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Liu L, Liu B, Liao M, Gan Q, Huang Q, Yang Y. Identifying key predictive features for live birth rate in advanced maternal age patients undergoing single vitrified-warmed blastocyst transfer. Reprod Biol Endocrinol 2024; 22:120. [PMID: 39375693 PMCID: PMC11457422 DOI: 10.1186/s12958-024-01295-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2024] [Accepted: 10/02/2024] [Indexed: 10/09/2024] Open
Abstract
BACKGROUND Infertility affects one in six couples worldwide, with advanced maternal age (AMA) posing unique challenges due to diminished ovarian reserve and reduced oocyte quality. Single vitrified-warmed blastocyst transfer (SVBT) has shown promise in assisted reproductive technology (ART), but success rates in AMA patients remain suboptimal. This study aimed to identify and refine predictive factors for live birth following SVBT in AMA patients, with the goal of enhancing clinical decision-making and enabling personalized treatment strategies. METHODS This retrospective cohort study analyzed 1,168 SVBT cycles conducted between June 2016 and December 2022 at the First Affiliated Hospital of Guangxi Medical University and Nanning Maternity and Child Health Hospital. Nineteen machine-learning models were applied to identify key predictive factors for live birth. Feature selection and 10-fold cross-validation were employed to validate the models. RESULTS The most significant predictors of live birth included inner cell mass quality, trophectoderm quality, number of oocytes retrieved, endometrial thickness, and the presence of 8-cell blastomeres on day 3. The stacking model demonstrated the best predictive performance (AUC: 0.791), followed by Extra Trees (AUC: 0.784) and Random Forest (AUC: 0.768). These models outperformed traditional methods, achieving superior accuracy, sensitivity, and specificity. CONCLUSION Leveraging advanced machine-learning models and identifying critical predictive factors can improve the accuracy of live birth outcome predictions for AMA patients undergoing SVBT. These findings offer valuable insights for enhancing clinical decision-making and managing patient expectations. Further research is needed to validate these results in larger, multi-center cohorts and to explore additional factors, including fresh embryo transfers, to broaden the applicability of these models in clinical practice.
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Affiliation(s)
- Lidan Liu
- Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
| | - Bo Liu
- Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Ming Liao
- Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Qiuying Gan
- Reproductive Center, Nanning Maternity and Child Health Hospital, Nanning, Guangxi, China
| | - Qianyi Huang
- Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yihua Yang
- Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
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Ten J, Herrero L, Linares Á, Álvarez E, Ortiz JA, Bernabeu A, Bernabéu R. Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights. Reprod Biol Endocrinol 2024; 22:116. [PMID: 39261843 PMCID: PMC11389240 DOI: 10.1186/s12958-024-01285-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 08/23/2024] [Indexed: 09/13/2024] Open
Abstract
BACKGROUND Data sciences and artificial intelligence are becoming encouraging tools in assisted reproduction, favored by time-lapse technology incubators. Our objective is to analyze, compare and identify the most predictive machine learning algorithm developed using a known implantation database of embryos transferred in our egg donation program, including morphokinetic and morphological variables, and recognize the most predictive embryo parameters in order to enhance IVF treatments clinical outcomes. METHODS Multicenter retrospective cohort study carried out in 378 egg donor recipients who performed a fresh single embryo transfer during 2021. All treatments were performed by Intracytoplasmic Sperm Injection, using fresh or frozen oocytes. The embryos were cultured in Geri® time-lapse incubators until transfer on day 5. The embryonic morphokinetic events of 378 blastocysts with known implantation and live birth were analyzed. Classical statistical analysis (binary logistic regression) and 10 machine learning algorithms were applied including Multi-Layer Perceptron, Support Vector Machines, k-Nearest Neighbor, Cart and C0.5 Classification Trees, Random Forest (RF), AdaBoost Classification Trees, Stochastic Gradient boost, Bagged CART and eXtrem Gradient Boosting. These algorithms were developed and optimized by maximizing the area under the curve. RESULTS The Random Forest emerged as the most predictive algorithm for implantation (area under the curve, AUC = 0.725, IC 95% [0.6232-0826]). Overall, implantation and miscarriage rates stood at 56.08% and 18.39%, respectively. Overall live birth rate was 41.26%. Significant disparities were observed regarding time to hatching out of the zona pellucida (p = 0.039). The Random Forest algorithm demonstrated good predictive capabilities for live birth (AUC = 0.689, IC 95% [0.5821-0.7921]), but the AdaBoost classification trees proved to be the most predictive model for live birth (AUC = 0.749, IC 95% [0.6522-0.8452]). Other important variables with substantial predictive weight for implantation and live birth were duration of visible pronuclei (DESAPPN-APPN), synchronization of cleavage patterns (T8-T5), duration of compaction (TM-TiCOM), duration of compaction until first sign of cavitation (TiCAV-TM) and time to early compaction (TiCOM). CONCLUSIONS This study highlights Random Forest and AdaBoost as the most effective machine learning models in our Known Implantation and Live Birth Database from our egg donation program. Notably, time to blastocyst hatching out of the zona pellucida emerged as a highly reliable parameter significantly influencing our implantation machine learning predictive models. Processes involving syngamy, genomic imprinting during embryo cleavage, and embryo compaction are also influential and could be crucial for implantation and live birth outcomes.
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Affiliation(s)
- Jorge Ten
- Instituto Bernabéu Alicante, Avda. Albufereta, 31, 03016, Alicante, Spain.
| | | | - Ángel Linares
- Instituto Bernabéu Alicante, Avda. Albufereta, 31, 03016, Alicante, Spain
| | | | - José Antonio Ortiz
- Molecular Biology and Genetics, Instituto Bernabéu Biotech, Alicante, Spain
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Sun L, Li J, Zeng S, Luo Q, Miao H, Liang Y, Cheng L, Sun Z, Tai WH, Han Y, Yin Y, Wu K, Zhang K. Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos. Chin Med J (Engl) 2024; 137:1939-1949. [PMID: 38997251 PMCID: PMC11332789 DOI: 10.1097/cm9.0000000000003162] [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: 01/21/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable. METHODS We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on day 3 or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for the prediction of aneuploidy and consequent live-birth outcomes. RESULTS These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709-0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes. CONCLUSIONS Our study underscores the AI model's ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.
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Affiliation(s)
- Ling Sun
- Department of Reproductive Medicine, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, Guangdong 510623, China
| | - Jiahui Li
- Department of Reproductive Medicine, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, Guangdong 510623, China
- Guangzhou National Laboratory, Guangzhou, Guangdong 510000, China
| | - Simiao Zeng
- Department of Reproductive Medicine, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, Guangdong 510623, China
- Guangzhou National Laboratory, Guangzhou, Guangdong 510000, China
| | - Qiangxiang Luo
- Department of Reproductive Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong 529000, China
| | - Hanpei Miao
- Guangzhou National Laboratory, Guangzhou, Guangdong 510000, China
- Department of Ophthalmology, Dongguan People’s Hospital, The First School of Clinical Medicine, Southern Medical University, Dongguan, Guangdong 523000, China
| | - Yunhao Liang
- Department of Reproductive Medicine, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, Guangdong 510623, China
| | - Linling Cheng
- Institute for Artificial Intelligence in Medicine, Faculty of Medicine, Macau University of Science and Technology, Macau Special Administrative Region 999078, China
| | - Zhuo Sun
- Wenzhou Eye Hospital, Institute for Advanced Studies on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Wa Hou Tai
- Institute for Artificial Intelligence in Medicine, Faculty of Medicine, Macau University of Science and Technology, Macau Special Administrative Region 999078, China
| | - Yibing Han
- Kiang Wu Hospital, Macau Special Administrative Region 999078, China
| | - Yun Yin
- Faculty of Business, City University of Macau, Macau Special Administrative Region 999078, China
| | - Keliang Wu
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Reproductive Medicine, Institute of Women, Children and Reproductive Health and Key laboratory of Reproductive Endocrinology of Ministry of Education, Shandong University, Jinan, Shandong 250000,China
| | - Kang Zhang
- Department of Reproductive Medicine, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, Guangdong 510623, China
- Guangzhou National Laboratory, Guangzhou, Guangdong 510000, China
- Institute for Artificial Intelligence in Medicine, Faculty of Medicine, Macau University of Science and Technology, Macau Special Administrative Region 999078, China
- Wenzhou Eye Hospital, Institute for Advanced Studies on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
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Mapstone C, Hunter H, Brison D, Handl J, Plusa B. Deep learning pipeline reveals key moments in human embryonic development predictive of live birth after in vitro fertilization. Biol Methods Protoc 2024; 9:bpae052. [PMID: 39114746 PMCID: PMC11305813 DOI: 10.1093/biomethods/bpae052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 07/12/2024] [Accepted: 07/18/2024] [Indexed: 08/10/2024] Open
Abstract
Demand for in vitro fertilization (IVF) treatment is growing; however, success rates remain low partly due to difficulty in selecting the best embryo to be transferred. Current manual assessments are subjective and may not take advantage of the most informative moments in embryo development. Here, we apply convolutional neural networks (CNNs) to identify key windows in pre-implantation human development that can be linked to embryo viability and are therefore suitable for the early grading of IVF embryos. We show how machine learning models trained at these developmental time points can be used to refine overall embryo viability assessment. Exploiting the well-known capabilities of transfer learning, we illustrate the performance of CNN models for very limited datasets, paving the way for the use on a clinic-by-clinic basis, catering for local data heterogeneity.
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Affiliation(s)
- Camilla Mapstone
- Faculty of Biology, Medicine and Health (FBMH), Division of Developmental Biology & Medicine, University of Manchester, Manchester, M13 9PT, United Kingdom
- Alliance Manchester Business School, University of Manchester, Manchester, M15 6PB, United Kingdom
| | - Helen Hunter
- Department of Reproductive Medicine, Old Saint Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, M13 0JH, United Kingdom
| | - Daniel Brison
- Department of Reproductive Medicine, Old Saint Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, M13 0JH, United Kingdom
- Division of Developmental Biology and Medicine, Maternal and Fetal Health Research Centre, School of Medical Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9WL, United Kingdom
| | - Julia Handl
- Alliance Manchester Business School, University of Manchester, Manchester, M15 6PB, United Kingdom
| | - Berenika Plusa
- Faculty of Biology, Medicine and Health (FBMH), Division of Developmental Biology & Medicine, University of Manchester, Manchester, M13 9PT, United Kingdom
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Yang L, Leynes C, Pawelka A, Lorenzo I, Chou A, Lee B, Heaney JD. Machine learning in time-lapse imaging to differentiate embryos from young vs old mice†. Biol Reprod 2024; 110:1115-1124. [PMID: 38685607 PMCID: PMC11180621 DOI: 10.1093/biolre/ioae056] [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: 11/24/2023] [Revised: 02/15/2024] [Accepted: 04/05/2024] [Indexed: 05/02/2024] Open
Abstract
Time-lapse microscopy for embryos is a non-invasive technology used to characterize early embryo development. This study employs time-lapse microscopy and machine learning to elucidate changes in embryonic growth kinetics with maternal aging. We analyzed morphokinetic parameters of embryos from young and aged C57BL6/NJ mice via continuous imaging. Our findings show that aged embryos accelerated through cleavage stages (from 5-cells) to morula compared to younger counterparts, with no significant differences observed in later stages of blastulation. Unsupervised machine learning identified two distinct clusters comprising of embryos from aged or young donors. Moreover, in supervised learning, the extreme gradient boosting algorithm successfully predicted the age-related phenotype with 0.78 accuracy, 0.81 precision, and 0.83 recall following hyperparameter tuning. These results highlight two main scientific insights: maternal aging affects embryonic development pace, and artificial intelligence can differentiate between embryos from aged and young maternal mice by a non-invasive approach. Thus, machine learning can be used to identify morphokinetics phenotypes for further studies. This study has potential for future applications in selecting human embryos for embryo transfer, without or in complement with preimplantation genetic testing.
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Affiliation(s)
- Liubin Yang
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, Texas, USA
- Division of Reproductive Endocrinology and Infertility, Division of Reproductive Sciences, Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Carolina Leynes
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Ashley Pawelka
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Isabel Lorenzo
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Andrew Chou
- Pain Research, Informatics, Multi-morbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, USA
- Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Brendan Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Jason D Heaney
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
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Borna MR, Sepehri MM, Maleki B. An artificial intelligence algorithm to select most viable embryos considering current process in IVF labs. Front Artif Intell 2024; 7:1375474. [PMID: 38881952 PMCID: PMC11177761 DOI: 10.3389/frai.2024.1375474] [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: 01/23/2024] [Accepted: 05/16/2024] [Indexed: 06/18/2024] Open
Abstract
Background The most common Assisted Reproductive Technology is In-Vitro Fertilization (IVF). During IVF, embryologists commonly perform a morphological assessment to evaluate embryo quality and choose the best embryo for transferring to the uterus. However, embryo selection through morphological assessment is subjective, so various embryologists obtain different conclusions. Furthermore, humans can consider only a limited number of visual parameters resulting in a poor IVF success rate. Artificial intelligence (AI) for embryo selection is objective and can include many parameters, leading to better IVF outcomes. Objectives This study sought to use AI to (1) predict pregnancy results based on embryo images, (2) assess using more than one image of the embryo in the prediction of pregnancy but based on the current process in IVF labs, and (3) compare results of AI-Based methods and embryologist experts in predicting pregnancy. Methods A data set including 252 Time-lapse Videos of embryos related to IVF performed between 2017 and 2020 was collected. Frames related to 19 ± 1, 43 ± 1, and 67 ± 1 h post-insemination were extracted. Well-Known CNN architectures with transfer learning have been applied to these images. The results have been compared with an algorithm that only uses the final image of embryos. Furthermore, the results have been compared with five experienced embryologists. Results To predict the pregnancy outcome, we applied five well-known CNN architectures (AlexNet, ResNet18, ResNet34, Inception V3, and DenseNet121). DeepEmbryo, using three images, predicts pregnancy better than the algorithm that only uses one final image. It also can predict pregnancy better than all embryologists. Different well-known architectures can successfully predict pregnancy chances with up to 75.0% accuracy using Transfer Learning. Conclusion We have developed DeepEmbryo, an AI-based tool that uses three static images to predict pregnancy. Additionally, DeepEmbryo uses images that can be obtained in the current IVF process in almost all IVF labs. AI-based tools have great potential for predicting pregnancy and can be used as a proper tool in the future.
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Affiliation(s)
- Mahdi-Reza Borna
- Department of IT Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Mehdi Sepehri
- Department of IT Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Behnam Maleki
- Infertility Center, Department of Obstetrics and Gynecology, Mazandaran University of Medical Sciences, Sari, Iran
- Research and Clinical Center for Infertility, Yazd Reproductive Sciences Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
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Braude I, Haikin Herzberger E, Semo M, Soifer K, Goren Gepstein N, Wiser A, Miller N. Machine learning for predicting elective fertility preservation outcomes. Sci Rep 2024; 14:10158. [PMID: 38698132 PMCID: PMC11065881 DOI: 10.1038/s41598-024-60671-w] [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: 10/10/2023] [Accepted: 04/25/2024] [Indexed: 05/05/2024] Open
Abstract
This retrospective study applied machine-learning models to predict treatment outcomes of women undergoing elective fertility preservation. Two-hundred-fifty women who underwent elective fertility preservation at a tertiary center, 2019-2022 were included. Primary outcome was the number of metaphase II oocytes retrieved. Outcome class was based on oocyte count (OC): Low (≤ 8), Medium (9-15) or High (≥ 16). Machine-learning models and statistical regression were used to predict outcome class, first based on pre-treatment parameters, and then using post-treatment data from ovulation-triggering day. OC was 136 Low, 80 Medium, and 34 High. Random Forest Classifier (RFC) was the most accurate model (pre-treatment receiver operating characteristic (ROC) area under the curve (AUC) was 77%, and post-treatment ROC AUC was 87%), followed by XGBoost Classifier (pre-treatment ROC AUC 74%, post-treatment ROC AUC 86%). The most important pre-treatment parameters for RFC were basal FSH (22.6%), basal LH (19.1%), AFC (18.2%), and basal estradiol (15.6%). Post-treatment parameters were estradiol levels on trigger-day (17.7%), basal FSH (11%), basal LH (9%), and AFC (8%). Machine-learning models trained with clinical data appear to predict fertility preservation treatment outcomes with relatively high accuracy.
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Affiliation(s)
- Itai Braude
- Department of Obstetrics and Gynecology, Meir Medical Center, Kfar Saba, Israel
| | - Einat Haikin Herzberger
- Department of Obstetrics and Gynecology, Meir Medical Center, Kfar Saba, Israel
- School of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Mor Semo
- Department of Obstetrics and Gynecology, Meir Medical Center, Kfar Saba, Israel
| | - Kim Soifer
- Department of Obstetrics and Gynecology, Meir Medical Center, Kfar Saba, Israel
| | - Nitzan Goren Gepstein
- Department of Obstetrics and Gynecology, Meir Medical Center, Kfar Saba, Israel
- School of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Amir Wiser
- Department of Obstetrics and Gynecology, Meir Medical Center, Kfar Saba, Israel
- School of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Netanella Miller
- Department of Obstetrics and Gynecology, Meir Medical Center, Kfar Saba, Israel.
- School of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel.
- OB/GYN Department, Mayanei Hayeshua Medical Center, Bnei Brak, Israel.
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Pugeda TGS. Embryo Selection in the Context of In Vitro Fertilization. LINACRE QUARTERLY 2024; 91:21-28. [PMID: 38304880 PMCID: PMC10829575 DOI: 10.1177/00243639231169828] [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: 02/03/2024]
Abstract
From the Catholic perspective, in vitro fertilization (IVF) is morally problematic because it artificially separates the procreative and unitive aspects of the conjugal act. Embryo selection (ES) in the context of IVF is an injustice against the resulting embryos because it treats them as commodities and works against their right to life by determining their implantation potential in light of their features. The Church opposes the eugenics mentality underlying ES. Meanwhile, the IVF industry increasingly uses artificial intelligence (AI) for ES. However, doing so could worsen the injustice by deepening the disrespect of human lives under the technocratic paradigm. As such, Catholic bioethicists are encouraged to advocate for the Church's teachings with renewed vigor. In this commentary, we will examine (1) ES in the context of IVF, (2) using AI for ES, (3) the moral implications of using AI for ES, and (4) points for further consideration. Summary: Using AI for Embryo selection in the context of IVF deepens the disrespect of human lives under the technocratic paradigm.
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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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Raes A, Athanasiou G, Azari-Dolatabad N, Sadeghi H, Gonzalez Andueza S, Arcos JL, Cerquides J, Chaitanya Pavani K, Opsomer G, Bogado Pascottini O, Smits K, Angel-Velez D, Van Soom A. Manual versus deep learning measurements to evaluate cumulus expansion of bovine oocytes and its relationship with embryo development in vitro. Comput Biol Med 2024; 168:107785. [PMID: 38056209 DOI: 10.1016/j.compbiomed.2023.107785] [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: 07/20/2023] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023]
Abstract
Cumulus expansion is an important indicator of oocyte maturation and has been suggested to be indicative of greater oocyte developmental capacity. Although multiple methods have been described to assess cumulus expansion, none of them is considered a gold standard. Additionally, these methods are subjective and time-consuming. In this manuscript, the reliability of three cumulus expansion measurement methods was assessed, and a deep learning model was created to automatically perform the measurement. Cumulus expansion of 232 cumulus-oocyte complexes was evaluated by three independent observers using three methods: (1) measurement of the cumulus area, (2) measurement of three distances between the zona pellucida and outer cumulus, and (3) scoring cumulus expansion on a 5-point Likert scale. The reliability of the methods was calculated in terms of intraclass-correlation coefficients (ICC) for both inter- and intra-observer agreements. The area method resulted in the best overall inter-observer agreement with an ICC of 0.89 versus 0.54 and 0.30 for the 3-distance and scoring methods, respectively. Therefore, the area method served as the base to create a deep learning model, AI-xpansion, which reaches a human-level performance in terms of average rank, bias and variance. To evaluate the accuracy of the methods, the results of cumulus expansion calculations were linked to embryonic development. Cumulus expansion had increased significantly in oocytes that achieved successful embryo development when measured by AI-xpansion, the area- or 3-distance method, while this was not the case for the scoring method. Measuring the area is the most reliable method to manually evaluate cumulus expansion, whilst deep learning automatically performs the calculation with human-level precision and high accuracy and could therefore be a valuable prospective tool for embryologists.
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Affiliation(s)
- Annelies Raes
- Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium.
| | - Georgios Athanasiou
- Artificial Intelligence Research Institute (IIIA-CSIC), 08193, Bellaterra, Spain; Department of Computer Science, Universitat Autonoma de Barcelona, Spain.
| | - Nima Azari-Dolatabad
- Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Hafez Sadeghi
- Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Sebastian Gonzalez Andueza
- Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Josep Lluis Arcos
- Artificial Intelligence Research Institute (IIIA-CSIC), 08193, Bellaterra, Spain
| | - Jesus Cerquides
- Artificial Intelligence Research Institute (IIIA-CSIC), 08193, Bellaterra, Spain.
| | - Krishna Chaitanya Pavani
- Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Geert Opsomer
- Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Osvaldo Bogado Pascottini
- Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Katrien Smits
- Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Daniel Angel-Velez
- Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium; Research Group in Animal Sciences-INCA-CES, Universidad CES, Medellin, 050021, Colombia
| | - Ann Van Soom
- Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
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Serdarogullari M, Raad G, Yarkiner Z, Bazzi M, Mourad Y, Alpturk S, Fakih F, Fakih C, Liperis G. Identifying predictors of Day 5 blastocyst utilization rate using an artificial neural network. Reprod Biomed Online 2023; 47:103399. [PMID: 37862857 DOI: 10.1016/j.rbmo.2023.103399] [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: 05/10/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 10/22/2023]
Abstract
RESEARCH QUESTION Can artificial intelligence identify predictors of an increased Day 5 blastocyst utilization rate (D5BUR), which is one of the most informative key performance indicators in an IVF laboratory? DESIGN This retrospective, multicentre study evaluated six variables for predicting D5BUR using an artificial neural network (ANN): number of metaphase II (MII) oocytes injected (intracytoplasmic sperm injection); use of autologous/donated gametes; maternal age at oocyte retrieval; sperm concentration; progressive sperm motility rate; and fertilization rate. Cycles were divided into training and testing sets through stratified random sampling. D5BUR on Day 5 was grouped into <60% and ≥60% as per the Vienna consensus benchmark values. RESULTS The area under the receiver operating characteristic curve (AUC) to predict the D5BUR groups was 80.2%. From the ANN model, all six independent variables were found to be of significant value for the prediction of D5BUR (P<0.0001), with the most important variable being the number of MII oocytes injected. Investigation of the effect of MII oocytes injected on D5BUR indicated an inverse correlation, with injection of an increasing number of MII oocytes resulting in a decreasing D5BUR (r=-0.344, P<0.001) and injection of up to six oocytes resulting in D5BUR ≥60%. CONCLUSION The number of MII oocytes injected is the most important predictor of D5BUR. Exploration of additional variables and further validation of models that can predict D5BUR can guide the way towards personalized treatment and increased safety.
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Affiliation(s)
| | - Georges Raad
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon; Faculty of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon
| | - Zalihe Yarkiner
- Cyprus International University, Faculty of Arts and Sciences, Department of Basic Sciences and Humanities, Northern Cyprus via Mersin 10, Turkey
| | - Marwa Bazzi
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | - Youmna Mourad
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | | | - Fadi Fakih
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | - Chadi Fakih
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | - George Liperis
- Westmead Fertility Centre, Institute of Reproductive Medicine, University of Sydney, Westmead, NSW, Australia.
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Berman A, Anteby R, Efros O, Klang E, Soffer S. Deep learning for embryo evaluation using time-lapse: a systematic review of diagnostic test accuracy. Am J Obstet Gynecol 2023; 229:490-501. [PMID: 37116822 DOI: 10.1016/j.ajog.2023.04.027] [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: 09/06/2022] [Revised: 03/28/2023] [Accepted: 04/19/2023] [Indexed: 04/30/2023]
Abstract
OBJECTIVE This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring. DATA SOURCES A systematic search was conducted in PubMed and Web of Science databases from January 2016 to December 2022. The search strategy was carried out by using key words and MeSH (Medical Subject Headings) terms. STUDY ELIGIBILITY CRITERIA Studies were included if they reported the accuracy of convolutional neural network models for embryo evaluation using time-lapse monitoring. The review was registered with PROSPERO (International Prospective Register of Systematic Reviews; identification number CRD42021275916). METHODS Two reviewer authors independently screened results using the Covidence systematic review software. The full-text articles were reviewed when studies met the inclusion criteria or in any uncertainty. Nonconsensus was resolved by a third reviewer. Risk of bias and applicability were evaluated using the QUADAS-2 tool and the modified Joanna Briggs Institute or JBI checklist. RESULTS Following a systematic search of the literature, 22 studies were identified as eligible for inclusion. All studies were retrospective. A total of 522,516 images of 222,998 embryos were analyzed. Three main outcomes were evaluated: successful in vitro fertilization, blastocyst stage classification, and blastocyst quality. Most studies reported >80% accuracy, and embryologists were outperformed in some. Ten studies had a high risk of bias, mostly because of patient bias. CONCLUSION The application of artificial intelligence in time-lapse monitoring has the potential to provide more efficient, accurate, and objective embryo evaluation. Models that examined blastocyst stage classification showed the best predictions. Models that predicted live birth had a low risk of bias, used the largest databases, and had external validation, which heightens their relevance to clinical application. Our systematic review is limited by the high heterogeneity among the included studies. Researchers should share databases and standardize reporting.
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Affiliation(s)
- Aya Berman
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
| | - Roi Anteby
- Department of Surgery and Transplantation B, Chaim Sheba Medical Center, Tel Hashomer, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Orly Efros
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; National Hemophilia Center and Institute of Thrombosis & Hemostasis, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Eyal Klang
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Institute for Health Care Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY; Deep Vision Lab, Chaim Sheba Medical Center, Ramat Gan, Israel; Division of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Shelly Soffer
- Deep Vision Lab, Chaim Sheba Medical Center, Ramat Gan, Israel; Internal Medicine B, Assuta Medical Center, Ashdod, Israel; Ben-Gurion University of the Negev, Be'er Sheva, Israel
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13
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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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14
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Si K, Huang B, Jin L. Application of artificial intelligence in gametes and embryos selection. HUM FERTIL 2023; 26:757-777. [PMID: 37705466 DOI: 10.1080/14647273.2023.2256980] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/22/2023] [Indexed: 09/15/2023]
Abstract
Gamete and embryo quality are critical to the success rate of Assisted Reproductive Technology (ART) cycles, but there remains a lack of methods to accurately measure the quality of sperm, oocytes and embryos. The ability of Artificial Intelligence (AI) technology to analyze large amounts of data, especially video and images, is particularly useful in gamete and embryo assessment and selection. The well-trained model has fast calculation speed and high accuracy, which can help embryologists to perform more objective gamete and embryo selection. Various artificial intelligence models have been developed for gamete and embryo assessment, some of which exhibit good performance. In this review, we summarize the latest applications of AI technology in semen analysis, as well as selection for sperm, oocyte and embryo, and discuss the existing problems and development directions of artificial intelligence in this field.
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Affiliation(s)
- Keyi Si
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Bo Huang
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Lei Jin
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
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15
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Zabari N, Kan-Tor Y, Or Y, Shoham Z, Shufaro Y, Richter D, Har-Vardi I, Ben-Meir A, Srebnik N, Buxboim A. Delineating the heterogeneity of embryo preimplantation development using automated and accurate morphokinetic annotation. J Assist Reprod Genet 2023; 40:1391-1406. [PMID: 37300648 PMCID: PMC10310622 DOI: 10.1007/s10815-023-02806-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 04/03/2023] [Indexed: 06/12/2023] Open
Abstract
PURPOSE Our objective was to design an automated deep learning model that extracts the morphokinetic events of embryos that were recorded by time-lapse incubators. Using automated annotation, we set out to characterize the temporal heterogeneity of preimplantation development across a large number of embryos. METHODS To perform a retrospective study, we used a dataset of video files of 67,707 embryos from four IVF clinics. A convolutional neural network (CNN) model was trained to assess the developmental states that appear in single frames from 20,253 manually-annotated embryos. Probability-weighted superposition of multiple predicted states was permitted, thus accounting for visual uncertainties. Superimposed embryo states were collapsed onto discrete series of morphokinetic events via monotonic regression of whole-embryo profiles. Unsupervised K-means clustering was applied to define subpopulations of embryos of distinctive morphokinetic profiles. RESULTS We perform automated assessment of single-frame embryo states with 97% accuracy and demonstrate whole-embryo morphokinetic annotation with R-square 0.994. High quality embryos that had been valid candidates for transfer were clustered into nine subpopulations, as characterized by distinctive developmental dynamics. Retrospective comparative analysis of transfer versus implantation rates reveals differences between embryo clusters as marked by poor synchronization of the third mitotic cell-cleavage cycle. CONCLUSIONS By demonstrating fully automated, accurate, and standardized morphokinetic annotation of time-lapse embryo recordings from IVF clinics, we provide practical means to overcome current limitations that hinder the implementation of morphokinetic decision-support tools within clinical IVF settings due to inter-observer and intra-observer manual annotation variations and workload constrains. Furthermore, our work provides a platform to address embryo heterogeneity using dimensionality-reduced morphokinetic descriptions of preimplantation development.
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Affiliation(s)
- Nir Zabari
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, The Edmond J. Safra Campus, 9190416, Jerusalem, Israel
| | - Yoav Kan-Tor
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, The Edmond J. Safra Campus, 9190416, Jerusalem, Israel
- The Center for Interdisciplinary Data Science Research, The Hebrew University of Jerusalem, The Edmond J. Safra Campus, Givat Ram, 9190401, Jerusalem, Israel
| | - Yuval Or
- Department of Obstetrics and Gynecology, Division of Reproductive Endocrinology and Infertility, Kaplan Hospital, Rehovot, Israel
| | - Zeev Shoham
- Department of Obstetrics and Gynecology, Division of Reproductive Endocrinology and Infertility, Kaplan Hospital, Rehovot, Israel
| | - Yoel Shufaro
- Infertility and IVF Unit, Helen Schneider Hospital for Women, Rabin Medical Center, Beilinson Hospital, Petach Tikva, Israel
| | - Dganit Richter
- The IVF Unit Gyn/Obs, Soroka University Medical Center, Beer-Sheva, Israel
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Iris Har-Vardi
- The IVF Unit Gyn/Obs, Soroka University Medical Center, Beer-Sheva, Israel
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Assaf Ben-Meir
- Department of Obstetrics and Gynecology, Hadassah Medical Center - Hebrew University of Jerusalem, Jerusalem, Israel
- Infertility and IVF Unit, Hadassah Hebrew University Hospital, Jerusalem, Israel
| | - Naama Srebnik
- The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, The Edmond J. Safra Campus - Givat Ram, 9190401, Jerusalem, Israel
- In Vitro Fertilization Unit, Department of Obstetrics and Gynecology, Shaare Zedek Medical Center, 9103102, Jerusalem, Israel
| | - Amnon Buxboim
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, The Edmond J. Safra Campus, 9190416, Jerusalem, Israel.
- The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, The Edmond J. Safra Campus - Givat Ram, 9190401, Jerusalem, Israel.
- The Alexender Grass Center for Bioengineering, The Hebrew University of Jerusalem, The Edmond J. Safra Campus, Givat Ram, 9190401, Jerusalem, Israel.
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16
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Tokuoka Y, Yamada TG, Mashiko D, Ikeda Z, Kobayashi TJ, Yamagata K, Funahashi A. An explainable deep learning-based algorithm with an attention mechanism for predicting the live birth potential of mouse embryos. Artif Intell Med 2022; 134:102432. [PMID: 36462898 DOI: 10.1016/j.artmed.2022.102432] [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: 10/04/2021] [Revised: 08/13/2022] [Accepted: 10/19/2022] [Indexed: 11/09/2022]
Abstract
In assisted reproductive technology (ART), embryos produced by in vitro fertilization (IVF) are graded according to their live birth potential, and high-grade embryos are preferentially transplanted. However, rates of live birth following clinical ART remain low worldwide. Grading is based on the embryo shape at a limited number of stages and does not consider the shape of embryos and intracellular structures, e.g., nuclei, at various stages important for normal embryogenesis. Here, we developed a Normalized Multi-View Attention Network (NVAN) that directly predicts live birth potential from the nuclear structure in live-cell fluorescence images of mouse embryos from zygote to across a wide range of stages. The input is morphological features of cell nuclei, which were extracted as multivariate time-series data by using the segmentation algorithm for mouse embryos. The classification accuracy of our method (83.87%) greatly exceeded that of existing machine-learning methods and that of visual inspection by embryo culture specialists. Our method also has a new attention mechanism that allows us to determine which values of multivariate time-series data, used to describe nuclear morphology, were the basis for the prediction. By visualizing the features that contributed most to the prediction of live birth potential, we found that the size and shape of the nucleus at the morula stage and at the time of cell division were important for live birth prediction. We anticipate that our method will help ART and developmental engineering as a new basic technology for IVF embryo selection.
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Affiliation(s)
- Yuta Tokuoka
- Center for Biosciences and Informatics, Graduate School of Fundamental Science and Technology, Keio University, 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama, 223-8522, Japan
| | - Takahiro G Yamada
- Center for Biosciences and Informatics, Graduate School of Fundamental Science and Technology, Keio University, 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama, 223-8522, Japan; Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama, 223-8522, Japan
| | - Daisuke Mashiko
- Faculty of Biology-Oriented Science and Technology, Kindai University, 930 Nishimitani, Kinokawa, Wakayama, 649-6493, Japan
| | - Zenki Ikeda
- Faculty of Biology-Oriented Science and Technology, Kindai University, 930 Nishimitani, Kinokawa, Wakayama, 649-6493, Japan
| | - Tetsuya J Kobayashi
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
| | - Kazuo Yamagata
- Faculty of Biology-Oriented Science and Technology, Kindai University, 930 Nishimitani, Kinokawa, Wakayama, 649-6493, Japan
| | - Akira Funahashi
- Center for Biosciences and Informatics, Graduate School of Fundamental Science and Technology, Keio University, 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama, 223-8522, Japan; Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama, 223-8522, Japan.
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17
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Zhu X, Zhu Z, Gu L, Chen L, Zhan Y, Li X, Huang C, Xu J, Li J. Prediction models and associated factors on the fertility behaviors of the floating population in China. Front Public Health 2022; 10:977103. [PMID: 36187657 PMCID: PMC9521649 DOI: 10.3389/fpubh.2022.977103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/15/2022] [Indexed: 01/25/2023] Open
Abstract
The floating population has been growing rapidly in China, and their fertility behaviors do affect urban management and development. Based on the data set of the China Migrants Dynamic Survey in 2016, the logistic regression model and multiple linear regression model were used to explore the related factors of fertility behaviors among the floating populace. The artificial neural network model, the naive Bayes model, and the logistic regression model were used for prediction. The findings showed that age, gender, ethnic, household registration, education level, occupation, duration of residence, scope of migration, housing, economic conditions, and health services all affected the reproductive behavior of the floating population. Among them, the improvement duration of post-migration residence and family economic conditions positively impacted their fertility behavior. Non-agricultural new industry workers with college degrees or above living in first-tier cities were less likely to have children and more likely to delay childbearing. Among the prediction models, both the artificial neural network model and logistic regression model had better prediction effects. Improving the employment and income of new industry workers, and introducing preferential housing policies might improve their probability of bearing children. The artificial neural network and logistic regression model could predict individual fertility behavior and provide a scientific basis for the urban population management.
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Affiliation(s)
- Xiaoxia Zhu
- Department of Epidemiology & Biostatistics, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Zhixin Zhu
- Department of Epidemiology & Biostatistics, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Lanfang Gu
- Department of Epidemiology & Biostatistics, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Liang Chen
- Department of Epidemiology & Biostatistics, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Yancen Zhan
- Department of Epidemiology & Biostatistics, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Xiuyang Li
- Department of Epidemiology & Biostatistics, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China,*Correspondence: Xiuyang Li
| | - Cheng Huang
- Zhejiang University Library, Zhejiang University, Hangzhou, China
| | - Jiangang Xu
- Zhejiang University Library, Zhejiang University, Hangzhou, China
| | - Jie Li
- Zhejiang University Library, Zhejiang University, Hangzhou, China
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18
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Jin L, Dong X, Tan W, Huang B. Incidence, dynamics and recurrences of reverse cleavage in aneuploid, mosaic and euploid blastocysts, and its relationship with embryo quality. J Ovarian Res 2022; 15:91. [PMID: 35932054 PMCID: PMC9356443 DOI: 10.1186/s13048-022-01026-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 08/02/2022] [Indexed: 11/10/2022] Open
Abstract
Background During embryonic development, the normality of cleavage and the ploidy state are closely related to the final clinical outcome. At present, many research teams are focusing on the combined application of timelapse (TL) technology and preimplantation genetic testing (PGT) technology, hoping to find a connection between the two aspects of morphodynamics and genes. In the process of embryonic cleavage, there is a common abnormal cleavage pattern called reverse cleavage (RC). RC refers to blastomere fusion and failed cytokinesis. There are very few reports about it. Whether the occurrence of RC affects blastocyst euploidy is even less clear. Whether the RC phenomenon affects the embryonic developmental potential and whether it is related to the embryo ploidy. This is important for clinicians and embryologists. In this study, we used TL to observe whether there was a phenomenon of RC in each biopsy embryo and then combined it with the ploidy state to give an answer, which provided support for the selection strategy of RC embryos. Methods A total of 405 TL-PGT cycles and 1,467 blastocysts were included in the study. All TL data were collected from the Reproductive Medicine Center, Huazhong University of Science and Technology Hospital. Embryos images throughout embryonic development, from post-insemination to day 5 or 6 until biopsy and cryopreservation, were acquired by the Embryoscope Plus TL microscopy system from January 2019 to December 2020. This study investigated the overall incidence of RC during cleavage; the relationship between RC phenomenon and the number of occurrences and ploidy results; the relationship between RC occurrence and blastocyst developmental quality, as well as the dynamics of RC embryos. Results Among the 1,453 blastocysts biopsied, 400 blastocysts showed RC phenomenon at the cleavage stage, and the incidence rate was 25.9%. In euploid, mosaic and aneuploid embryos, the incidence of RC was 27.2%, 26.6%, and 25.0%, respectively. The incidence of RC was similar among these three groups with no significant difference (P > 0.05). The number of RC occurrences was not associated with embryo ploidy status (P > 0.05). In general, the blastocyst quality of the RC + group was lower than that of the RC- group. In the ICM score, the proportion of A score in the RC + group was significantly lower than that in RC- group (P < 0.05). In the TE score, there was no significant difference between the two groups of A-grade blastocysts, but the proportion of B-grade blastocysts in the RC + group was significantly lower than that in the RC- group (P < 0.01). In terms of developmental kinetic parameters, the cleavage synchrony parameters s2 and s3 were significantly longer in RC + embryos than in RC- embryos (P < 0.05). However, these changes in kinetic parameters were not significantly different between the euploid, mosaic and aneuploid groups. Conclusions The chromosomal euploidy of cleavage-stage embryos with RC phenomenon developed to the blastocyst stage was not significantly different from that of cleavage normal blastocysts. Therefore, RC embryos should not be discarded. It is recommended to select and utilize blastocyst culture, which has similar clinical value to normal cleavage embryos. Supplementary Information The online version contains supplementary material available at 10.1186/s13048-022-01026-9.
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Affiliation(s)
- Lei Jin
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China, 430030
| | - Xiyuan Dong
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China, 430030
| | - Wei Tan
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China, 430030
| | - Bo Huang
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China, 430030.
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