield, UK
| | | | - S Fishel
- CARE Fertility UK, Nottingham, UK.,School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
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Sciorio R, Meseguer M. Focus on time-lapse analysis: blastocyst collapse and morphometric assessment as new features of embryo viability. Reprod Biomed Online 2021; 43:821-832. [PMID: 34593324 DOI: 10.1016/j.rbmo.2021.08.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 06/29/2021] [Accepted: 08/05/2021] [Indexed: 11/28/2022]
Abstract
The main goal of assisted reproductive technology (ART) is to achieve a healthy singleton live birth after the transfer of one embryo. A major objective of IVF scientists has always been to use adequate criteria for selecting the embryo for transfer according to its implantation potential. Indeed, embryo quality is usually assessed by evaluating visual morphology, which relies on the removal of the embryo from the incubator and might include inter- and intra-evaluator variation among embryologists. Recently, an advancement in embryo culture has taken place with the introduction of a new type of incubator with an integrated time-lapse monitoring system, which enables embryologists to analyse the dynamic events of embryo development from fertilization to blastocyst formation. This novel practice is rapidly growing and has been used in many IVF centres worldwide. Therefore, the main aim of this review is to present the benefits of time-lapse monitoring in a modern embryology laboratory; in particular, we discuss blastocyst collapse and morphometric blastocyst assessment, and analyse their association with embryo viability and implantation potential. In addition, we highlight preliminary studies involving artificial intelligence and machine learning models as non-invasive markers of clinical pregnancy.
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Affiliation(s)
- Romualdo Sciorio
- Edinburgh Assisted Conception Programme, EFREC, Royal Infirmary of Edinburgh, UK.
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Should there be an "AI" in TEAM? Embryologists selection of high implantation potential embryos improves with the aid of an artificial intelligence algorithm. J Assist Reprod Genet 2021; 38:2663-2670. [PMID: 34535847 DOI: 10.1007/s10815-021-02318-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 09/09/2021] [Indexed: 10/20/2022] Open
Abstract
PURPOSE A deep learning artificial intelligence (AI) algorithm has been demonstrated to outperform embryologists in identifying euploid embryos destined to implant with an accuracy of 75.3% (1). Our aim was to evaluate the performance of highly trained embryologists in selecting top quality day 5 euploid blastocysts with and without the aid of a deep learning algorithm. MATERIALS AND METHODS A non-overlapping series of 200 sets of day 5 euploid embryo images with known implantation outcomes was distributed to 17 highly trained embryologists. One embryo in each set was known to have implanted and one failed implantation. They were asked to select which embryo to transfer from each set. The same 200 sets of embryos, with indication of which embryo in each set had been identified by the algorithm as more likely to implant was then distributed. Chi-squared, t-test, and receiver operating curves were performed to compare the embryologist performeance with and without AI. RESULTS Fourteen embryologists completed both assessments. Embryologists provided with AI results selected successfully implanted embryos in 73.6% of cases compared to 65.5% for those selected using visual assessments alone (p < 0.001). All embryologists improved in their ability to select embryos with the aid of the AI algorithm with a mean percent improvement of 11.1% (range 1.4% to 15.5%). There were no differences in degree of improvement by embryologist level of experience (junior, intermediate, senior). CONCLUSIONS The incorporation of an AI framework for blastocyst selection enhanced the performance of trained embryologists in identifying PGT-A euploid embryos destined to implant.
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