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Yang HY, Leahy BD, Jang WD, Wei D, Kalma Y, Rahav R, Carmon A, Kopel R, Azem F, Venturas M, Kelleher CP, Cam L, Pfister H, Needleman DJ, Ben-Yosef D. BlastAssist: a deep learning pipeline to measure interpretable features of human embryos. Hum Reprod 2024; 39:698-708. [PMID: 38396213 DOI: 10.1093/humrep/deae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 01/05/2024] [Indexed: 02/25/2024] Open
Abstract
STUDY QUESTION Can the BlastAssist deep learning pipeline perform comparably to or outperform human experts and embryologists at measuring interpretable, clinically relevant features of human embryos in IVF? SUMMARY ANSWER The BlastAssist pipeline can measure a comprehensive set of interpretable features of human embryos and either outperform or perform comparably to embryologists and human experts in measuring these features. WHAT IS KNOWN ALREADY Some studies have applied deep learning and developed 'black-box' algorithms to predict embryo viability directly from microscope images and videos but these lack interpretability and generalizability. Other studies have developed deep learning networks to measure individual features of embryos but fail to conduct careful comparisons to embryologists' performance, which are fundamental to demonstrate the network's effectiveness. STUDY DESIGN, SIZE, DURATION We applied the BlastAssist pipeline to 67 043 973 images (32 939 embryos) recorded in the IVF lab from 2012 to 2017 in Tel Aviv Sourasky Medical Center. We first compared the pipeline measurements of individual images/embryos to manual measurements by human experts for sets of features, including: (i) fertilization status (n = 207 embryos), (ii) cell symmetry (n = 109 embryos), (iii) degree of fragmentation (n = 6664 images), and (iv) developmental timing (n = 21 036 images). We then conducted detailed comparisons between pipeline outputs and annotations made by embryologists during routine treatments for features, including: (i) fertilization status (n = 18 922 embryos), (ii) pronuclei (PN) fade time (n = 13 781 embryos), (iii) degree of fragmentation on Day 2 (n = 11 582 embryos), and (iv) time of blastulation (n = 3266 embryos). In addition, we compared the pipeline outputs to the implantation results of 723 single embryo transfer (SET) cycles, and to the live birth results of 3421 embryos transferred in 1801 cycles. PARTICIPANTS/MATERIALS, SETTING, METHODS In addition to EmbryoScope™ image data, manual embryo grading and annotations, and electronic health record (EHR) data on treatment outcomes were also included. We integrated the deep learning networks we developed for individual features to construct the BlastAssist pipeline. Pearson's χ2 test was used to evaluate the statistical independence of individual features and implantation success. Bayesian statistics was used to evaluate the association of the probability of an embryo resulting in live birth to BlastAssist inputs. MAIN RESULTS AND THE ROLE OF CHANCE The BlastAssist pipeline integrates five deep learning networks and measures comprehensive, interpretable, and quantitative features in clinical IVF. The pipeline performs similarly or better than manual measurements. For fertilization status, the network performs with very good parameters of specificity and sensitivity (area under the receiver operating characteristics (AUROC) 0.84-0.94). For symmetry score, the pipeline performs comparably to the human expert at both 2-cell (r = 0.71 ± 0.06) and 4-cell stages (r = 0.77 ± 0.07). For degree of fragmentation, the pipeline (acc = 69.4%) slightly under-performs compared to human experts (acc = 73.8%). For developmental timing, the pipeline (acc = 90.0%) performs similarly to human experts (acc = 91.4%). There is also strong agreement between pipeline outputs and annotations made by embryologists during routine treatments. For fertilization status, the pipeline and embryologists strongly agree (acc = 79.6%), and there is strong correlation between the two measurements (r = 0.683). For degree of fragmentation, the pipeline and embryologists mostly agree (acc = 55.4%), and there is also strong correlation between the two measurements (r = 0.648). For both PN fade time (r = 0.787) and time of blastulation (r = 0.887), there's strong correlation between the pipeline and embryologists. For SET cycles, 2-cell time (P < 0.01) and 2-cell symmetry (P < 0.03) are significantly correlated with implantation success rate, while other features showed correlations with implantation success without statistical significance. In addition, 2-cell time (P < 5 × 10-11), PN fade time (P < 5 × 10-10), degree of fragmentation on Day 3 (P < 5 × 10-4), and 2-cell symmetry (P < 5 × 10-3) showed statistically significant correlation with the probability of the transferred embryo resulting in live birth. LIMITATIONS, REASONS FOR CAUTION We have not tested the BlastAssist pipeline on data from other clinics or other time-lapse microscopy (TLM) systems. The association study we conducted with live birth results do not take into account confounding variables, which will be necessary to construct an embryo selection algorithm. Randomized controlled trials (RCT) will be necessary to determine whether the pipeline can improve success rates in clinical IVF. WIDER IMPLICATIONS OF THE FINDINGS BlastAssist provides a comprehensive and holistic means of evaluating human embryos. Instead of using a black-box algorithm, BlastAssist outputs meaningful measurements of embryos that can be interpreted and corroborated by embryologists, which is crucial in clinical decision making. Furthermore, the unprecedentedly large dataset generated by BlastAssist measurements can be used as a powerful resource for further research in human embryology and IVF. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by Harvard Quantitative Biology Initiative, the NSF-Simons Center for Mathematical and Statistical Analysis of Biology at Harvard (award number 1764269), the National Institute of Heath (award number R01HD104969), the Perelson Fund, and the Sagol fund for embryos and stem cells as part of the Sagol Network. The authors declare no competing interests. TRIAL REGISTRATION NUMBER Not applicable.
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Affiliation(s)
- Helen Y Yang
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- Department of Biophysics, Harvard Graduate School of Arts and Sciences, Cambridge, MA, USA
| | - Brian D Leahy
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- Department of Applied Physics, Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA
| | - Won-Dong Jang
- Department of Computer Science, Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA
| | - Donglai Wei
- Department of Computer Science, Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA
| | - Yael Kalma
- Department of Reproduction and IVF, Lis Maternity Hospital Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Roni Rahav
- Department of Reproduction and IVF, Lis Maternity Hospital Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Ariella Carmon
- Department of Reproduction and IVF, Lis Maternity Hospital Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Rotem Kopel
- Department of Reproduction and IVF, Lis Maternity Hospital Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Foad Azem
- Department of Reproduction and IVF, Lis Maternity Hospital Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Marta Venturas
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Colm P Kelleher
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Liz Cam
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Hanspeter Pfister
- Department of Computer Science, Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA
| | - Daniel J Needleman
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- Department of Applied Physics, Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA
| | - Dalit Ben-Yosef
- Department of Reproduction and IVF, Lis Maternity Hospital Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
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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 2024:00001703-990000000-00122. [PMID: 38597425 DOI: 10.1097/gco.0000000000000951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [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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3
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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4
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Canosa S, Licheri N, Bergandi L, Gennarelli G, Paschero C, Beccuti M, Cimadomo D, Coticchio G, Rienzi L, Benedetto C, Cordero F, Revelli A. A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development. J Ovarian Res 2024; 17:63. [PMID: 38491534 PMCID: PMC10941455 DOI: 10.1186/s13048-024-01376-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/16/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to assist embryologists with automatized and objective predictive models able to standardize human embryo assessment. In this study, we aimed at developing a novel ML-based strategy to identify relevant patterns associated with the prediction of blastocyst development stage on day 5. METHODS We retrospectively analysed the morphokinetics of 575 embryos obtained from 80 women who underwent IVF at our Unit. Embryo morphokinetics was registered using the Geri plus® time-lapse system. Overall, 30 clinical, morphological and morphokinetic variables related to women and embryos were recorded and combined. Some embryos reached the expanded blastocyst stage on day 5 (BL Group, n = 210), some others did not (nBL Group, n = 365). RESULTS The novel EmbryoMLSelection framework was developed following four-steps: Feature Selection, Rules Extraction, Rules Selection and Rules Evaluation. Six rules composed by a combination of 8 variables were finally selected, and provided a predictive power described by an AUC of 0.84 and an accuracy of 81%. CONCLUSIONS We provided herein a new feature-signature able to identify with an high performance embryos with the best developmental competence to reach the expanded blastocyst stage on day 5. Clear and clinically relevant cut-offs were identified for each considered variable, providing an objective tool for early embryo developmental assessment.
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Affiliation(s)
- S Canosa
- Gynecology and Obstetrics 1U, Physiopathology of Reproduction and IVF Unit, Department of Surgical Sciences, S. Anna Hospital, University of Turin, Turin, Italy.
- IVIRMA Global Research Alliance, Livet, Turin, Italy.
| | - N Licheri
- Department of Computer Science, University di Turin, Turin, Italy
| | - L Bergandi
- Department of Oncology, University of Turin, Turin, Italy
| | - G Gennarelli
- Gynecology and Obstetrics 1U, Physiopathology of Reproduction and IVF Unit, Department of Surgical Sciences, S. Anna Hospital, University of Turin, Turin, Italy
- IVIRMA Global Research Alliance, Livet, Turin, Italy
| | - C Paschero
- Gynecology and Obstetrics 1U, Physiopathology of Reproduction and IVF Unit, Department of Surgical Sciences, S. Anna Hospital, University of Turin, Turin, Italy
| | - M Beccuti
- Department of Computer Science, University di Turin, Turin, Italy
| | - D Cimadomo
- IVIRMA Global Research Alliance, Genera, Clinica Valle Giulia, Rome, Italy
| | - G Coticchio
- IVIRMA Global Research Alliance, 9.Baby, Bologna, Italy
| | - L Rienzi
- IVIRMA Global Research Alliance, Genera, Clinica Valle Giulia, Rome, Italy
- Department of Biomolecular Sciences, University of Urbino "Carlo Bo", Urbino, Italy
| | - C Benedetto
- Gynecology and Obstetrics 1U, Physiopathology of Reproduction and IVF Unit, Department of Surgical Sciences, S. Anna Hospital, University of Turin, Turin, Italy
| | - F Cordero
- Department of Computer Science, University di Turin, Turin, Italy
| | - A Revelli
- Gynecology and Obstetrics 1U, Physiopathology of Reproduction and IVF Unit, Department of Surgical Sciences, S. Anna Hospital, University of Turin, Turin, Italy
- Gynecology and Obstetrics 2U, Department of Surgical Sciences, S. Anna Hospital, University of Turin, Turin, Italy
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5
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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 02/22/2024] [Indexed: 03/07/2024] Open
Abstract
The combination of a good quality embryo and proper maternal health factors promise higher chances of a successful in vitro fertilization (IVF) procedure leading to clinical pregnancy and live birth. Of these two factors, selection of a good embryo is a controllable aspect. The current gold standard in clinical practice is visual assessment of an embryo based on its morphological appearance by trained embryologists. More recently, machine learning has been incorporated into embryo selection "packages". Here, we report EVATOM: a machine-learning assisted embryo health assessment tool utilizing an optical quantitative phase imaging technique called artificial confocal microscopy (ACM). We present a label-free nucleus detection method with, to the best of our knowledge, novel quantitative embryo health biomarkers. Two viability assessment models are presented for grading embryos into two classes: healthy/intermediate (H/I) or sick (S) class. The models achieve a weighted F1 score of 1.0 and 0.99 respectively on the in-distribution test set of 72 fixed embryos and a weighted F1 score of 0.9 and 0.95 respectively on the out-of-distribution test dataset of 19 time-instances from 8 live embryos.
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Affiliation(s)
- Neha Goswami
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
- Beckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Nicola Winston
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, University of Illinois at Chicago College of Medicine, Chicago, IL, 60612, USA
| | - Wonho Choi
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Nastasia Z E Lai
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Rachel B Arcanjo
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Animal Science, University of California, Davis, CA, 95616, USA
| | - Xi Chen
- Beckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- School of Applied and Engineering Physics, Cornell University, Ithaca, NY, 14850, USA
| | - Nahil Sobh
- NCSA Center for Artificial Intelligence Innovation, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Romana A Nowak
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Mark A Anastasio
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
- Beckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Gabriel Popescu
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Beckman Institute of Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
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He C, Karpavičiūtė N, Hariharan R, Lees L, Jacques C, Ferrand T, Chambost J, Wouters K, Malmsten J, Miller R, Zaninovic N, Vasconcelos F, Hickman C. Seeking arrangements: cell contact as a cleavage-stage biomarker. Reprod Biomed Online 2024; 48:103654. [PMID: 38246064 DOI: 10.1016/j.rbmo.2023.103654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/10/2023] [Accepted: 10/30/2023] [Indexed: 01/23/2024]
Abstract
RESEARCH QUESTION What can three-dimensional cell contact networks tell us about the developmental potential of cleavage-stage human embryos? DESIGN This pilot study was a retrospective analysis of two Embryoscope imaging datasets from two clinics. An artificial intelligence system was used to reconstruct the three-dimensional structure of embryos from 11-plane focal stacks. Networks of cell contacts were extracted from the resulting embryo three-dimensional models and each embryo's mean contacts per cell was computed. Unpaired t-tests and receiver operating characteristic curve analysis were used to statistically analyse mean cell contact outcomes. Cell contact networks from different embryos were compared with identical embryos with similar cell arrangements. RESULTS At t4, a higher mean number of contacts per cell was associated with greater rates of blastulation and blastocyst quality. No associations were found with biochemical pregnancy, live birth, miscarriage or ploidy. At t8, a higher mean number of contacts was associated with increased blastocyst quality, biochemical pregnancy and live birth. No associations were found with miscarriage or aneuploidy. Mean contacts at t4 weakly correlated with those at t8. Four-cell embryos fell into nine distinct cell arrangements; the five most common accounted for 97% of embryos. Eight-cell embryos, however, displayed a greater degree of variation with 59 distinct cell arrangements. CONCLUSIONS Evidence is provided for the clinical relevance of cleavage-stage cell arrangement in the human preimplantation embryo beyond the four-cell stage, which may improve selection techniques for day-3 transfers. This pilot study provides a strong case for further investigation into spatial biomarkers and three-dimensional morphokinetics.
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Affiliation(s)
- Chloe He
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London 43-45 Foley St, London, W1W 7TY, UK.; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK.; AI Team, Apricity, 14 Grays Inn Rd, London WC1 X 8HN, UK..
| | | | | | - Lilly Lees
- AI Team, Apricity, 14 Grays Inn Rd, London WC1 X 8HN, UK
| | | | | | | | - Koen Wouters
- Brussels IVF, University Hospital Brussels, Jette Bldg R, Laarbeeklaan 101 1090 Jette, Belgium, Brussels
| | - Jonas Malmsten
- Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, 1305 York Ave 6th floor, New York, NY 10021, USA
| | - Ryan Miller
- Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, 1305 York Ave 6th floor, New York, NY 10021, USA
| | - Nikica Zaninovic
- Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, 1305 York Ave 6th floor, New York, NY 10021, USA
| | - Francisco Vasconcelos
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London 43-45 Foley St, London, W1W 7TY, UK.; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK
| | - Cristina Hickman
- AI Team, Apricity, 14 Grays Inn Rd, London WC1 X 8HN, UK.; Institute of Reproductive and Developmental Biology, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0HS, UK
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Liao Z, Yan C, Wang J, Zhang N, Yang H, Lin C, Zhang H, Wang W, Li W. A clinical consensus-compliant deep learning approach to quantitatively evaluate human in vitro fertilization early embryonic development with optical microscope images. Artif Intell Med 2024; 149:102773. [PMID: 38462274 DOI: 10.1016/j.artmed.2024.102773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 12/13/2023] [Accepted: 01/14/2024] [Indexed: 03/12/2024]
Abstract
The selection of embryos is a key for the success of in vitro fertilization (IVF). However, automatic quality assessment on human IVF embryos with optical microscope images is still challenging. In this study, we developed a clinical consensus-compliant deep learning approach, named Esava (Embryo Segmentation and Viability Assessment), to quantitatively evaluate the development of IVF embryos using optical microscope images. In total 551 optical microscope images of human IVF embryos of day-2 to day-3 were collected, preprocessed, and annotated. Using the Faster R-CNN model as baseline, our Esava model was constructed, refined, trained, and validated for precise and robust blastomere detection. A novel algorithm Crowd-NMS was proposed and employed in Esava to enhance the object detection and to precisely quantify the embryonic cells and their size uniformity. Additionally, an innovative GrabCut-based unsupervised module was integrated for the segmentation of blastomeres and embryos. Independently tested on 94 embryo images for blastomere detection, Esava obtained the high rates of 0.9940, 0.9121, and 0.9531 for precision, recall, and mAP respectively, and gained significant advances compared with previous computational methods. Intraclass correlation coefficients indicated the consistency between Esava and three experienced embryologists. Another test on 51 extra images demonstrated that Esava surpassed other tools significantly, achieving the highest average precision 0.9025. Moreover, it also accurately identified the borders of blastomeres with mIoU over 0.88 on the independent testing dataset. Esava is compliant with the Istanbul clinical consensus and compatible to senior embryologists. Taken together, Esava improves the accuracy and efficiency of embryonic development assessment with optical microscope images.
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Affiliation(s)
- Zaowen Liao
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chaoyu Yan
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jianbo Wang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Ningfeng Zhang
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huan Yang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Medical Big Data Center, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chenghao Lin
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Haiyue Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Wenjun Wang
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Weizhong Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China; Key Laboratory of Tropical Disease Control (Ministry of Education), Sun Yat-Sen University, Guangzhou, China.
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8
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Güell E. Criteria for implementing artificial intelligence systems in reproductive medicine. Clin Exp Reprod Med 2024; 51:1-12. [PMID: 38035589 PMCID: PMC10914497 DOI: 10.5653/cerm.2023.06009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/31/2023] [Indexed: 12/02/2023] Open
Abstract
This review article discusses the integration of artificial intelligence (AI) in assisted reproductive technology and provides key concepts to consider when introducing AI systems into reproductive medicine practices. The article highlights the various applications of AI in reproductive medicine and discusses whether to use commercial or in-house AI systems. This review also provides criteria for implementing new AI systems in the laboratory and discusses the factors that should be considered when introducing AI in the laboratory, including the user interface, scalability, training, support, follow-up, cost, ethics, and data quality. The article emphasises the importance of ethical considerations, data quality, and continuous algorithm updates to ensure the accuracy and safety of AI systems.
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Affiliation(s)
- Enric Güell
- CONSULTFIV, Valls, Spain
- Procrear, Reus, Spain
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Wu T, Wu Y, Yan J, Zhang J, Wang S. Microfluidic chip as a promising evaluation method in assisted reproduction: A systematic review. Bioeng Transl Med 2024; 9:e10625. [PMID: 38435817 PMCID: PMC10905557 DOI: 10.1002/btm2.10625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/26/2023] [Accepted: 11/09/2023] [Indexed: 03/05/2024] Open
Abstract
The aim of assisted reproductive technology (ART) is to select the high-quality sperm, oocytes, and embryos, and finally achieve a successful pregnancy. However, functional evaluation is hindered by intra- and inter-operator variability. Microfluidic chips emerge as the one of the most powerful tools to analyze biological samples for reduced size, precise control, and flexible extension. Herein, a systematic search was conducted in PubMed, Scopus, Web of Science, ScienceDirect, and IEEE Xplore databases until March 2023. We displayed and prospected all detection strategies based on microfluidics in the ART field. After full-text screening, 71 studies were identified as eligible for inclusion. The percentages of human and mouse studies equaled with 31.5%. The prominent country in terms of publication number was the USA (n = 13). Polydimethylsiloxane (n = 49) and soft lithography (n = 28) were the most commonly used material and fabrication method, respectively. All articles were classified into three types: sperm (n = 38), oocytes (n = 20), and embryos (n = 13). The assessment contents included motility, counting, mechanics, permeability, impedance, secretion, oxygen consumption, and metabolism. Collectively, the microfluidic chip technology facilitates more efficient, accurate, and objective evaluation in ART. It can even be combined with artificial intelligence to assist the daily activities of embryologists. More well-designed clinical studies and affordable integrated microfluidic chips are needed to validate the safety, efficacy, and reproducibility. Trial registration: The protocol was registered in the Open Science Frame REGISTRIES (identification: osf.io/6rv4a).
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Affiliation(s)
- Tong Wu
- National Clinical Research Center for Obstetrical and Gynecological DiseasesTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
- Key Laboratory of Cancer Invasion and Metastasis, Ministry of EducationTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
- Department of Obstetrics and GynecologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Yangyang Wu
- College of Animal Science and TechnologySichuan Agricultural UniversityYa'anSichuanChina
| | - Jinfeng Yan
- National Clinical Research Center for Obstetrical and Gynecological DiseasesTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
- Key Laboratory of Cancer Invasion and Metastasis, Ministry of EducationTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
- Department of Obstetrics and GynecologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
- School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhanChina
| | - Jinjin Zhang
- National Clinical Research Center for Obstetrical and Gynecological DiseasesTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
- Key Laboratory of Cancer Invasion and Metastasis, Ministry of EducationTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
- Department of Obstetrics and GynecologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Shixuan Wang
- National Clinical Research Center for Obstetrical and Gynecological DiseasesTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
- Key Laboratory of Cancer Invasion and Metastasis, Ministry of EducationTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
- Department of Obstetrics and GynecologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
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Ma Y, Zhang B, Liu Z, Liu Y, Wang J, Li X, Feng F, Ni Y, Li S. IAS-FET: An intelligent assistant system and an online platform for enhancing successful rate of in-vitro fertilization embryo transfer technology based on clinical features. Comput Methods Programs Biomed 2024; 245:108050. [PMID: 38301430 DOI: 10.1016/j.cmpb.2024.108050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 02/03/2024]
Abstract
BACKGROUND Among all of the assisted reproductive technology (ART) methods, in vitro fertilization-embryo transfer (IVF-ET) holds a prominent position as a key solution for overcoming infertility. However, its success rate hovers at a modest 30% to 70%. Adding to the challenge is the absence of effective models and clinical tools capable of predicting the outcome of IVF-ET before embryo formation. Our study is dedicated to filling this critical gap by aiming to predict IVF-ET outcomes and ultimately enhance the success rate of this transformative procedure. METHODS In this retrospective study, infertile patients who received artificial assisted pregnancy treatment at Gansu Provincial Maternity and Child-care Hospital in China were enrolled from 2016 to 2020. Individual's clinical information were studied by cascade XGBoost method to build an intelligent assisted system for predicting the outcome of IVF-ET, called IAS-FET. The cascade XGBoost model was trained using clinical information from 2292 couples and externally tested using clinical information from 573 couples. In addition, several schemes which will be of help for patients to adjust their physical condition to improve their success rate on ART were suggested by IAS-FET. RESULTS The outcome of IVF-ET can be predicted by the built IAS-FET method with the area under curve (AUC) value of 0.8759 on the external test set. Besides, this IAS-FET method can provide several schemes to improve the successful rate of IVF-ET outcomes. The built tool for IAS-FET is addressed as a free platform online at http://www.cppdd.cn/ART for the convenient usage of users. CONCLUSIONS It suggested the significant influence of personal clinical features for the success of ART. The proposed system IAS-FET based on the top 27 factors could be a promising tool to predict the outcome of ART and propose a plan for the patient's physical adjustment. With the help of IAS-FET, patients can take informed steps towards increasing their chances of a successful outcome on their journey to parenthood.
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Affiliation(s)
- Ying Ma
- Gansu Provincial Maternity and Child-care Hospital, Lanzhou, Gansu 730030, China
| | - Bowen Zhang
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei 430073, China
| | - Zhaoqing Liu
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Yujie Liu
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Jiarui Wang
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Xingxuan Li
- School of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, Gansu 730030, China
| | - Fan Feng
- Gansu Provincial Maternity and Child-care Hospital, Lanzhou, Gansu 730030, China
| | - Yali Ni
- Gansu Provincial Maternity and Child-care Hospital, Lanzhou, Gansu 730030, China
| | - Shuyan Li
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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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13
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Kim HM, Ko T, Kang H, Choi S, Park JH, Chung MK, Kim M, Kim NY, Lee HJ. Improved prediction of clinical pregnancy using artificial intelligence with enhanced inner cell mass and trophectoderm images. Sci Rep 2024; 14:3240. [PMID: 38331914 PMCID: PMC10853203 DOI: 10.1038/s41598-024-52241-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 01/16/2024] [Indexed: 02/10/2024] Open
Abstract
This study aimed to assess the performance of an artificial intelligence (AI) model for predicting clinical pregnancy using enhanced inner cell mass (ICM) and trophectoderm (TE) images. In this retrospective study, we included static images of 2555 day-5-blastocysts from seven in vitro fertilization centers in South Korea. The main outcome of the study was the predictive capability of the model to detect clinical pregnancies (gestational sac). Compared with the original embryo images, the use of enhanced ICM and TE images improved the average area under the receiver operating characteristic curve for the AI model from 0.716 to 0.741. Additionally, a gradient-weighted class activation mapping analysis demonstrated that the enhanced image-trained AI model was able to extract features from crucial areas of the embryo in 99% (506/512) of the cases. Particularly, it could extract the ICM and TE. In contrast, the AI model trained on the original images focused on the main areas in only 86% (438/512) of the cases. Our results highlight the potential efficacy of using ICM- and TE-enhanced embryo images when training AI models to predict clinical pregnancy.
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Affiliation(s)
| | - Taehoon Ko
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, South Korea
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea
- CMC Institute for Basic Medical Science, The Catholic Medical Center of The Catholic University of Korea, Seoul, South Korea
| | | | | | | | - Mi Kyung Chung
- Seoul Rachel Fertility Center, IVF Clinic, Seoul, South Korea
| | - Miran Kim
- Department of Obstetrics & Gynecology, Ajou University School of Medicine, Suwon, South Korea
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Capodanno F, Anastasi A, Cinti M, Bonesi F, Gallinelli A. Current and future methods for embryo selection: on a quest for reliable strategies to reduce time to pregnancy. Minerva Obstet Gynecol 2024; 76:80-88. [PMID: 37162493 DOI: 10.23736/s2724-606x.23.05257-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
INTRODUCTION The aim of this study was to analyze the usefulness of the principal embryological strategies to reduce time to pregnancy. EVIDENCE ACQUISITION A systematic search of publications in the PubMed/MEDLINE, Embase and Scopus databases from inception to present including "IVF," "blastocyst," "embryo colture," "competent embryo," "time to pregnancy," "aneuploid," "euploid," "vitrification," "preimplantation genetic," "IVF strategies" and "embryo selection" alone or in combinations has been done. EVIDENCE SYNTHESIS We have selected 230 articles and 9 of them have been included in this mini-review. CONCLUSIONS Several embryological strategies aimed to select the most competent embryo and reduce time to pregnancy have been proposed, even if few publications on this specific topic are available. preimplantation genetic testing (PGT-A) represents the unique method able to assess the embryonic chromosomal status, but this does not mean that PGT-A is a reliable strategy to reduce time to pregnancy. There is no consensus on a specific method to reduce time to pregnancy, nevertheless this final goal could be probably reached through a harmonious combination of procedures. Thus, a reliable strategy to reduce time to pregnancy could be achieved when embryo culture, embryo cryopreservation and PGT-A are perfectly integrated and appropriately offered to selected patients.
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Affiliation(s)
- Francesco Capodanno
- Center of Physiopathology of Human Reproduction, "Delta" Hospital, AUSL Ferrara, Ferrara, Italy
| | - Attilio Anastasi
- Center of Physiopathology of Human Reproduction, "Delta" Hospital, AUSL Ferrara, Ferrara, Italy -
| | - Marialuisa Cinti
- Center of Physiopathology of Human Reproduction, "Delta" Hospital, AUSL Ferrara, Ferrara, Italy
| | - Francesca Bonesi
- Center of Physiopathology of Human Reproduction, "Delta" Hospital, AUSL Ferrara, Ferrara, Italy
| | - Andrea Gallinelli
- Center of Physiopathology of Human Reproduction, "Delta" Hospital, AUSL Ferrara, Ferrara, Italy
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16
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Lei R, Chen S, Li W. Advances in the study of the correlation between insulin resistance and infertility. Front Endocrinol (Lausanne) 2024; 15:1288326. [PMID: 38348417 PMCID: PMC10860338 DOI: 10.3389/fendo.2024.1288326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/04/2024] [Indexed: 02/15/2024] Open
Abstract
This is a narrative review of the progress of research on the correlation between insulin resistance and infertility. Insulin resistance (IR) is not only involved in the development of various metabolic diseases, but also affects female reproductive function, and to some extent is closely related to female infertility. IR may increase the risk of female infertility by activating oxidative stress, interfering with energy metabolism, affecting oocyte development, embryo quality and endometrial tolerance, affecting hormone secretion and embryo implantation, as well as affecting assisted conception outcomes in infertile populations and reducing the success rate of assisted reproductive technology treatment in infertile populations. In addition, IR is closely associated with spontaneous abortion, gestational diabetes and other adverse pregnancies, and if not corrected in time, may increase the risk of obesity and metabolic diseases in the offspring in the long term. This article provides a review of the relationship between IR and infertility to provide new ideas for the treatment of infertility.
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Affiliation(s)
| | | | - Weihong Li
- Reproductive Medical Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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17
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Lee CI, Huang CC, Lee TH, Chen HH, Cheng EH, Lin PY, Yu TN, Chen CI, Chen CH, Lee MS. Associations between the artificial intelligence scoring system and live birth outcomes in preimplantation genetic testing for aneuploidy cycles. Reprod Biol Endocrinol 2024; 22:12. [PMID: 38233926 PMCID: PMC10792866 DOI: 10.1186/s12958-024-01185-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 01/12/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND Several studies have demonstrated that iDAScore is more accurate in predicting pregnancy outcomes in cycles without preimplantation genetic testing for aneuploidy (PGT-A) compared to KIDScore and the Gardner criteria. However, the effectiveness of iDAScore in cycles with PGT-A has not been thoroughly investigated. Therefore, this study aims to assess the association between artificial intelligence (AI)-based iDAScore (version 1.0) and pregnancy outcomes in single-embryo transfer (SET) cycles with PGT-A. METHODS This retrospective study was approved by the Institutional Review Board of Chung Sun Medical University, Taichung, Taiwan. Patients undergoing SET cycles (n = 482) following PGT-A at a single reproductive center between January 2017 and June 2021. The blastocyst morphology and morphokinetics of all embryos were evaluated using a time-lapse system. The blastocysts were ranked based on the scores generated by iDAScore, which were defined as AI scores, or by KIDScore D5 (version 3.2) following the manufacturer's protocols. A single blastocyst without aneuploidy was transferred after examining the embryonic ploidy status using a next-generation sequencing-based PGT-A platform. Logistic regression analysis with generalized estimating equations was conducted to assess whether AI scores are associated with the probability of live birth (LB) while considering confounding factors. RESULTS Logistic regression analysis revealed that AI score was significantly associated with LB probability (adjusted odds ratio [OR] = 2.037, 95% confidence interval [CI]: 1.632-2.542) when pulsatility index (PI) level and types of chromosomal abnormalities were controlled. Blastocysts were divided into quartiles in accordance with their AI score (group 1: 3.0-7.8; group 2: 7.9-8.6; group 3: 8.7-8.9; and group 4: 9.0-9.5). Group 1 had a lower LB rate (34.6% vs. 59.8-72.3%) and a higher rate of pregnancy loss (26% vs. 4.7-8.9%) compared with the other groups (p < 0.05). The receiver operating characteristic curve analysis verified that the iDAScore had a significant but limited ability to predict LB (area under the curve [AUC] = 0.64); this ability was significantly weaker than that of the combination of iDAScore, type of chromosomal abnormalities, and PI level (AUC = 0.67). In the comparison of the LB groups with the non-LB groups, the AI scores were significantly lower in the non-LB groups, both for euploid (median: 8.6 vs. 8.8) and mosaic (median: 8.0 vs. 8.6) SETs. CONCLUSIONS Although its predictive ability can be further enhanced, the AI score was significantly associated with LB probability in SET cycles. Euploid or mosaic blastocysts with low AI scores (≤ 7.8) were associated with a lower LB rate, indicating the potential of this annotation-free AI system as a decision-support tool for deselecting embryos with poor pregnancy outcomes following PGT-A.
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Affiliation(s)
- Chun-I Lee
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
- Department of Obstetrics and Gynecology, Chung Shan Medical University Hospital, Taichung, Taiwan
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Chun-Chia Huang
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Tsung-Hsien Lee
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
- Department of Obstetrics and Gynecology, Chung Shan Medical University Hospital, Taichung, Taiwan
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Hsiu-Hui Chen
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan
| | - En-Hui Cheng
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Pin-Yao Lin
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Tzu-Ning Yu
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | - Chung-I Chen
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | - Chien-Hong Chen
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan.
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan.
| | - Maw-Sheng Lee
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan.
- Department of Obstetrics and Gynecology, Chung Shan Medical University Hospital, Taichung, Taiwan.
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan.
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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19
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Wang S, Chen L, Sun H. Interpretable artificial intelligence-assisted embryo selection improved single-blastocyst transfer outcomes: a prospective cohort study. Reprod Biomed Online 2023; 47:103371. [PMID: 37839212 DOI: 10.1016/j.rbmo.2023.103371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/01/2023] [Accepted: 08/23/2023] [Indexed: 10/17/2023]
Abstract
RESEARCH QUESTION What is the pregnancy and neonatal outcomes of an interpretable artificial intelligence (AI) model for embryo selection in a prospective clinical trial? DESIGN This single-centre prospective cohort study was carried out from October 2021 to March 2022. A total of 330 eligible patients were assigned to their preferred groups, with 250 patients undergoing a fresh single-blastocyst transfer cycle after the exclusion criteria had been applied. For the AI-assisted group (AAG), embryologists selected the embryos for transfer based on the ranking recommendations provided by an interpretable AI system, while with the manual group, embryologists used the Gardner grading system to make their decisions. RESULTS The implantation rate was significantly higher in the AAG than the manual group (80.87% versus 68.15%, P = 0.022). No significant difference was found in terms of monozygotic twin rate, miscarriage rate, live birth rate and ectopic pregnancy rate between the groups. Furthermore, there was no significant difference in terms of neonatal outcomes, including gestational weeks, premature birth rate, birth height, birthweight, sex ratio at birth and newborn malformation rate. The consensus rate between the AI and retrospective analysis by the embryologists was significantly higher for good-quality embryos (i.e. grade 4BB or higher) versus poor-quality embryos (i.e. less than 4BB) (84.71% versus 25%, P < 0.001). CONCLUSIONS These prospective trial results suggest that the proposed AI system could effectively help embryologists to improve the implantation rate with single-blastocyst transfer compared with traditional manual evaluation methods.
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Affiliation(s)
- Shanshan Wang
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Lei Chen
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Haixiang Sun
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
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20
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Jiang VS, Pavlovic ZJ, Hariton E. The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies. Obstet Gynecol Clin North Am 2023; 50:747-762. [PMID: 37914492 DOI: 10.1016/j.ogc.2023.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
Artificial intelligence (AI) and machine learning, the form most commonly used in medicine, offer powerful tools utilizing the strengths of large data sets and intelligent algorithms. These systems can help to revolutionize delivery of treatments, access to medical care, and improvement of outcomes, particularly in the realm of reproductive medicine. Whether that is more robust oocyte and embryo grading or more accurate follicular measurement, AI will be able to aid clinicians, and most importantly patients, in providing the best possible and individualized care. However, despite all of the potential strengths of AI, algorithms are not immune to bias and are vulnerable to the many socioeconomic and demographic biases that current healthcare systems suffer from. Wrong diagnoses as well is furthering of healthcare discrimination are real possibilities if both the capabilities and limitations of AI are not well understood. Armed with appropriate knowledge of how AI can most appropriately operate within medicine, and specifically reproductive medicine, will enable clinicians to both create and utilize machine learning-based innovations for the furthering of reproductive medicine and ultimately achieving the goal of building of healthy families.
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Affiliation(s)
- Victoria S Jiang
- Division of Reproductive Endocrinology & Infertility, Vincent Department of Obstetrics and Gynecology, Massachusetts General Hospital/Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA 02116, USA
| | - Zoran J Pavlovic
- Department of Obstetrics and Gynecology/Reproductive Endocrinology and Infertility, University of South Florida, Morsani College of Medicine, 2 Tampa General Circle, 6th Floor, Suite 6022, Tampa, FL 33602, USA
| | - Eduardo Hariton
- Reproductive Science Center of the San Francisco Bay Area, 100 Park Place #200, San Ramon, CA 94583, USA.
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21
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Butt Z, Tinning H, O'Connell MJ, Fenn J, Alberio R, Forde N. Understanding conceptus-maternal interactions: what tools do we need to develop? Reprod Fertil Dev 2023; 36:81-92. [PMID: 38064186 DOI: 10.1071/rd23181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023] Open
Abstract
Communication between the maternal endometrium and developing embryo/conceptus is critical to support successful pregnancy to term. Studying the peri-implantation period of pregnancy is critical as this is when most pregnancy loss occurs in cattle. Our current understanding of these interactions is limited, due to the lack of appropriate in vitro models to assess these interactions. The endometrium is a complex and heterogeneous tissue that is regulated in a transcriptional and translational manner throughout the oestrous cycle. While there are in vitro models to study endometrial function, they are static and 2D in nature or explant models and are limited in how well they recapitulate the in vivo endometrium. Recent developments in organoid systems, microfluidic approaches, extracellular matrix biology, and in silico approaches provide a new opportunity to develop in vitro systems that better model the in vivo scenario. This will allow us to investigate in a more high-throughput manner the fundamental molecular interactions that are required for successful pregnancy in cattle.
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Affiliation(s)
- Zenab Butt
- Discovery and Translational Sciences Department, Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds LS2 9JT, UK
| | - Haidee Tinning
- Discovery and Translational Sciences Department, Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds LS2 9JT, UK
| | - Mary J O'Connell
- Computational and Molecular Evolutionary Biology Group, School of Life Sciences, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Jonathan Fenn
- Computational and Molecular Evolutionary Biology Group, School of Life Sciences, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Ramiro Alberio
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
| | - Niamh Forde
- Discovery and Translational Sciences Department, Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds LS2 9JT, UK
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22
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Tomita S. Unlocking the potential of bioanalytical data through machine learning. ANAL SCI 2023; 39:1937-1938. [PMID: 37996767 DOI: 10.1007/s44211-023-00447-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2023]
Affiliation(s)
- Shunsuke Tomita
- Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba, Ibaraki, 305-8566, Japan.
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23
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Nguyen TV, Diakiw SM, VerMilyea MD, Dinsmore AW, Perugini M, Perugini D, Hall JMM. Efficient automated error detection in medical data using deep-learning and label-clustering. Sci Rep 2023; 13:19587. [PMID: 37949906 PMCID: PMC10638377 DOI: 10.1038/s41598-023-45946-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 10/26/2023] [Indexed: 11/12/2023] Open
Abstract
Medical datasets inherently contain errors from subjective or inaccurate test results, or from confounding biological complexities. It is difficult for medical experts to detect these elusive errors manually, due to lack of contextual information, limiting data privacy regulations, and the sheer scale of data to be reviewed. Current methods for training robust artificial intelligence (AI) models on data containing mislabeled examples generally fall into one of several categories-attempting to improve the robustness of the model architecture, the regularization techniques used, the loss function used during training, or selecting a subset of data that contains cleaner labels. This last category requires the ability to efficiently detect errors either prior to or during training, either relabeling them or removing them completely. More recent progress in error detection has focused on using multi-network learning to minimize deleterious effects of errors on training, however, using many neural networks to reach a consensus on which data should be removed can be computationally intensive and inefficient. In this work, a deep-learning based algorithm was used in conjunction with a label-clustering approach to automate error detection. For dataset with synthetic label flips added, these errors were identified with an accuracy of up to 85%, while requiring up to 93% less computing resources to complete compared to a previous model consensus approach developed previously. The resulting trained AI models exhibited greater training stability and up to a 45% improvement in accuracy, from 69 to over 99% compared to the consensus approach, at least 10% improvement on using noise-robust loss functions in a binary classification problem, and a 51% improvement for multi-class classification. These results indicate that practical, automated a priori detection of errors in medical data is possible, without human oversight.
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Affiliation(s)
- T V Nguyen
- Presagen, Adelaide, SA, 5000, Australia.
- School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, 2522, Australia.
| | | | - M D VerMilyea
- Ovation Fertility, Austin, TX, 78731, USA
- Texas Fertility Center, Austin, TX, 78731, USA
| | - A W Dinsmore
- California Fertility Partners, Los Angeles, CA, 90025, USA
| | - M Perugini
- Presagen, Adelaide, SA, 5000, Australia
- Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5000, Australia
| | | | - J M M Hall
- Presagen, Adelaide, SA, 5000, Australia
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, SA, 5005, Australia
- School of Physical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia
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24
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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25
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Palmer GA, Tomkin G, Martín-Alcalá HE, Mendizabal-Ruiz G, Cohen J. The Internet of Things in assisted reproduction. Reprod Biomed Online 2023; 47:103338. [PMID: 37757612 DOI: 10.1016/j.rbmo.2023.103338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/06/2023] [Accepted: 08/07/2023] [Indexed: 09/29/2023]
Abstract
The Internet of Things (IoT) is a network connecting physical objects with sensors, software and internet connectivity for data exchange. Integrating the IoT with medical devices shows promise in healthcare, particularly in IVF laboratories. By leveraging telecommunications, cybersecurity, data management and intelligent systems, the IoT can enable a data-driven laboratory with automation, improved conditions, personalized treatment and efficient workflows. The integration of 5G technology ensures fast and reliable connectivity for real-time data transmission, while blockchain technology secures patient data. Fog computing reduces latency and enables real-time analytics. Microelectromechanical systems enable wearable IoT and miniaturized monitoring devices for tracking IVF processes. However, challenges such as security risks and network issues must be addressed through cybersecurity measures and networking advancements. Clinical embryologists should maintain their expertise and knowledge for safety and oversight, even with IoT in the IVF laboratory.
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Affiliation(s)
- Giles A Palmer
- IVF2.0 Ltd, London, UK; International IVF Initiative, New York, New York, USA
| | | | | | - Gerardo Mendizabal-Ruiz
- Conceivable Life Sciences, New York, New York, USA; Departamento de Bioingeniería Traslacional, Universidad de Guadalajara, Guadalajara, Mexico
| | - Jacques Cohen
- IVF2.0 Ltd, London, UK; International IVF Initiative, New York, New York, USA; Althea Science Inc, New York, New York, USA; Conceivable Life Sciences, New York, New York, USA.
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26
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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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27
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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28
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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29
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Pons MC, Carrasco B, Rives N, Delgado A, Martínez-Moro A, Martínez-Granados L, Rodriguez I, Cairó O, Cuevas-Saiz I. Predicting the likelihood of live birth: an objective and user-friendly blastocyst grading system. Reprod Biomed Online 2023; 47:103243. [PMID: 37473718 DOI: 10.1016/j.rbmo.2023.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/10/2023] [Accepted: 05/31/2023] [Indexed: 07/22/2023]
Abstract
RESEARCH QUESTION Can day-5 blastocysts be ranked according to their likelihood of live birth using an objective and user-friendly grading system? DESIGN A retrospective multicentre study conducted between 2017 and 2019, including 1044 day-5 blastocysts. Blastocyst expansion degree, trophectoderm and inner cell mass quality were assessed morphologically and morphometrically. Several analyses were conducted: the association between the qualitative and quantitative assessment for the blastocyst expansion degree and the number of trophectoderm cells; the effect of the embryo quality on day 3 and the contribution of the three blastocyst parameters to live birth, with logistic regression; and a decision tree with the most significant variables to create the new scoring system. RESULTS Cut-off points were found to discriminate between expanding and expanded blastocysts (165 µm for blastocyst diameter) and between trophectoderm grades (A: ≥14 cells; B: 11-13 cells; C: ≤10 cells). When the embryos reached the blastocyst stage, their quality on day 3 did not add predictive value for implantation and live birth. In the logistic regression analysis, the only parameter capable of significantly predicting the live birth likelihood was the trophectoderm grade: A versus C (OR 1.95, 95% CI 1.26 to 3.0); B versus C (OR 1.71, 95% CI 1.22 to 2.4). The decision tree supported the finding that the trophectoderm grade had the highest predictive value for a live birth, followed by the blastocyst expansion degree in a second step. CONCLUSIONS This new method makes objective blastocyst assessment feasible, allowing for standardization and exportation to other laboratories worldwide.
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Affiliation(s)
- Maria Carme Pons
- Dexeus Mujer- Hospital Universitari Dexeus, Reproductive Medicine Service, Gran, Via Carles III, 71-75. 08028 Barcelona, Spain.
| | - Beatriz Carrasco
- Dexeus Mujer- Hospital Universitari Dexeus, Reproductive Medicine Service, Gran, Via Carles III, 71-75. 08028 Barcelona, Spain
| | - Natalia Rives
- Barcelona IVF, Escoles Pies, 103. 08017 Barcelona, Spain
| | - Arantza Delgado
- Institut Universitari IVI Valencia, Plaza Policía local, 3. 46015 Valencia, Spain
| | - Alvaro Martínez-Moro
- IVF Spain Madrid, Calle Manuel de Falla, 6-8. 28036 Madrid, Spain; Animal Reproduction Department, INIA-CSIC, Avda. Puerta del Hierro, 18. 28040, Madrid, Spain
| | - Luís Martínez-Granados
- Hospital Universitario Príncipe de Asturias, Unidad de Reproducción Humana, Carretera de Alcalá-Meco s/n. 28805 Alcalá de Henares, Spain
| | - Ignacio Rodriguez
- Dexeus Mujer- Hospital Universitari Dexeus, Reproductive Medicine Service, Gran, Via Carles III, 71-75. 08028 Barcelona, Spain
| | - Olga Cairó
- Centro de Infertilidad y Reproducción Humana (CIRH), Plaza Eguilaz, 14 bajos. 08017 Barcelona, Spain
| | - Irene Cuevas-Saiz
- Hospital General Universitario de Valencia, Unidad de Medicina Reproductiva, Avenida Tres Cruces, 2. 46014 Valencia, Spain
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30
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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: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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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 Sci 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Allahbadia GN, Allahbadia SG, Gupta A. In Contemporary Reproductive Medicine Human Beings are Not Yet Dispensable. J Obstet Gynaecol India 2023; 73:295-300. [PMID: 37701084 PMCID: PMC10492706 DOI: 10.1007/s13224-023-01747-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 03/04/2023] [Indexed: 04/05/2023] Open
Abstract
In the past few years almost every aspect of an IVF cycle has been investigated, including research on sperm, color doppler in follicular studies, prediction of embryo cleavage, prediction of blastocyst formation, scoring blastocyst quality, prediction of euploid blastocysts and live birth from blastocysts, improving the embryo selection process, and for developing deep machine learning (ML) algorithms for optimal IVF stimulation protocols. Also, artificial intelligence (AI)-based methods have been implemented for some clinical aspects of IVF, such as assessing patient reproductive potential and individualizing gonadotropin stimulation protocols. As AI has the inherent capacity to analyze "Big" data, the goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data to provide patient-tailored individualized treatments. Human skillsets including hand eye coordination to perform an embryo transfer is probably the only step of IVF that is outside the realm of AI & ML today. Embryo transfer success is presently human skill dependent and deep machine learning may one day intrude into this sacred space with the advent of programed humanoid robots. Embryo transfer is arguably the rate limiting step in the sequential events that complete an IVF cycle. Many variables play a role in the success of embryo transfer, including catheter type, atraumatic technique, and the use of sonography guidance before and during the procedure of embryo transfer. In contemporary Reproductive Medicine human beings are not yet dispensable.
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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 Glob Rep 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] [What about the content of this article? (0)] [Affiliation(s)] [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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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 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] [What about the content of this article? (0)] [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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Charnpinyo N, Suthicharoenpanich K, Onthuam K, Engphaiboon S, Chaichaowarat R, Suebthawinkul C, Siricharoen P. Embryo Selection for IVF using Machine Learning Techniques Based on Light Microscopic Images of Embryo and Additional Factors. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4. [PMID: 38082871 DOI: 10.1109/embc40787.2023.10340767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The current process of embryo selection in In Vitro Fertilization (IVF) process is based on morphological criteria, e.g., Istanbul scoring system and manually evaluated by embryologists; consequently, the assessment can be subjective. In the case of multiple embryos that have the same morphological grading, there is no guidance on how embryos should be prioritized to be transferred. This work aims to develop a deep learning-based model to classify viable and non-viable embryos using light microscopic images of an embryo. Additional features according to Istanbul grading system and the patients' age is also included in the model. Various models are evaluated and the best model based on the fusion of embryo images and additional features provides accuracy, sensitivity, and area under curve (AUC) of 65%, 74.29% and 0.72, respectively. The distributions of the prediction score corresponding to each additional feature are analysed and compared with pregnant and non-pregnant ground truths. We have found that the additional factors, such as age, embryo development stage, the quality of inner cell mass (ICM), and trophectoderm (TE) have a positive impact and enhanced the model prediction of implantation potential.
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36
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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37
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Theilgaard Lassen J, Fly Kragh M, Rimestad J, Nygård Johansen M, Berntsen J. Development and validation of deep learning based embryo selection across multiple days of transfer. Sci Rep 2023; 13:4235. [PMID: 36918648 PMCID: PMC10015019 DOI: 10.1038/s41598-023-31136-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 03/07/2023] [Indexed: 03/16/2023] Open
Abstract
This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of human embryos incubated for 2, 3, and 5 or more days. We trained and evaluated the model on an extensive and diverse dataset including 181,428 embryos from 22 IVF clinics across the world. To discriminate the transferred embryos with known outcome, we show areas under the receiver operating curve ranging from 0.621 to 0.707 depending on the day of transfer. Predictive performance increased over time and showed a strong correlation with morphokinetic parameters. The model's performance is equivalent to the KIDScore D3 model on day 3 embryos while it significantly surpasses the performance of KIDScore D5 v3 on day 5+ embryos. This model provides an analysis of time-lapse sequences without the need for user input, and provides a reliable method for ranking embryos for their likelihood of implantation, at both cleavage and blastocyst stages. This greatly improves embryo grading consistency and saves time compared to traditional embryo evaluation methods.
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Palay P, Fathi D, Fathi R. Oocyte quality evaluation: a review of engineering approaches toward clinical challenges. Biol Reprod 2023; 108:393-407. [PMID: 36495197 DOI: 10.1093/biolre/ioac219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/24/2022] [Accepted: 11/30/2022] [Indexed: 12/14/2022] Open
Abstract
Although assisted reproductive technology has been very successful for the treatment of infertility, its steps are still dependent on direct human opinion. An important step of assisted reproductive treatments in lab for women is choosing an oocyte that has a better quality. This step would predict which oocyte has developmental competence leading to healthy baby. Observation of the oocyte morphological quality indicators under microscope by an embryologist is the most common evaluation method of oocyte quality. Such subjective method which relies on embryologist's experience may vary and leads to misdiagnosis. An alternative solution to eliminate human misjudging in traditional methods and overcome the limitations of them is always using engineering-based procedure. In this review article, we deeply study and categorize engineering-based methods applied for the evaluation of oocyte quality. Then, the challenges in laboratories and clinics settings move forward with translational medicine perspective in mind for all those methods which had been studied were discussed. Finally, a standardized process was presented, which may help improving and focusing the research in this field. Moreover, effective suggestion techniques were introduced that are expected they would be complementary methods to accelerate future researches. The aim of this review was to create a new prospect with the engineering approaches to evaluate oocyte quality and we hope this would help infertile couples to get a baby.
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Affiliation(s)
- Peyman Palay
- Department of Electrical and Computer Engineering, Tarbiat Modares University (TMU), Tehran, Iran
| | - Davood Fathi
- Department of Electrical and Computer Engineering, Tarbiat Modares University (TMU), Tehran, Iran
| | - Rouhollah Fathi
- Department of Embryology, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, Academic Center for Education, Culture and Research (ACECR), Tehran, Iran
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Li M, Zhu X, Wang L, Fu H, Zhao W, Zhou C, Chen L, Yao B. Evaluation of endometrial receptivity by ultrasound elastography to predict pregnancy outcome is a non-invasive and worthwhile method. Biotechnol Genet Eng Rev 2023:1-15. [PMID: 36883689 DOI: 10.1080/02648725.2023.2183585] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 02/13/2023] [Indexed: 03/09/2023]
Abstract
Up to today, there is no effective, specific and non-invasive evaluation method to assess the endometrial receptivity. This study aimed to establish a non-invasive and effective model with the clinical indicators to evaluate endometrial receptivity. Ultrasound elastography can reflect the overall state of the endometrium. Ultrasonic elastography images from 78 hormonally prepared frozen embryo transfer (FET) patients were assessed in this study. Meanwhile, the clinical indicators reflecting endometrium in the transplantation cycle were collected. The patients were received to transfer only one high-quality blastocyst. A novel code rule that can generate a large number of 0-1 symbols was designed to collect data on different factors. At the same time, a logistic regression model of the machine learning process with an automatic combination of factors was designed for analysis. The logistic regression model was based on age, body mass index, waist-hip ratio, endometrial thickness, perfusion index (PI), resistance index (RI), elastic grade, elastic ratio cutoff value, serum estradiol level and 9 other indicators. The accuracy rate of predicting pregnancy outcome of the logistic regression model was 76.92%. Elastic ultrasound can reflect the endometrial receptivity of patients in FET cycles. We established a prediction model including ultrasound elastography and the model precisely predicted the pregnancy outcome. The predictive accuracy of endometrial receptivity by the predictive model is significantly higher than that of the single clinical indicator. The prediction model by integrating the clinical indicators to evaluate endometrial receptivity may be a non-invasive and worthwhile method for evaluating endometrial receptivity.
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Affiliation(s)
- Meiling Li
- Department of Reproductive Medicine, Affiliated Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, Jiangsu, China
| | - Xianjun Zhu
- Department of Reproductive Medicine, Affiliated Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, Jiangsu, China
- School of Software Engineering, Jinling Institute of Technology, Nanjing, Jiangsu, China
| | - Liping Wang
- School of Software Engineering, Jinling Institute of Technology, Nanjing, Jiangsu, China
- Department of Ultrasound Diagnosis, Nanjing Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu Province, China
| | - Haiyan Fu
- Department of Reproductive Medicine, Affiliated Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, Jiangsu, China
| | - Wei Zhao
- Department of Reproductive Medicine, Affiliated Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, Jiangsu, China
| | - Chen Zhou
- Department of Reproductive Medicine, Affiliated Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, Jiangsu, China
| | - Li Chen
- Department of Reproductive Medicine, Affiliated Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, Jiangsu, China
| | - Bing Yao
- Department of Reproductive Medicine, Affiliated Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, Jiangsu, China
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Cheredath A, Uppangala S, C S A, Jijo A, R VL, Kumar P, Joseph D, G A NG, Kalthur G, Adiga SK. Combining Machine Learning with Metabolomic and Embryologic Data Improves Embryo Implantation Prediction. Reprod Sci 2023; 30:984-94. [PMID: 36097248 DOI: 10.1007/s43032-022-01071-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/23/2022] [Indexed: 10/14/2022]
Abstract
This study investigated whether combining metabolomic and embryologic data with machine learning (ML) models improve the prediction of embryo implantation potential. In this prospective cohort study, infertile couples (n=56) undergoing day-5 single blastocyst transfer between February 2019 and August 2021 were included. After day-5 single blastocyst transfer, spent culture medium (SCM) was subjected to metabolite analysis using nuclear magnetic resonance (NMR) spectroscopy. Derived metabolite levels and embryologic parameters between successfully implanted and failed groups were incorporated into ML models to explore their predictive potential regarding embryo implantation. The SCM of blastocysts that resulted in successful embryo implantation had significantly lower pyruvate (p<0.05) and threonine (p<0.05) levels compared to medium control but not compared to SCM related to embryos that failed to implant. Notably, the prediction accuracy increased when classical ML algorithms were combined with metabolomic and embryologic data. Specifically, the custom artificial neural network (ANN) model with regularized parameters for metabolomic data provided 100% accuracy, indicating the efficiency in predicting implantation potential. Hence, combining ML models (specifically, custom ANN) with metabolomic and embryologic data improves the prediction of embryo implantation potential. The approach could potentially be used to derive clinical benefits for patients in real-time.
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Duval A, Nogueira D, Dissler N, Maskani Filali M, Delestro Matos F, Chansel-Debordeaux L, Ferrer-Buitrago M, Ferrer E, Antequera V, Ruiz-Jorro M, Papaxanthos A, Ouchchane H, Keppi B, Prima PY, Regnier-Vigouroux G, Trebesses L, Geoffroy-Siraudin C, Zaragoza S, Scalici E, Sanguinet P, Cassagnard N, Ozanon C, De La Fuente A, Gómez E, Gervoise Boyer M, Boyer P, Ricciarelli E, Pollet-Villard X, Boussommier-Calleja A. A hybrid artificial intelligence model leverages multi-centric clinical data to improve fetal heart rate pregnancy prediction across time-lapse systems. Hum Reprod 2023; 38:596-608. [PMID: 36763673 PMCID: PMC10068266 DOI: 10.1093/humrep/dead023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 01/10/2023] [Indexed: 02/12/2023] Open
Abstract
STUDY QUESTION Can artificial intelligence (AI) algorithms developed to assist embryologists in evaluating embryo morphokinetics be enriched with multi-centric clinical data to better predict clinical pregnancy outcome? SUMMARY ANSWER Training algorithms on multi-centric clinical data significantly increased AUC compared to algorithms that only analyzed the time-lapse system (TLS) videos. WHAT IS KNOWN ALREADY Several AI-based algorithms have been developed to predict pregnancy, most of them based only on analysis of the time-lapse recording of embryo development. It remains unclear, however, whether considering numerous clinical features can improve the predictive performances of time-lapse based embryo evaluation. STUDY DESIGN, SIZE, DURATION A dataset of 9986 embryos (95.60% known clinical pregnancy outcome, 32.47% frozen transfers) from 5226 patients from 14 European fertility centers (in two countries) recorded with three different TLS was used to train and validate the algorithms. A total of 31 clinical factors were collected. A separate test set (447 videos) was used to compare performances between embryologists and the algorithm. PARTICIPANTS/MATERIALS, SETTING, METHODS Clinical pregnancy (defined as a pregnancy leading to a fetal heartbeat) outcome was first predicted using a 3D convolutional neural network that analyzed videos of the embryonic development up to 2 or 3 days of development (33% of the database) or up to 5 or 6 days of development (67% of the database). The output video score was then fed as input alongside clinical features to a gradient boosting algorithm that generated a second score corresponding to the hybrid model. AUC was computed across 7-fold of the validation dataset for both models. These predictions were compared to those of 13 senior embryologists made on the test dataset. MAIN RESULTS AND THE ROLE OF CHANCE The average AUC of the hybrid model across all 7-fold was significantly higher than that of the video model (0.727 versus 0.684, respectively, P = 0.015; Wilcoxon test). A SHapley Additive exPlanations (SHAP) analysis of the hybrid model showed that the six first most important features to predict pregnancy were morphokinetics of the embryo (video score), oocyte age, total gonadotrophin dose intake, number of embryos generated, number of oocytes retrieved, and endometrium thickness. The hybrid model was shown to be superior to embryologists with respect to different metrics, including the balanced accuracy (P ≤ 0.003; Wilcoxon test). The likelihood of pregnancy was linearly linked to the hybrid score, with increasing odds ratio (maximum P-value = 0.001), demonstrating the ranking capacity of the model. Training individual hybrid models did not improve predictive performance. A clinic hold-out experiment was conducted and resulted in AUCs ranging between 0.63 and 0.73. Performance of the hybrid model did not vary between TLS or between subgroups of embryos transferred at different days of embryonic development. The hybrid model did fare better for patients older than 35 years (P < 0.001; Mann-Whitney test), and for fresh transfers (P < 0.001; Mann-Whitney test). LIMITATIONS, REASONS FOR CAUTION Participant centers were located in two countries, thus limiting the generalization of our conclusion to wider subpopulations of patients. Not all clinical features were available for all embryos, thus limiting the performances of the hybrid model in some instances. WIDER IMPLICATIONS OF THE FINDINGS Our study suggests that considering clinical data improves pregnancy predictive performances and that there is no need to retrain algorithms at the clinic level unless they follow strikingly different practices. This study characterizes a versatile AI algorithm with similar performance on different time-lapse microscopes and on embryos transferred at different development stages. It can also help with patients of different ages and protocols used but with varying performances, presumably because the task of predicting fetal heartbeat becomes more or less hard depending on the clinical context. This AI model can be made widely available and can help embryologists in a wide range of clinical scenarios to standardize their practices. STUDY FUNDING/COMPETING INTEREST(S) Funding for the study was provided by ImVitro with grant funding received in part from BPIFrance (Bourse French Tech Emergence (DOS0106572/00), Paris Innovation Amorçage (DOS0132841/00), and Aide au Développement DeepTech (DOS0152872/00)). A.B.-C. is a co-owner of, and holds stocks in, ImVitro SAS. A.B.-C. and F.D.M. hold a patent for 'Devices and processes for machine learning prediction of in vitro fertilization' (EP20305914.2). A.D., N.D., M.M.F., and F.D.M. are or have been employees of ImVitro and have been granted stock options. X.P.-V. has been paid as a consultant to ImVitro and has been granted stocks options of ImVitro. L.C.-D. and C.G.-S. have undertaken paid consultancy for ImVitro SAS. The remaining authors have no conflicts to declare. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
| | - D Nogueira
- INOVIE Fertilité, Institut de Fertilité La Croix Du Sud, Toulouse, France
- Art Fertility Clinics, IVF laboratory, Abu Dhabi, United Arab Emirate
| | | | | | | | - L Chansel-Debordeaux
- Service de la biologie et de la reproduction et CECOS, CHU Bordeaux Groupe Hospitalier Pellegrin, Bordeaux, France
| | - M Ferrer-Buitrago
- Crea Centro Médico de Fertilidad y Reproducción Asistida, Valencia, Spain
| | - E Ferrer
- Crea Centro Médico de Fertilidad y Reproducción Asistida, Valencia, Spain
| | - V Antequera
- Crea Centro Médico de Fertilidad y Reproducción Asistida, Valencia, Spain
| | - M Ruiz-Jorro
- Crea Centro Médico de Fertilidad y Reproducción Asistida, Valencia, Spain
| | - A Papaxanthos
- Service de la biologie et de la reproduction et CECOS, CHU Bordeaux Groupe Hospitalier Pellegrin, Bordeaux, France
| | - H Ouchchane
- INOVIE Fertilité, Gen-Bio, Clermont-Ferrand, France
| | - B Keppi
- INOVIE Fertilité, Gen-Bio, Clermont-Ferrand, France
| | - P-Y Prima
- Laboratoire FIV PMAtlantique - Clinique Santé Atlantique, Nantes, France
| | | | | | - C Geoffroy-Siraudin
- Hopital Saint Joseph, Service Médicine et Biologie de la Reproduction, Marseille, France
| | - S Zaragoza
- INOVIE Fertilité, Bioaxiome, Avignon, France
| | - E Scalici
- INOVIE Fertilité, Bioaxiome, Avignon, France
| | - P Sanguinet
- INOVIE Fertilité, LaboSud, Montpellier, France
| | - N Cassagnard
- INOVIE Fertilité, Institut de Fertilité La Croix Du Sud, Toulouse, France
| | - C Ozanon
- Clinique Hôtel Privé Natecia, Centre Assistance Médicale à la Procréation, Lyon, France
| | | | - E Gómez
- Next Fertility, Murcia, Spain
| | - M Gervoise Boyer
- Hopital Saint Joseph, Service Médicine et Biologie de la Reproduction, Marseille, France
| | - P Boyer
- Hopital Saint Joseph, Service Médicine et Biologie de la Reproduction, Marseille, France
| | | | - X Pollet-Villard
- Nataliance, Centre Assistance Médicale à la Procréation, Saran, France
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Yuan Z, Yuan M, Song X, Huang X, Yan W. Development of an artificial intelligence based model for predicting the euploidy of blastocysts in PGT-A treatments. Sci Rep 2023; 13:2322. [PMID: 36759639 DOI: 10.1038/s41598-023-29319-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
The euploidy of embryos is unpredictable before transfer in in vitro fertilisation (IVF) treatments without pre-implantation genetic testing (PGT). Previous studies have suggested that morphokinetic characteristics using an artificial intelligence (AI)-based model in the time-lapse monitoring (TLM) system were correlated with the outcomes of frozen embryo transfer (FET), but the predictive effectiveness of the model for euploidy remains to be perfected. In this study, we combined morphokinetic characteristics, morphological characteristics of blastocysts, and clinical parameters of patients to build a model to predict the euploidy of blastocysts and live births in PGT for aneuploidy treatments. The model was effective in predicting euploidy (AUC = 0.879) but was ineffective in predicting live birth after FET. These results provide a potential method for the selection of embryos for IVF treatments with non-PGT.
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Jiang Y, Jiang R, He H, Ren X, Yu Q, Jin L. Comparison of clinical outcomes for different morphological scores of D5 and D6 blastocysts in the frozen-thawed cycle. BMC Pregnancy Childbirth 2023; 23:97. [PMID: 36747146 PMCID: PMC9900991 DOI: 10.1186/s12884-023-05415-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 01/30/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Both embryo development speed and embryo morphology score played a significant role in frozen-thawed embryo transfer cycle (FET) outcomes. Most of the literature indicates that D5 embryos performed better than D6 embryos, although a few also indicate that there is no difference in clinical outcomes between D5 and D6 embryos. Clinically, D5 embryos are preferred for equal morphological scores. But how to choose embryos when the morphological score of D6 embryos is better than D5? METHODS A retrospective study including 8199 frozen-thawed embryo transfers (FETs) was conducted to analyze patients who underwent IVF-FET from January 2018 to December 2020. Patients were divided into 8 groups according to the rate of embryonic development and morphological scores to compare pregnancy outcomes. We further compared clinical pregnancy outcomes and neonatal outcomes between BC embryos on day 5 (D5) and BA/BB embryos on day 6 (D6). RESULTS Our study found no difference in clinical pregnancy rate (CPR) and live birth rate (LBR) between AA/AB blastocysts in D5 or D6 frozen blastocysts. However, for BA/BB/BC blastocysts, embryonic pregnancy outcome was significantly better in D5 than in D6. In our further analysis and comparison of BC embryos in D5 and BA/BB embryos in D6, we found no difference in clinical pregnancy outcomes and neonatal outcomes, but D6 BA/BB embryos had a higher rate of miscarriage. After adjusting for confounding factors, none of the indicators differed between groups. CONCLUSION Our study provides suggestions for embryo selection: AA/AB embryos are preferred, regardless of the embryo development day, and the second choice is BA or BB embryos on D5. BA/BB embryos in D6 had a higher miscarriage rate than BC embryos in D5 but were not statistically significant after adjusting for confounding factors.
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Affiliation(s)
- Yaping Jiang
- grid.33199.310000 0004 0368 7223Department of Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei PR China
| | - Rui Jiang
- grid.33199.310000 0004 0368 7223Laboratory of Clinical Immunology, Wuhan No. 1 Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei PR China
| | - Hui He
- grid.33199.310000 0004 0368 7223Department of Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei PR China
| | - Xinling Ren
- grid.33199.310000 0004 0368 7223Department of Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei PR China
| | - Qiong Yu
- Department of Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.
| | - Lei Jin
- Department of Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.
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Amitai T, Kan-Tor Y, Or Y, Shoham Z, Shofaro Y, Richter D, Har-Vardi I, Ben-Meir A, Srebnik N, Buxboim A. Embryo classification beyond pregnancy: early prediction of first trimester miscarriage using machine learning. J Assist Reprod Genet 2023; 40:309-322. [PMID: 36194342 PMCID: PMC9935804 DOI: 10.1007/s10815-022-02619-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 09/09/2022] [Indexed: 11/27/2022] Open
Abstract
PURPOSE First trimester miscarriage is a major concern in IVF-ET treatments, accounting for one out of nine clinical pregnancies and for up to one out of three recognized pregnancies. To develop a machine learning classifier for predicting the risk of cleavage-stage embryos to undergo first trimester miscarriage based on time-lapse images of preimplantation development. METHODS Retrospective study of a 4-year multi-center cohort of 391 women undergoing intra-cytoplasmatic sperm injection (ICSI) and fresh single or double embryo transfers. The study included embryos with positive indication of clinical implantation based on gestational sac visualization either with first trimester miscarriage or live-birth outcome. Miscarriage was determined based on negative fetal heartbeat indication during the first trimester. Data were recorded and obtained in hospital setting and research was performed in university setting. RESULTS A minimal subset of six non-redundant morphodynamic features were screened that maintained high prediction capacity. Features that account for the distribution of the nucleolus precursor bodies within the small pronucleus and pronuclei dynamics were highly predictive of miscarriage outcome as evaluated using the SHapley Additive exPlanations (SHAP) methodology. Using this feature subset, XGBoost and random forest models were trained following a 100-fold Monte-Carlo cross validation scheme. Miscarriage was predicted with AUC 0.68 to 0.69. CONCLUSION We report the development of a decision-support tool for identifying the embryos with high risk of miscarriage. Prioritizing embryos for transfer based on their predicted risk of miscarriage in combination with their predicted implantation potential is expected to improve live-birth rates and shorten time-to-pregnancy.
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Affiliation(s)
- Tamar Amitai
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, 9190416, Israel
| | - Yoav Kan-Tor
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, 9190416, Israel
- The Center for Interdisciplinary Data Science Research, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem, 9190401, 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 Shofaro
- Infertility and IVF Unit, Rabin Medical Center, Helen Schneider Hospital for Women, , 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, Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel
- In Vitro Fertilization Unit, Department of Obstetrics and Gynecology, Shaare Zedek Medical Center, Jerusalem, 9103102, Israel
| | - Amnon Buxboim
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, 9190416, Israel.
- The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel.
- The Alexender Grass Center for Bioengineering, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem, 9190401, Israel.
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Eastick J, Venetis C, Cooke S, Chapman M. Detailed analysis of cytoplasmic strings in human blastocysts: new insights. ZYGOTE 2023; 31:78-84. [PMID: 36384982 DOI: 10.1017/S0967199422000570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The aim of this study was to determine if there was an association between the presence of cytoplasmic strings (CS) and their characteristics, with blastocyst quality, development and clinical outcome in human blastocysts. This two-centre cohort study was performed between July 2017 and September 2018 and involved a total of 1152 blastocysts from 225 patients undergoing in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI). All embryos were cultured in Embryoscope+ and were assessed for CS using time-lapse images. A single assessor examined all blastocysts and reviewed videos using the EmbyroViewer® Software. Blastocyst quality was assessed on day 5 of embryo development. The number of CS, location and duration of their activity was recorded on days 5/6. A positive association between the presence of CS in human blastocysts with blastocyst quality was identified. Blastocysts with a higher number of CS present, were of higher quality and were in the more advanced stages of development. Top quality blastocysts had CS activity present for longer, as well as having a higher number of vesicles present travelling along the CS. Blastocysts that had CS present, had a significantly higher live birth rate. This study has confirmed that a higher number of CS and vesicles in human blastocysts is associated with top quality blastocysts and is not a negative predictor of development. They had a higher number of CS present that appeared earlier in development and, although ceased activity sooner, had a longer duration of activity. Blastocysts with CS had a significant increase in live birth rate.
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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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Buldo-Licciardi J, Large MJ, McCulloh DH, McCaffrey C, Grifo JA. 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; 40:289-99. [PMID: 36609941 DOI: 10.1007/s10815-022-02695-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [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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Farias AF, Chavez-Badiola A, Mendizabal-Ruiz G, Valencia-Murillo R, Drakeley A, Cohen J, Cardenas-Esparza E. Automated identification of blastocyst regions at different development stages. Sci Rep 2023; 13:15. [PMID: 36593239 DOI: 10.1038/s41598-022-26386-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [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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Wu C, Fu L, Tian Z, Liu J, Song J, Guo W, Zhao Y, Zheng D, Jin Y, Yi D, Jiang X. LWMA-Net: Light-weighted morphology attention learning for human embryo grading. Comput Biol Med 2022; 151:106242. [PMID: 36436483 DOI: 10.1016/j.compbiomed.2022.106242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/23/2022] [Accepted: 10/22/2022] [Indexed: 11/16/2022]
Abstract
Visual inspection of embryo morphology is routinely used in embryo assessment and selection. However, due to the complexity of morphologies and large inter- and intra-observer variances among embryologists, manual evaluations remain to be subjective and time-consuming. Thus, we proposed a light-weighted morphology attention learning network (LWMA-Net) for automatic assistance on embryo grading. The LWMA-Net integrated a morphology attention module (MAM) to seek the informative features and their locations and a multiscale fusion module (MFM) to increase the features flowing in the model. The LWMA-Net was trained with a primary set of 3599 embryos from 2318 couples that were clinically enrolled between Sep. 2016 and Dec. 2018, and generated area under the receiver operating characteristic curves (AUCs) of 96.88% and 97.58% on 4- and 3-category gradings, respectively. An independent test set comprises 691 embryos from 321 couples between Jan. 2019 and Jan. 2021 were used to test the assisted fertility values on the embryo grading. Five experienced embryologists were invited to regrade the embryos in the independent set with and without the aid of the LWMA-Net three months apart. Embryologists aided by our LWMA-Net significantly improved their grading capabilities with average AUCs improved by 4.98%-5.32% on 4- and 3-category grading tasks, respectively, which suggests good potential of our LWMA-Net on assisted human reproduction.
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Affiliation(s)
- Chongwei Wu
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, China
| | - Langyuan Fu
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, China
| | - Zhiying Tian
- Key Laboratory of Reproductive Health and Medical Genetics, National Health and Family Planning Commission, Liaoning Research Institute of Family Planning, Shenyang, 110031, China
| | - Jiao Liu
- Department of Reproductive Medicine, Dalian Municipal Women and Children's Medical Center (Group), Dalian, 116083, China
| | - Jiangdian Song
- School of Medical Informatics, China Medical University, Shenyang, 110122, China
| | - Wei Guo
- College of Computer Science, Shenyang Aerospace University, Shenyang, 110136, China
| | - Yu Zhao
- Department of Reproductive Medicine, Dalian Municipal Women and Children's Medical Center (Group), Dalian, 116083, China
| | - Duo Zheng
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, China
| | - Ying Jin
- Key Laboratory of Reproductive Health and Medical Genetics, National Health and Family Planning Commission, Liaoning Research Institute of Family Planning, Shenyang, 110031, China
| | - Dongxu Yi
- Key Laboratory of Reproductive Health and Medical Genetics, National Health and Family Planning Commission, Liaoning Research Institute of Family Planning, Shenyang, 110031, China
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, China.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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