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Ten J, Herrero L, Linares Á, Álvarez E, Ortiz JA, Bernabeu A, Bernabéu R. Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights. Reprod Biol Endocrinol 2024; 22:116. [PMID: 39261843 PMCID: PMC11389240 DOI: 10.1186/s12958-024-01285-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 08/23/2024] [Indexed: 09/13/2024] Open
Abstract
BACKGROUND Data sciences and artificial intelligence are becoming encouraging tools in assisted reproduction, favored by time-lapse technology incubators. Our objective is to analyze, compare and identify the most predictive machine learning algorithm developed using a known implantation database of embryos transferred in our egg donation program, including morphokinetic and morphological variables, and recognize the most predictive embryo parameters in order to enhance IVF treatments clinical outcomes. METHODS Multicenter retrospective cohort study carried out in 378 egg donor recipients who performed a fresh single embryo transfer during 2021. All treatments were performed by Intracytoplasmic Sperm Injection, using fresh or frozen oocytes. The embryos were cultured in Geri® time-lapse incubators until transfer on day 5. The embryonic morphokinetic events of 378 blastocysts with known implantation and live birth were analyzed. Classical statistical analysis (binary logistic regression) and 10 machine learning algorithms were applied including Multi-Layer Perceptron, Support Vector Machines, k-Nearest Neighbor, Cart and C0.5 Classification Trees, Random Forest (RF), AdaBoost Classification Trees, Stochastic Gradient boost, Bagged CART and eXtrem Gradient Boosting. These algorithms were developed and optimized by maximizing the area under the curve. RESULTS The Random Forest emerged as the most predictive algorithm for implantation (area under the curve, AUC = 0.725, IC 95% [0.6232-0826]). Overall, implantation and miscarriage rates stood at 56.08% and 18.39%, respectively. Overall live birth rate was 41.26%. Significant disparities were observed regarding time to hatching out of the zona pellucida (p = 0.039). The Random Forest algorithm demonstrated good predictive capabilities for live birth (AUC = 0.689, IC 95% [0.5821-0.7921]), but the AdaBoost classification trees proved to be the most predictive model for live birth (AUC = 0.749, IC 95% [0.6522-0.8452]). Other important variables with substantial predictive weight for implantation and live birth were duration of visible pronuclei (DESAPPN-APPN), synchronization of cleavage patterns (T8-T5), duration of compaction (TM-TiCOM), duration of compaction until first sign of cavitation (TiCAV-TM) and time to early compaction (TiCOM). CONCLUSIONS This study highlights Random Forest and AdaBoost as the most effective machine learning models in our Known Implantation and Live Birth Database from our egg donation program. Notably, time to blastocyst hatching out of the zona pellucida emerged as a highly reliable parameter significantly influencing our implantation machine learning predictive models. Processes involving syngamy, genomic imprinting during embryo cleavage, and embryo compaction are also influential and could be crucial for implantation and live birth outcomes.
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Affiliation(s)
- Jorge Ten
- Instituto Bernabéu Alicante, Avda. Albufereta, 31, 03016, Alicante, Spain.
| | | | - Ángel Linares
- Instituto Bernabéu Alicante, Avda. Albufereta, 31, 03016, Alicante, Spain
| | | | - José Antonio Ortiz
- Molecular Biology and Genetics, Instituto Bernabéu Biotech, Alicante, Spain
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Mikkola M, Desmet KLJ, Kommisrud E, Riegler MA. Recent advancements to increase success in assisted reproductive technologies in cattle. Anim Reprod 2024; 21:e20240031. [PMID: 39176005 PMCID: PMC11340803 DOI: 10.1590/1984-3143-ar2024-0031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 06/14/2024] [Indexed: 08/24/2024] Open
Abstract
Assisted reproductive technologies (ART) are fundamental for cattle breeding and sustainable food production. Together with genomic selection, these technologies contribute to reducing the generation interval and accelerating genetic progress. In this paper, we discuss advancements in technologies used in the fertility evaluation of breeding animals, and the collection, processing, and preservation of the gametes. It is of utmost importance for the breeding industry to select dams and sires of the next generation as young as possible, as is the efficient and timely collection of gametes. There is a need for reliable and easily applicable methods to evaluate sexual maturity and fertility. Although gametes processing and preservation have been improved in recent decades, challenges are still encountered. The targeted use of sexed semen and beef semen has obliterated the production of surplus replacement heifers and bull calves from dairy breeds, markedly improving animal welfare and ethical considerations in production practices. Parallel with new technologies, many well-established technologies remain relevant, although with evolving applications. In vitro production (IVP) has become the predominant method of embryo production. Although fundamental improvements in IVP procedures have been established, the quality of IVP embryos remains inferior to their in vivo counterparts. Improvements to facilitate oocyte maturation and development of new culture systems, e.g. microfluidics, are presented in this paper. New non-invasive and objective tools are needed to select embryos for transfer. Cryopreservation of semen and embryos plays a pivotal role in the distribution of genetics, and we discuss the challenges and opportunities in this field. Finally, machine learning (ML) is gaining ground in agriculture and ART. This paper delves into the utilization of emerging technologies in ART, along with the current status, key challenges, and future prospects of ML in both research and practical applications within ART.
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Affiliation(s)
| | | | - Elisabeth Kommisrud
- CRESCO, Centre for Embryology and Healthy Development, Department of Biotechnology, Inland Norway University of Applied Sciences, Hamar, Norway
| | - Michael A. Riegler
- Holistic Systems Department, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
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AlSaad R, Abd-Alrazaq A, Choucair F, Ahmed A, Aziz S, Sheikh J. Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review. J Med Internet Res 2024; 26:e53396. [PMID: 38967964 PMCID: PMC11259766 DOI: 10.2196/53396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 04/08/2024] [Accepted: 05/22/2024] [Indexed: 07/06/2024] Open
Abstract
BACKGROUND In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient's response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation. OBJECTIVE The objective of this review is to comprehensively examine the literature to explore the characteristics of AI models used for predicting ovarian stimulation outcomes in the context of IVF. METHODS A total of 6 electronic databases were searched for peer-reviewed literature published before August 2023, using the concepts of IVF and AI, along with their related terms. Records were independently screened by 2 reviewers against the eligibility criteria. The extracted data were then consolidated and presented through narrative synthesis. RESULTS Upon reviewing 1348 articles, 30 met the predetermined inclusion criteria. The literature primarily focused on the number of oocytes retrieved as the main predicted outcome. Microscopy images stood out as the primary ground truth reference. The reviewed studies also highlighted that the most frequently adopted stimulation protocol was the gonadotropin-releasing hormone (GnRH) antagonist. In terms of using trigger medication, human chorionic gonadotropin (hCG) was the most commonly selected option. Among the machine learning techniques, the favored choice was the support vector machine. As for the validation of AI algorithms, the hold-out cross-validation method was the most prevalent. The area under the curve was highlighted as the primary evaluation metric. The literature exhibited a wide variation in the number of features used for AI algorithm development, ranging from 2 to 28,054 features. Data were mostly sourced from patient demographics, followed by laboratory data, specifically hormonal levels. Notably, the vast majority of studies were restricted to a single infertility clinic and exclusively relied on nonpublic data sets. CONCLUSIONS These insights highlight an urgent need to diversify data sources and explore varied AI techniques for improved prediction accuracy and generalizability of AI models for the prediction of ovarian stimulation outcomes. Future research should prioritize multiclinic collaborations and consider leveraging public data sets, aiming for more precise AI-driven predictions that ultimately boost patient care and IVF success rates.
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Affiliation(s)
- Rawan AlSaad
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Fadi Choucair
- Reproductive Medicine Unit, Sidra Medicine, Doha, Qatar
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Javaid Sheikh
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
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Yang L, Leynes C, Pawelka A, Lorenzo I, Chou A, Lee B, Heaney JD. Machine learning in time-lapse imaging to differentiate embryos from young vs old mice†. Biol Reprod 2024; 110:1115-1124. [PMID: 38685607 PMCID: PMC11180621 DOI: 10.1093/biolre/ioae056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 02/15/2024] [Accepted: 04/05/2024] [Indexed: 05/02/2024] Open
Abstract
Time-lapse microscopy for embryos is a non-invasive technology used to characterize early embryo development. This study employs time-lapse microscopy and machine learning to elucidate changes in embryonic growth kinetics with maternal aging. We analyzed morphokinetic parameters of embryos from young and aged C57BL6/NJ mice via continuous imaging. Our findings show that aged embryos accelerated through cleavage stages (from 5-cells) to morula compared to younger counterparts, with no significant differences observed in later stages of blastulation. Unsupervised machine learning identified two distinct clusters comprising of embryos from aged or young donors. Moreover, in supervised learning, the extreme gradient boosting algorithm successfully predicted the age-related phenotype with 0.78 accuracy, 0.81 precision, and 0.83 recall following hyperparameter tuning. These results highlight two main scientific insights: maternal aging affects embryonic development pace, and artificial intelligence can differentiate between embryos from aged and young maternal mice by a non-invasive approach. Thus, machine learning can be used to identify morphokinetics phenotypes for further studies. This study has potential for future applications in selecting human embryos for embryo transfer, without or in complement with preimplantation genetic testing.
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Affiliation(s)
- Liubin Yang
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, Texas, USA
- Division of Reproductive Endocrinology and Infertility, Division of Reproductive Sciences, Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Carolina Leynes
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Ashley Pawelka
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Isabel Lorenzo
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Andrew Chou
- Pain Research, Informatics, Multi-morbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, USA
- Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Brendan Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Jason D Heaney
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
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Kitaya K, Yasuo T, Yamaguchi T, Morita Y, Hamazaki A, Murayama S, Mihara T, Mihara M. Construction of deep learning-based convolutional neural network model for automatic detection of fluid hysteroscopic endometrial micropolyps in infertile women with chronic endometritis. Eur J Obstet Gynecol Reprod Biol 2024; 297:249-253. [PMID: 38703449 DOI: 10.1016/j.ejogrb.2024.04.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/19/2024] [Accepted: 04/20/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVE(S) Chronic endometritis (CE) is a localized mucosal inflammatory disorder associated with female infertility of unknown etiology, endometriosis, tubal factors, repeated implantation failure, and recurrent pregnancy loss, along with atypical uterine bleeding and iron deficiency anemia. Diagnosis of CE has traditionally relied on endometrial biopsy and detection of CD138(+) endometrial stromal plasmacytes. To develop a less invasive diagnostic system for CE, we aimed to construct a deep learning-based convolutional neural network (CNN) model for the automatic detection of endometrial micropolyps (EMiP), a fluid hysteroscopy (F-HSC) finding recognized as tiny protrusive lesions that are closely related to this disease. STUDY DESIGN This is an in silico study using archival images of F-HSC performed at an infertility center in a private clinic. A total of 244 infertile women undergoing F-HSC on the days 6-12 of the menstrual cycle between April 2019 and December 2021 with histopathologically-confirmed CE with the aid of immunohistochemistry for CD138 were utilized. RESULTS The archival F-HSC images of 208 women (78 with EMiP and 130 without EMiP) who met the inclusion criteria were finally subjected to analysis. Following preprocessing of the images, half a set was input into a CNN architecture for training, whereas the remaining images were utilized as the test set to evaluate the performance of the model, which was compared with that of the experienced gynecologists. The sensitivity, specificity, accuracy, precision, and F1-score of the CNN model-aided diagnosis were 93.6 %, 92.3 %, 92.8 %, 88.0 %, and 0.907, respectively. The area under the receiver operating characteristic curves of the CNN model-aided diagnosis (0.930) was at a similar level (p > .05) to the value of conventional diagnosis by three experienced gynecologists (0.927, 0.948, and 0.906). CONCLUSION These findings indicate that our deep learning-based CNN is capable of recognizing EMiP in F-HSC images and holds promise for further development of the computer-aided diagnostic system for CE.
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Affiliation(s)
- Kotaro Kitaya
- Infertility Center, Iryouhoujin Kouseikai Mihara Hospital. 6-8 Kamikatsura Miyanogo-cho, Nishikyo-ku, Kyoto 615-8227, Japan; Iryouhoujin Kouseikai Katsura-ekimae Mihara Clinic. 103 Katsura OS Plaza Building, 133 Katsura Minamitatsumi-cho, Nishikyo-ku, Kyoto 615-8074, Japan.
| | - Tadahiro Yasuo
- Department of Obstetrics and Gynecology, Otsu City Hospital. 2-9-9 Motomiya, Otsu 520-0804, Japan
| | - Takeshi Yamaguchi
- Infertility Center, Daigo Watanabe Clinic. 30-15 Daigo Takahata-cho, Fushimi-ku, Kyoto 601-1375, Japan
| | - Yuko Morita
- Infertility Center, Iryouhoujin Kouseikai Mihara Hospital. 6-8 Kamikatsura Miyanogo-cho, Nishikyo-ku, Kyoto 615-8227, Japan
| | - Atsumi Hamazaki
- Infertility Center, Iryouhoujin Kouseikai Mihara Hospital. 6-8 Kamikatsura Miyanogo-cho, Nishikyo-ku, Kyoto 615-8227, Japan
| | - Shinji Murayama
- Infertility Center, Iryouhoujin Kouseikai Mihara Hospital. 6-8 Kamikatsura Miyanogo-cho, Nishikyo-ku, Kyoto 615-8227, Japan
| | - Takako Mihara
- Infertility Center, Iryouhoujin Kouseikai Mihara Hospital. 6-8 Kamikatsura Miyanogo-cho, Nishikyo-ku, Kyoto 615-8227, Japan; Iryouhoujin Kouseikai Katsura-ekimae Mihara Clinic. 103 Katsura OS Plaza Building, 133 Katsura Minamitatsumi-cho, Nishikyo-ku, Kyoto 615-8074, Japan
| | - Masaya Mihara
- Infertility Center, Iryouhoujin Kouseikai Mihara Hospital. 6-8 Kamikatsura Miyanogo-cho, Nishikyo-ku, Kyoto 615-8227, Japan; Iryouhoujin Kouseikai Katsura-ekimae Mihara Clinic. 103 Katsura OS Plaza Building, 133 Katsura Minamitatsumi-cho, Nishikyo-ku, Kyoto 615-8074, Japan
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Fjeldstad J, Qi W, Mercuri N, Siddique N, Meriano J, Krivoi A, Nayot D. An artificial intelligence tool predicts blastocyst development from static images of fresh mature oocytes. Reprod Biomed Online 2024; 48:103842. [PMID: 38552566 DOI: 10.1016/j.rbmo.2024.103842] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/04/2024] [Accepted: 01/10/2024] [Indexed: 06/11/2024]
Abstract
RESEARCH QUESTION Can a deep learning image analysis model be developed to assess oocyte quality by predicting blastocyst development from images of denuded mature oocytes? DESIGN A deep learning model was developed utilizing 37,133 static oocyte images with associated laboratory outcomes from eight fertility clinics (six countries). A subset of data (n = 7807) was allocated to test model performance. External model validation was conducted to assess generalizability and robustness on new data (n = 12,357) from two fertility clinics (two countries). Performance was assessed by calculating area under the curve (AUC), balanced accuracy, specificity and sensitivity. Subgroup analyses were performed on the test dataset for age group, male factor and geographical location of the clinic. Model probabilities of the external dataset were converted to a 0-10 scoring scale to facilitate analysis of correlation with blastocyst development and quality. RESULTS The deep learning model demonstrated AUC of 0.64, balanced accuracy of 0.60, specificity of 0.55 and sensitivity of 0.65 on the test dataset. Subgroup analyses displayed the highest performance for age group 38-39 years (AUC 0.68), a negligible impact of male factor, and good model generalizability across geographical locations. Model performance was confirmed on external data: AUC of 0.63, balanced accuracy of 0.58, specificity of 0.57 and sensitivity of 0.59. Analysis of the scoring scale revealed that higher scoring oocytes correlated with higher likelihood of blastocyst development and good-quality blastocyst formation. CONCLUSION The deep learning model showed a favourable performance for the evaluation of oocytes in terms of competence to develop into a blastocyst, and when the predictions were converted into scores, they correlated with blastocyst quality. This represents a significant first step in oocyte evaluation for scientific and clinical applications.
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Affiliation(s)
| | - Weikai Qi
- Future Fertility, Toronto, Ontario, Canada
| | | | | | | | | | - Dan Nayot
- Future Fertility, Toronto, Ontario, Canada
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Benhal P. Micro/Nanorobotics in In Vitro Fertilization: A Paradigm Shift in Assisted Reproductive Technologies. MICROMACHINES 2024; 15:510. [PMID: 38675321 PMCID: PMC11052506 DOI: 10.3390/mi15040510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 03/28/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024]
Abstract
In vitro fertilization (IVF) has transformed the sector of assisted reproductive technology (ART) by presenting hope to couples facing infertility challenges. However, conventional IVF strategies include their own set of problems such as success rates, invasive procedures, and ethical issues. The integration of micro/nanorobotics into IVF provides a prospect to address these challenging issues. This article provides an outline of the use of micro/nanorobotics in IVF specializing in advancing sperm manipulation, egg retrieval, embryo culture, and capacity future improvements in this swiftly evolving discipline. The article additionally explores the challenges and obstacles associated with the integration of micro/nanorobotics into IVF, in addition to the ethical concerns and regulatory elements related to the usage of advanced technologies in ART. A comprehensive discussion of the risk and safety considerations related to using micro/nanorobotics in IVF techniques is likewise presented. Through this exploration, we delve into the core principles, benefits, challenges, and potential impact of micro/nanorobotics in revolutionizing IVF procedures and enhancing affected person outcomes.
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Affiliation(s)
- Prateek Benhal
- Department of Chemical and Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA; ; Tel.: +1-240-972-1482
- National High Magnetic Field Laboratory, 1800 E. Paul Dirac Dr., Tallahassee, FL 32310, USA
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Handayani N, Danardono GB, Boediono A, Wiweko B, Sini I, Sirait B, Polim AA, Suheimi I, Bowolaksono A. Improving Deep Learning-Based Algorithm for Ploidy Status Prediction Through Combined U-NET Blastocyst Segmentation and Sequential Time-Lapse Blastocysts Images. J Reprod Infertil 2024; 25:110-119. [PMID: 39157795 PMCID: PMC11327420 DOI: 10.18502/jri.v25i2.16006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 05/15/2024] [Indexed: 08/20/2024] Open
Abstract
Background Several approaches have been proposed to optimize the construction of an artificial intelligence-based model for assessing ploidy status. These encompass the investigation of algorithms, refining image segmentation techniques, and discerning essential patterns throughout embryonic development. The purpose of the current study was to evaluate the effectiveness of using U-NET architecture for embryo segmentation and time-lapse embryo image sequence extraction, three and ten hr before biopsy to improve model accuracy for prediction of embryonic ploidy status. Methods A total of 1.020 time-lapse videos of blastocysts with known ploidy status were used to construct a convolutional neural network (CNN)-based model for ploidy detection. Sequential images of each blastocyst were extracted from the time-lapse videos over a period of three and ten hr prior to the biopsy, generating 31.642 and 99.324 blastocyst images, respectively. U-NET architecture was applied for blastocyst image segmentation before its implementation in CNN-based model development. Results The accuracy of ploidy prediction model without applying the U-NET segmented sequential embryo images was 0.59 and 0.63 over a period of three and ten hr before biopsy, respectively. Improved model accuracy of 0.61 and 0.66 was achieved, respectively with the implementation of U-NET architecture for embryo segmentation on the current model. Extracting blastocyst images over a 10 hr period yields higher accuracy compared to a three-hr extraction period prior to biopsy. Conclusion Combined implementation of U-NET architecture for blastocyst image segmentation and the sequential compilation of ten hr of time-lapse blastocyst images could yield a CNN-based model with improved accuracy in predicting ploidy status.
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Affiliation(s)
- Nining Handayani
- Doctoral Program in Biomedical Sciences, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
- IRSI Research and Training Centre, Jakarta, Indonesia
| | | | - Arief Boediono
- IRSI Research and Training Centre, Jakarta, Indonesia
- Morula IVF Jakarta Clinic, Jakarta, Indonesia
- Department of Anatomy, Physiology and Pharmacology, IPB University, Bogor, Indonesia
| | - Budi Wiweko
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
- Yasmin IVF Clinic, Dr Cipto Mangunkusumo General Hospital, Jakarta, Indonesia
- Human Reproduction, Infertility, and Family Planning Cluster, Indonesia Reproductive Medicine Research and Training Center, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Ivan Sini
- IRSI Research and Training Centre, Jakarta, Indonesia
- Morula IVF Jakarta Clinic, Jakarta, Indonesia
| | - Batara Sirait
- IRSI Research and Training Centre, Jakarta, Indonesia
- Morula IVF Jakarta Clinic, Jakarta, Indonesia
- Department of Obstetrics and Gynaecology, Faculty of Medicine, Universitas Kristen Indonesia, Jakarta, Indonesia
| | - Arie A Polim
- Department of Obstetrics and Gynecology, School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia
| | - Irham Suheimi
- IRSI Research and Training Centre, Jakarta, Indonesia
- Morula IVF Jakarta Clinic, Jakarta, Indonesia
| | - Anom Bowolaksono
- Cellular and Molecular Mechanisms in Biological System (CEMBIOS) Research Group, Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia
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Güell E. Criteria for implementing artificial intelligence systems in reproductive medicine. Clin Exp Reprod Med 2024; 51:1-12. [PMID: 38035589 PMCID: PMC10914497 DOI: 10.5653/cerm.2023.06009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/31/2023] [Indexed: 12/02/2023] Open
Abstract
This review article discusses the integration of artificial intelligence (AI) in assisted reproductive technology and provides key concepts to consider when introducing AI systems into reproductive medicine practices. The article highlights the various applications of AI in reproductive medicine and discusses whether to use commercial or in-house AI systems. This review also provides criteria for implementing new AI systems in the laboratory and discusses the factors that should be considered when introducing AI in the laboratory, including the user interface, scalability, training, support, follow-up, cost, ethics, and data quality. The article emphasises the importance of ethical considerations, data quality, and continuous algorithm updates to ensure the accuracy and safety of AI systems.
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Affiliation(s)
- Enric Güell
- CONSULTFIV, Valls, Spain
- Procrear, Reus, Spain
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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] [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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Asada Y, Shinohara T, Yonezawa S, Kinugawa T, Asano E, Kojima M, Fukunaga N, Hashizume N, Hashiba Y, Inoue D, Mizuno R, Saito M, Kabeya Y. Development of an AI-based support system for controlled ovarian stimulation. Reprod Med Biol 2024; 23:e12603. [PMID: 39224211 PMCID: PMC11366684 DOI: 10.1002/rmb2.12603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 07/25/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024] Open
Abstract
Purpose Controlled ovarian stimulation (COS) is vital for IVF. We have developed an AI system to support the implementation of COS protocols in our clinical group. Methods We developed two models as AI algorithms of the AI system. One was the oocyte retrieval decision model, to determine the timing of oocyte retrieval, and the other was the prescription inference model, to provide a prescription similar to that of an expert physician. Data was obtained from IVF treatment records from the In Vitro Fertilization (IVF) management system at the Asada Ladies Clinic, and these models were trained with this data. Results The oocyte retrieval decision model achieved superior sensitivity and specificity with 0.964 area under the curve (AUC). The prescription inference model achieved an AUC value of 0.948. Four models, namely the hCG prediction model, the hMG prediction model, the Cetrorelix prediction model, and the Estradiol prediction model included in the prescription inference model, achieved AUC values of 0.914, 0.937, 0.966, and 0.976, respectively. Conclusion The AI algorithm achieved high accuracy and was confirmed to be useful. The AI system has now been implemented as a COS tool in our clinical group for self-funded treatments.
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Affiliation(s)
- Yoshimasa Asada
- Asada Ladies ClinicNagoyaJapan
- Asada Institute for Reproductive MedicineKasugaiJapan
| | | | | | | | - Emiko Asano
- Asada Ladies ClinicNagoyaJapan
- Asada Institute for Reproductive MedicineKasugaiJapan
| | - Masae Kojima
- Asada Ladies ClinicNagoyaJapan
- Asada Institute for Reproductive MedicineKasugaiJapan
| | - Noritaka Fukunaga
- Asada Ladies ClinicNagoyaJapan
- Asada Institute for Reproductive MedicineKasugaiJapan
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12
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Ghayda RA, Cannarella R, Calogero AE, Shah R, Rambhatla A, Zohdy W, Kavoussi P, Avidor-Reiss T, Boitrelle F, Mostafa T, Saleh R, Toprak T, Birowo P, Salvio G, Calik G, Kuroda S, Kaiyal RS, Ziouziou I, Crafa A, Phuoc NHV, Russo GI, Durairajanayagam D, Al-Hashimi M, Hamoda TAAAM, Pinggera GM, Adriansjah R, Maldonado Rosas I, Arafa M, Chung E, Atmoko W, Rocco L, Lin H, Huyghe E, Kothari P, Solorzano Vazquez JF, Dimitriadis F, Garrido N, Homa S, Falcone M, Sabbaghian M, Kandil H, Ko E, Martinez M, Nguyen Q, Harraz AM, Serefoglu EC, Karthikeyan VS, Tien DMB, Jindal S, Micic S, Bellavia M, Alali H, Gherabi N, Lewis S, Park HJ, Simopoulou M, Sallam H, Ramirez L, Colpi G, Agarwal A. Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics. World J Mens Health 2024; 42:39-61. [PMID: 37382282 PMCID: PMC10782130 DOI: 10.5534/wjmh.230050] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/10/2023] [Accepted: 03/17/2023] [Indexed: 06/30/2023] Open
Abstract
Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.
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Affiliation(s)
- Ramy Abou Ghayda
- Urology Institute, University Hospitals, Case Western Reserve University, Cleveland, OH, USA
| | - Rossella Cannarella
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
- Glickman Urological & Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Aldo E. Calogero
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Rupin Shah
- Department of Urology, Lilavati Hospital and Research Centre, Mumbai, India
| | - Amarnath Rambhatla
- Department of Urology, Henry Ford Health System, Vattikuti Urology Institute, Detroit, MI, USA
| | - Wael Zohdy
- Andrology and STDs, Cairo University, Cairo, Egypt
| | - Parviz Kavoussi
- Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Tomer Avidor-Reiss
- Department of Biological Sciences, University of Toledo, Toledo, OH, USA
- Department of Urology, College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Florence Boitrelle
- Reproductive Biology, Fertility Preservation, Andrology, CECOS, Poissy Hospital, Poissy, France
- Department of Biology, Reproduction, Epigenetics, Environment, and Development, Paris Saclay University, UVSQ, INRAE, BREED, Paris, France
| | - Taymour Mostafa
- Andrology, Sexology & STIs Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Ramadan Saleh
- Department of Dermatology, Venereology and Andrology, Faculty of Medicine, Sohag University, Sohag, Egypt
| | - Tuncay Toprak
- Department of Urology, Fatih Sultan Mehmet Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
| | - Ponco Birowo
- Department of Urology, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Gianmaria Salvio
- Department of Endocrinology, Polytechnic University of Marche, Ancona, Italy
| | - Gokhan Calik
- Department of Urology, Istanbul Medipol University, Istanbul, Turkey
| | - Shinnosuke Kuroda
- Glickman Urological & Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
- Department of Urology, Reproduction Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Raneen Sawaid Kaiyal
- Glickman Urological & Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Imad Ziouziou
- Department of Urology, College of Medicine and Pharmacy, Ibn Zohr University, Agadir, Morocco
| | - Andrea Crafa
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Nguyen Ho Vinh Phuoc
- Department of Andrology, Binh Dan Hospital, Ho Chi Minh City, Vietnam
- Department of Urology and Andrology, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Vietnam
| | | | - Damayanthi Durairajanayagam
- Department of Physiology, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Selangor, Malaysia
| | - Manaf Al-Hashimi
- Department of Urology, Burjeel Hospital, Abu Dhabi, United Arab Emirates (UAE)
- Khalifa University, College of Medicine and Health Science, Abu Dhabi, United Arab Emirates (UAE)
| | - Taha Abo-Almagd Abdel-Meguid Hamoda
- Department of Urology, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Urology, Faculty of Medicine, Minia University, El-Minia, Egypt
| | | | - Ricky Adriansjah
- Department of Urology, Hasan Sadikin General Hospital, Universitas Padjadjaran, Banding, Indonesia
| | | | - Mohamed Arafa
- Department of Urology, Hamad Medical Corporation, Doha, Qatar
- Department of Urology, Weill Cornell Medical-Qatar, Doha, Qatar
| | - Eric Chung
- Department of Urology, Princess Alexandra Hospital, University of Queensland, Brisbane QLD, Australia
| | - Widi Atmoko
- Department of Urology, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Lucia Rocco
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”, Caserta, Italy
| | - Haocheng Lin
- Department of Urology, Peking University Third Hospital, Peking University, Beijing, China
| | - Eric Huyghe
- Department of Urology and Andrology, University Hospital of Toulouse, Toulouse, France
| | - Priyank Kothari
- Department of Urology, B.Y.L. Nair Charitable Hospital, Topiwala National Medical College, Mumbai, India
| | | | - Fotios Dimitriadis
- Department of Urology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nicolas Garrido
- IVIRMA Global Research Alliance, IVI Foundation, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
| | - Sheryl Homa
- Department of Biosciences, University of Kent, Canterbury, United Kingdom
| | - Marco Falcone
- Department of Urology, Molinette Hospital, A.O.U. Città della Salute e della Scienza, University of Turin, Torino, Italy
| | - Marjan Sabbaghian
- Department of Andrology, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
| | | | - Edmund Ko
- Department of Urology, Loma Linda University Health, Loma Linda, CA, USA
| | - Marlon Martinez
- Section of Urology, Department of Surgery, University of Santo Tomas Hospital, Manila, Philippines
| | - Quang Nguyen
- Section of Urology, Department of Surgery, University of Santo Tomas Hospital, Manila, Philippines
- Center for Andrology and Sexual Medicine, Viet Duc University Hospital, Hanoi, Vietnam
- Department of Urology, Andrology and Sexual Medicine, University of Medicine and Pharmacy, Vietnam National University, Hanoi, Vietnam
| | - Ahmed M. Harraz
- Urology and Nephrology Center, Mansoura University, Mansoura, Egypt
- Department of Surgery, Urology Unit, Farwaniya Hospital, Farwaniya, Kuwait
- Department of Urology, Sabah Al Ahmad Urology Center, Kuwait City, Kuwait
| | - Ege Can Serefoglu
- Department of Urology, Biruni University School of Medicine, Istanbul, Turkey
| | | | - Dung Mai Ba Tien
- Department of Andrology, Binh Dan Hospital, Ho Chi Minh City, Vietnam
| | - Sunil Jindal
- Department of Andrology and Reproductive Medicine, Jindal Hospital, Meerut, India
| | - Sava Micic
- Department of Andrology, Uromedica Polyclinic, Belgrade, Serbia
| | - Marina Bellavia
- Andrology and IVF Center, Next Fertility Procrea, Lugano, Switzerland
| | - Hamed Alali
- King Fahad Specialist Hospital, Dammam, Saudi Arabia
| | - Nazim Gherabi
- Andrology Committee of the Algerian Association of Urology, Algiers, Algeria
| | - Sheena Lewis
- Examen Lab Ltd., Northern Ireland, United Kingdom
| | - Hyun Jun Park
- Department of Urology, Pusan National University School of Medicine, Busan, Korea
- Medical Research Institute of Pusan National University Hospital, Busan, Korea
| | - Mara Simopoulou
- Department of Experimental Physiology, School of Health Sciences, Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Hassan Sallam
- Alexandria University Faculty of Medicine, Alexandria, Egypt
| | - Liliana Ramirez
- IVF Laboratory, CITMER Reproductive Medicine, Mexico City, Mexico
| | - Giovanni Colpi
- Andrology and IVF Center, Next Fertility Procrea, Lugano, Switzerland
| | - Ashok Agarwal
- Global Andrology Forum, Moreland Hills, OH, USA
- Cleveland Clinic, Cleveland, OH, USA
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Wang S, Chen L, Sun H. Interpretable artificial intelligence-assisted embryo selection improved single-blastocyst transfer outcomes: a prospective cohort study. Reprod Biomed Online 2023; 47:103371. [PMID: 37839212 DOI: 10.1016/j.rbmo.2023.103371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/01/2023] [Accepted: 08/23/2023] [Indexed: 10/17/2023]
Abstract
RESEARCH QUESTION What is the pregnancy and neonatal outcomes of an interpretable artificial intelligence (AI) model for embryo selection in a prospective clinical trial? DESIGN This single-centre prospective cohort study was carried out from October 2021 to March 2022. A total of 330 eligible patients were assigned to their preferred groups, with 250 patients undergoing a fresh single-blastocyst transfer cycle after the exclusion criteria had been applied. For the AI-assisted group (AAG), embryologists selected the embryos for transfer based on the ranking recommendations provided by an interpretable AI system, while with the manual group, embryologists used the Gardner grading system to make their decisions. RESULTS The implantation rate was significantly higher in the AAG than the manual group (80.87% versus 68.15%, P = 0.022). No significant difference was found in terms of monozygotic twin rate, miscarriage rate, live birth rate and ectopic pregnancy rate between the groups. Furthermore, there was no significant difference in terms of neonatal outcomes, including gestational weeks, premature birth rate, birth height, birthweight, sex ratio at birth and newborn malformation rate. The consensus rate between the AI and retrospective analysis by the embryologists was significantly higher for good-quality embryos (i.e. grade 4BB or higher) versus poor-quality embryos (i.e. less than 4BB) (84.71% versus 25%, P < 0.001). CONCLUSIONS These prospective trial results suggest that the proposed AI system could effectively help embryologists to improve the implantation rate with single-blastocyst transfer compared with traditional manual evaluation methods.
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Affiliation(s)
- Shanshan Wang
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Lei Chen
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Haixiang Sun
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
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14
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Serdarogullari M, Raad G, Yarkiner Z, Bazzi M, Mourad Y, Alpturk S, Fakih F, Fakih C, Liperis G. Identifying predictors of Day 5 blastocyst utilization rate using an artificial neural network. Reprod Biomed Online 2023; 47:103399. [PMID: 37862857 DOI: 10.1016/j.rbmo.2023.103399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 10/22/2023]
Abstract
RESEARCH QUESTION Can artificial intelligence identify predictors of an increased Day 5 blastocyst utilization rate (D5BUR), which is one of the most informative key performance indicators in an IVF laboratory? DESIGN This retrospective, multicentre study evaluated six variables for predicting D5BUR using an artificial neural network (ANN): number of metaphase II (MII) oocytes injected (intracytoplasmic sperm injection); use of autologous/donated gametes; maternal age at oocyte retrieval; sperm concentration; progressive sperm motility rate; and fertilization rate. Cycles were divided into training and testing sets through stratified random sampling. D5BUR on Day 5 was grouped into <60% and ≥60% as per the Vienna consensus benchmark values. RESULTS The area under the receiver operating characteristic curve (AUC) to predict the D5BUR groups was 80.2%. From the ANN model, all six independent variables were found to be of significant value for the prediction of D5BUR (P<0.0001), with the most important variable being the number of MII oocytes injected. Investigation of the effect of MII oocytes injected on D5BUR indicated an inverse correlation, with injection of an increasing number of MII oocytes resulting in a decreasing D5BUR (r=-0.344, P<0.001) and injection of up to six oocytes resulting in D5BUR ≥60%. CONCLUSION The number of MII oocytes injected is the most important predictor of D5BUR. Exploration of additional variables and further validation of models that can predict D5BUR can guide the way towards personalized treatment and increased safety.
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Affiliation(s)
| | - Georges Raad
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon; Faculty of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon
| | - Zalihe Yarkiner
- Cyprus International University, Faculty of Arts and Sciences, Department of Basic Sciences and Humanities, Northern Cyprus via Mersin 10, Turkey
| | - Marwa Bazzi
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | - Youmna Mourad
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | | | - Fadi Fakih
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | - Chadi Fakih
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | - George Liperis
- Westmead Fertility Centre, Institute of Reproductive Medicine, University of Sydney, Westmead, NSW, Australia.
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Raj M K, Priyadarshani J, Karan P, Bandyopadhyay S, Bhattacharya S, Chakraborty S. Bio-inspired microfluidics: A review. BIOMICROFLUIDICS 2023; 17:051503. [PMID: 37781135 PMCID: PMC10539033 DOI: 10.1063/5.0161809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 09/01/2023] [Indexed: 10/03/2023]
Abstract
Biomicrofluidics, a subdomain of microfluidics, has been inspired by several ideas from nature. However, while the basic inspiration for the same may be drawn from the living world, the translation of all relevant essential functionalities to an artificially engineered framework does not remain trivial. Here, we review the recent progress in bio-inspired microfluidic systems via harnessing the integration of experimental and simulation tools delving into the interface of engineering and biology. Development of "on-chip" technologies as well as their multifarious applications is subsequently discussed, accompanying the relevant advancements in materials and fabrication technology. Pointers toward new directions in research, including an amalgamated fusion of data-driven modeling (such as artificial intelligence and machine learning) and physics-based paradigm, to come up with a human physiological replica on a synthetic bio-chip with due accounting of personalized features, are suggested. These are likely to facilitate physiologically replicating disease modeling on an artificially engineered biochip as well as advance drug development and screening in an expedited route with the minimization of animal and human trials.
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Affiliation(s)
- Kiran Raj M
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Jyotsana Priyadarshani
- Department of Mechanical Engineering, Biomechanics Section (BMe), KU Leuven, Celestijnenlaan 300, 3001 Louvain, Belgium
| | - Pratyaksh Karan
- Géosciences Rennes Univ Rennes, CNRS, Géosciences Rennes, UMR 6118, 35000 Rennes, France
| | - Saumyadwip Bandyopadhyay
- Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumya Bhattacharya
- Achira Labs Private Limited, 66b, 13th Cross Rd., Dollar Layout, 3–Phase, JP Nagar, Bangalore, Karnataka 560078, India
| | - Suman Chakraborty
- Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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GhoshRoy D, Alvi PA, Santosh KC. AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review. J Med Syst 2023; 47:91. [PMID: 37610455 DOI: 10.1007/s10916-023-01983-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 08/02/2023] [Indexed: 08/24/2023]
Abstract
Infertility has massively disrupted social and marital life, resulting in stressful emotional well-being. Early diagnosis is the utmost need for faster adaption to respond to these changes, which makes possible via AI tools. Our main objective is to comprehend the role of AI in fertility detection since we have primarily worked to find biomarkers and related risk factors associated with infertility. This paper aims to vividly analyse the role of AI as an effective method in screening, predicting for infertility and related risk factors. Three scientific repositories: PubMed, Web of Science, and Scopus, are used to gather relevant articles via technical terms: (human infertility OR human fertility) AND risk factors AND (machine learning OR artificial intelligence OR intelligent system). In this way, we systematically reviewed 42 articles and performed a meta-analysis. The significant findings and recommendations are discussed. These include the rising importance of data augmentation, feature extraction, explainability, and the need to revisit the meaning of an effective system for fertility analysis. Additionally, the paper outlines various mitigation actions that can be employed to tackle infertility and its related risk factors. These insights contribute to a better understanding of the role of AI in fertility analysis and the potential for improving reproductive health outcomes.
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Affiliation(s)
- Debasmita GhoshRoy
- School of Automation, Banasthali Vidyapith, 304022, Rajasthan, India
- Applied AI Research Lab, Vermillion, SD, 57069, USA
| | - P A Alvi
- Department of Physics, Banasthali Vidyapith, 304022, Rajasthan, India
| | - K C Santosh
- Department of Computer Science, University of South Dakota, Vermillion, SD, 57069, USA.
- Applied AI Research Lab, Vermillion, SD, 57069, USA.
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Salih M, Austin C, Warty RR, Tiktin C, Rolnik DL, Momeni M, Rezatofighi H, Reddy S, Smith V, Vollenhoven B, Horta F. Embryo selection through artificial intelligence versus embryologists: a systematic review. Hum Reprod Open 2023; 2023:hoad031. [PMID: 37588797 PMCID: PMC10426717 DOI: 10.1093/hropen/hoad031] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 07/17/2023] [Indexed: 08/18/2023] Open
Abstract
STUDY QUESTION What is the present performance of artificial intelligence (AI) decision support during embryo selection compared to the standard embryo selection by embryologists? SUMMARY ANSWER AI consistently outperformed the clinical teams in all the studies focused on embryo morphology and clinical outcome prediction during embryo selection assessment. WHAT IS KNOWN ALREADY The ART success rate is ∼30%, with a worrying trend of increasing female age correlating with considerably worse results. As such, there have been ongoing efforts to address this low success rate through the development of new technologies. With the advent of AI, there is potential for machine learning to be applied in such a manner that areas limited by human subjectivity, such as embryo selection, can be enhanced through increased objectivity. Given the potential of AI to improve IVF success rates, it remains crucial to review the performance between AI and embryologists during embryo selection. STUDY DESIGN SIZE DURATION The search was done across PubMed, EMBASE, Ovid Medline, and IEEE Xplore from 1 June 2005 up to and including 7 January 2022. Included articles were also restricted to those written in English. Search terms utilized across all databases for the study were: ('Artificial intelligence' OR 'Machine Learning' OR 'Deep learning' OR 'Neural network') AND ('IVF' OR 'in vitro fertili*' OR 'assisted reproductive techn*' OR 'embryo'), where the character '*' refers the search engine to include any auto completion of the search term. PARTICIPANTS/MATERIALS SETTING METHODS A literature search was conducted for literature relating to AI applications to IVF. Primary outcomes of interest were accuracy, sensitivity, and specificity of the embryo morphology grade assessments and the likelihood of clinical outcomes, such as clinical pregnancy after IVF treatments. Risk of bias was assessed using the Modified Down and Black Checklist. MAIN RESULTS AND THE ROLE OF CHANCE Twenty articles were included in this review. There was no specific embryo assessment day across the studies-Day 1 until Day 5/6 of embryo development was investigated. The types of input for training AI algorithms were images and time-lapse (10/20), clinical information (6/20), and both images and clinical information (4/20). Each AI model demonstrated promise when compared to an embryologist's visual assessment. On average, the models predicted the likelihood of successful clinical pregnancy with greater accuracy than clinical embryologists, signifying greater reliability when compared to human prediction. The AI models performed at a median accuracy of 75.5% (range 59-94%) on predicting embryo morphology grade. The correct prediction (Ground Truth) was defined through the use of embryo images according to post embryologists' assessment following local respective guidelines. Using blind test datasets, the embryologists' accuracy prediction was 65.4% (range 47-75%) with the same ground truth provided by the original local respective assessment. Similarly, AI models had a median accuracy of 77.8% (range 68-90%) in predicting clinical pregnancy through the use of patient clinical treatment information compared to 64% (range 58-76%) when performed by embryologists. When both images/time-lapse and clinical information inputs were combined, the median accuracy by the AI models was higher at 81.5% (range 67-98%), while clinical embryologists had a median accuracy of 51% (range 43-59%). LIMITATIONS REASONS FOR CAUTION The findings of this review are based on studies that have not been prospectively evaluated in a clinical setting. Additionally, a fair comparison of all the studies were deemed unfeasible owing to the heterogeneity of the studies, development of the AI models, database employed and the study design and quality. WIDER IMPLICATIONS OF THE FINDINGS AI provides considerable promise to the IVF field and embryo selection. However, there needs to be a shift in developers' perception of the clinical outcome from successful implantation towards ongoing pregnancy or live birth. Additionally, existing models focus on locally generated databases and many lack external validation. STUDY FUNDING/COMPETING INTERESTS This study was funded by Monash Data Future Institute. All authors have no conflicts of interest to declare. REGISTRATION NUMBER CRD42021256333.
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Affiliation(s)
- M Salih
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - C Austin
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - R R Warty
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
| | - C Tiktin
- School of Engineering, RMIT University, Melbourne, Victoria, Australia
| | - D L Rolnik
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Women’s and Newborn Program, Monash Health, Melbourne, Victoria, Australia
| | - M Momeni
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
| | - H Rezatofighi
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
- Monash Data Future Institute, Monash University, Clayton, Victoria, Australia
| | - S Reddy
- School of Medicine, Deakin University, Geelong, Victoria, Australia
| | - V Smith
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
| | - B Vollenhoven
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Women’s and Newborn Program, Monash Health, Melbourne, Victoria, Australia
- Monash IVF, Melbourne, Victoria, Australia
| | - F Horta
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Monash Data Future Institute, Monash University, Clayton, Victoria, Australia
- City Fertility, Melbourne, Victoria, Australia
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18
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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] [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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Glatstein I, Chavez-Badiola A, Curchoe CL. New frontiers in embryo selection. J Assist Reprod Genet 2023; 40:223-234. [PMID: 36609943 PMCID: PMC9935769 DOI: 10.1007/s10815-022-02708-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 12/23/2022] [Indexed: 01/09/2023] Open
Abstract
Human infertility is a major global public health issue estimated to affect one out of six couples, while the number of assisted reproduction cycles grows impressively year over year. Efforts to alleviate infertility using advanced technology are gaining traction rapidly as infertility has an enormous impact on couples and the potential to destabilize entire societies if replacement birthrates are not achieved. Artificial intelligence (AI) technologies, leveraged by the highly advanced assisted reproductive technology (ART) industry, are a promising addition to the armamentarium of tools available to combat global infertility. This review provides a background for current methodologies in embryo selection, which is a manual, time-consuming, and poorly reproducible task. AI has the potential to improve this process (among many others) in both the clinician's office and the IVF laboratory. Embryo selection is evolving through digital methodologies into an automated procedure, with superior reliability and reproducibility, that is likely to result in higher pregnancy rates for patients. There is an emerging body of data demonstrating the utility of AI applications in multiple areas in the IVF laboratory. AI platforms have been developed to evaluate individual embryologist performance; to provide quality assurance for culture systems; to correlate embryologist's assessments and AI systems; to predict embryo ploidy, implantation, fetal heartbeat, and live birth outcome; and to replace the current "analogue" system of embryo selection with a digital paradigm. AI capability will distinguish high performing, high profit margin, low-cost, and highly successful IVF clinic business models. We think it will become the standard, "new normal" in IVF labs, as rapidly and thoroughly as vitrification, blastocyst culture, and intracytoplasmic sperm injection replaced their predecessor technologies. At the time of this review, the AI technology to automate embryo evaluation and selection has robustly matured, and therefore, it is the main focus of this review.
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Affiliation(s)
| | - Alejandro Chavez-Badiola
- IVF 2.0 LTD, 1 Liverpool Road, Maghull, L31 2HB, Merseyside, UK
- New Hope Fertility Center, Av. Prado Norte 135, Lomas de Chapultepec, CP11000, Mexico City, Mexico
- Reproductive Genetics, School of Biosciences, University of Kent, Canterbury, CT2 7NZ, Kent, UK
| | - Carol Lynn Curchoe
- ART Compass, a Fertility Guidance Technology, Newport Beach, CA, 92660, USA
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20
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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; 40:289-299. [PMID: 36609941 PMCID: PMC9935782 DOI: 10.1007/s10815-022-02695-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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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Affiliation(s)
- Julia Buldo-Licciardi
- New York University Grossman School of Medicine, 550 First Avenue, NBV 9E2, New York, NY 10016 USA
| | - Michael J. Large
- CooperSurgical, Inc., 75 Corporate Drive, Trumbull, CT 06611 USA
| | - David H. McCulloh
- New York University Langone Fertility Center, 159 E 53rd Street 3rd Floor, New York, NY 10022 USA
| | - Caroline McCaffrey
- New York University Langone Fertility Center, 159 E 53rd Street 3rd Floor, New York, NY 10022 USA
| | - James A. Grifo
- New York University Langone Fertility Center, 159 E 53rd Street 3rd Floor, New York, NY 10022 USA
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21
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Zhukov OB, Chernykh VB. Artificial intelligence in reproductive medicine. ANDROLOGY AND GENITAL SURGERY 2023. [DOI: 10.17650/2070-9781-2022-23-4-15-25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- O. B. Zhukov
- Рeoples’ Friendship University of Russia (RUDN University); Association of Vascular Urologists and Reproductologists
| | - V. B. Chernykh
- Research Centre for Medical Genetics; N.I. Pirogov Russian National Research Medical University
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22
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Voliotis M, Hanassab S, Abbara A, Heinis T, Dhillo WS, Tsaneva-Atanasova K. Quantitative approaches in clinical reproductive endocrinology. CURRENT OPINION IN ENDOCRINE AND METABOLIC RESEARCH 2022; 27:100421. [PMID: 36643692 PMCID: PMC9831018 DOI: 10.1016/j.coemr.2022.100421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Understanding the human hypothalamic-pituitary-gonadal (HPG) axis presents a major challenge for medical science. Dysregulation of the HPG axis is linked to infertility and a thorough understanding of its dynamic behaviour is necessary to both aid diagnosis and to identify the most appropriate hormonal interventions. Here, we review how quantitative models are being used in the context of clinical reproductive endocrinology to: 1. analyse the secretory patterns of reproductive hormones; 2. evaluate the effect of drugs in fertility treatment; 3. aid in the personalization of assisted reproductive technology (ART). In this review, we demonstrate that quantitative models are indispensable tools enabling us to describe the complex dynamic behaviour of the reproductive axis, refine the treatment of fertility disorders, and predict clinical intervention outcomes.
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Key Words
- AI, artificial intelligence
- AMH, anti-Müllerian hormone
- ART, assisted reproductive technology
- Artificial intelligence
- Assisted reproductive technology
- BSA, Bayesian Spectrum Analysis
- Clinical decision making
- E2, estradiol
- FSH, follicle-stimulating hormone
- GnRH, gonadotropin-releasing hormone
- HA, hypothalamic amenorrhea
- HPG, hypothalamic-pituitary gonadal
- IVF, in vitro fertilization
- In vitro fertilization
- LH, luteinizing hormone
- ML, machine learning
- Machine learning
- Mathematical modelling
- OHSS, ovarian hyperstimulation syndrome
- P4, progesterone
- PCOS, polycystic ovary syndrome
- Pulsatility analysis
- Quantitative modelling
- Reproductive endocrinology
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Affiliation(s)
- Margaritis Voliotis
- Department of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Simon Hanassab
- Section of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom
- Department of Computing, Imperial College London, London, United Kingdom
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom
| | - Ali Abbara
- Section of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom
| | - Thomas Heinis
- Department of Computing, Imperial College London, London, United Kingdom
| | - Waljit S. Dhillo
- Section of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom
| | - Krasimira Tsaneva-Atanasova
- Department of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
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23
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Hernández-González J, Valls O, Torres-Martín A, Cerquides J. Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical models. Comput Biol Med 2022; 150:106160. [PMID: 36242813 DOI: 10.1016/j.compbiomed.2022.106160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 09/08/2022] [Accepted: 10/01/2022] [Indexed: 12/19/2022]
Abstract
Embryo selection is a critical step in assisted reproduction: good selection criteria are expected to increase the probability of inducing a pregnancy. Machine learning techniques have been applied for implantation prediction or embryo quality assessment, which embryologists can use to make a decision about embryo selection. However, this is a highly uncertain real-world problem, and current proposals do not model always all the sources of uncertainty. We present a novel probabilistic graphical model that accounts for three different sources of uncertainty, the standard embryo and cycle viability, and a third one that represents any unknown factor that can drive a treatment to a failure in otherwise perfect conditions. We derive a parametric learning method based on the Expectation-Maximization strategy, which accounts for uncertainty issues. We empirically analyze the model within a real database consisting of 604 cycles (3125 embryos) carried out at Hospital Donostia (Spain). Embryologists followed the protocol of the Spanish Association for Reproduction Biology Studies (ASEBIR), based on morphological features, for embryo selection. Our model predictions are correlated with the ASEBIR protocol, which validates our model. The benefits of accounting for the different sources of uncertainty and the importance of the cycle characteristics are shown. Considering only transferred embryos, our model does not further discriminate them as implanted or failed, suggesting that the ASEBIR protocol could be understood as a thorough summary of the available morphological features.
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Affiliation(s)
| | - Olga Valls
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona (UB), 08007 Barcelona, Spain
| | - Adrián Torres-Martín
- Department of Information and Communications Engineering, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain
| | - Jesús Cerquides
- Artificial Intelligence Research Institute (IIIA-CSIC), 08193 Bellaterra, Spain
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24
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Buccella A. "AI for all" is a matter of social justice. AI AND ETHICS 2022; 3:1-10. [PMID: 36189174 PMCID: PMC9510536 DOI: 10.1007/s43681-022-00222-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 09/09/2022] [Indexed: 11/29/2022]
Abstract
Artificial intelligence (AI) is a radically transformative technology (or system of technologies) that created new existential possibilities and new standards of well-being in human societies. In this article, I argue that to properly understand the increasingly important role AI plays in our society, we must consider its impacts on social justice. For this reason, I propose to conceptualize AI's transformative role and its socio-political implications through the lens of the theory of social justice known as the Capability Approach. According to the approach, a just society must put its members in a position to acquire and exercise a series of basic capabilities and provide them with the necessary means for these capabilities to be actively realized. Because AI is re-shaping the very definition of some of these basic capabilities, I conclude that AI itself should be considered among the conditions of possession and realization of the capabilities it transforms. In other words, access to AI-in the many forms this access can take-is necessary for social justice.
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Affiliation(s)
- Alessandra Buccella
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA USA
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25
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Letort G, Eichmuller A, Da Silva C, Nikalayevich E, Crozet F, Salle J, Minc N, Labrune E, Wolf JP, Terret ME, Verlhac MH. An interpretable and versatile machine learning approach for oocyte phenotyping. J Cell Sci 2022; 135:jcs260281. [PMID: 35660922 PMCID: PMC9377708 DOI: 10.1242/jcs.260281] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 11/20/2022] Open
Abstract
Meiotic maturation is a crucial step of oocyte formation, allowing its potential fertilization and embryo development. Elucidating this process is important for both fundamental research and assisted reproductive technology. However, few computational tools based on non-invasive measurements are available to characterize oocyte meiotic maturation. Here, we develop a computational framework to phenotype oocytes based on images acquired in transmitted light. We trained neural networks to segment the contour of oocytes and their zona pellucida using oocytes from diverse species. We defined a comprehensive set of morphological features to describe an oocyte. These steps were implemented in an open-source Fiji plugin. We present a feature-based machine learning pipeline to recognize oocyte populations and determine morphological differences between them. We first demonstrate its potential to screen oocytes from different strains and automatically identify their morphological characteristics. Its second application is to predict and characterize the maturation potential of oocytes. We identify the texture of the zona pellucida and cytoplasmic particle size as features to assess mouse oocyte maturation potential and tested whether these features were applicable to the developmental potential of human oocytes. This article has an associated First Person interview with the first author of the paper.
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Affiliation(s)
- Gaelle Letort
- Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, 75231Paris, France
| | - Adrien Eichmuller
- Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, 75231Paris, France
| | - Christelle Da Silva
- Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, 75231Paris, France
| | - Elvira Nikalayevich
- Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, 75231Paris, France
| | - Flora Crozet
- Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, 75231Paris, France
| | - Jeremy Salle
- Université Paris Cité, CNRS, Institut Jacques Monod, 75013 Paris, France
| | - Nicolas Minc
- Université Paris Cité, CNRS, Institut Jacques Monod, 75013 Paris, France
| | - Elsa Labrune
- Service de Médecine de la Reproduction, Hôpital Femme Mère Enfant, Hospices Civils de Lyon, 69500 Bron, France
- Université Claude Bernard Lyon 1, 69100 Lyon, France
- INSERM U1208, StemGamE, 69500 Bron, France
| | - Jean-Philippe Wolf
- Team ‘From Gametes To Birth’, Département Développement, Reproduction, Cancer, Institut Cochin, Inserm U1016, CNRS UMR8104, Université de Paris, 22 rue Mechain, 75014 Paris, France
- Service d'Histologie-Embryologie-Biologie de la Reproduction, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, 75014 Paris, France
| | - Marie-Emilie Terret
- Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, 75231Paris, France
| | - Marie-Hélène Verlhac
- Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, 75231Paris, France
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26
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An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Despite the use of new techniques on embryo selection and the presence of equipment on the market, such as EmbryoScope® and Geri®, which help in the evaluation of embryo quality, there is still a subjectivity between the embryologist’s classifications, which are subjected to inter- and intra-observer variability, therefore compromising the successful implantation of the embryo. Nonetheless, with the acquisition of images through the time-lapse system, it is possible to perform digital processing of these images, providing a better analysis of the embryo, in addition to enabling the automatic analysis of a large volume of information. An image processing protocol was developed using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each one representing a different aspect of the embryo and describing a different characteristic of the blastocyst. These variables can be categorized into texture, gray-level average, gray-level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. The image processing protocol can segment human blastocyst images and automatically extract 33 variables that describe quantitative aspects of the blastocyst’s regions, with potential utility in embryo selection for assisted reproductive technology (ART).
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27
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Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis. Biomedicines 2022; 10:biomedicines10030697. [PMID: 35327499 PMCID: PMC8945147 DOI: 10.3390/biomedicines10030697] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/08/2022] [Accepted: 03/13/2022] [Indexed: 12/04/2022] Open
Abstract
Artificial intelligence (AI) has been gaining support in the field of in vitro fertilization (IVF). Despite the promising existing data, AI cannot yet claim gold-standard status, which serves as the rationale for this study. This systematic review and data synthesis aims to evaluate and report on the predictive capabilities of AI-based prediction models regarding IVF outcome. The study has been registered in PROSPERO (CRD42021242097). Following a systematic search of the literature in Pubmed/Medline, Embase, and Cochrane Central Library, 18 studies were identified as eligible for inclusion. Regarding live-birth, the Area Under the Curve (AUC) of the Summary Receiver Operating Characteristics (SROC) was 0.905, while the partial AUC (pAUC) was 0.755. The Observed: Expected ratio was 1.12 (95%CI: 0.26–2.37; 95%PI: 0.02–6.54). Regarding clinical pregnancy with fetal heartbeat, the AUC of the SROC was 0.722, while the pAUC was 0.774. The O:E ratio was 0.77 (95%CI: 0.54–1.05; 95%PI: 0.21–1.62). According to this data synthesis, the majority of the AI-based prediction models are successful in accurately predicting the IVF outcome regarding live birth, clinical pregnancy, clinical pregnancy with fetal heartbeat, and ploidy status. This review attempted to compare between AI and human prediction capabilities, and although studies do not allow for a meta-analysis, this systematic review indicates that the AI-based prediction models perform rather similarly to the embryologists’ evaluations. While AI models appear marginally more effective, they still have some way to go before they can claim to significantly surpass the clinical embryologists’ predictive competence.
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28
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Valente RS, Marsico TV, Sudano MJ. Basic and applied features in the cryopreservation progress of bovine embryos. Anim Reprod Sci 2022; 239:106970. [DOI: 10.1016/j.anireprosci.2022.106970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 03/10/2022] [Accepted: 03/19/2022] [Indexed: 11/30/2022]
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29
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Liu L, Shen F, Liang H, Yang Z, Yang J, Chen J. Machine Learning-Based Modeling of Ovarian Response and the Quantitative Evaluation of Comprehensive Impact Features. Diagnostics (Basel) 2022; 12:diagnostics12020492. [PMID: 35204580 PMCID: PMC8871024 DOI: 10.3390/diagnostics12020492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 01/22/2022] [Accepted: 02/11/2022] [Indexed: 01/27/2023] Open
Abstract
Appropriate ovarian responses to the controlled ovarian stimulation strategy is the premise for a good outcome of the in vitro fertilization cycle. With the booming of artificial intelligence, machine learning is becoming a popular and promising approach for tailoring a controlled ovarian stimulation strategy. Nowadays, most machine learning-based tailoring strategies aim to generally classify the controlled ovarian stimulation outcome, lacking the capacity to precisely predict the outcome and evaluate the impact features. Based on a clinical cohort composed of 1365 women and two machine learning methods of artificial neural network and supporting vector regression, a regression prediction model of the number of oocytes retrieved is trained, validated, and selected. Given the proposed model, an index called the normalized mean impact value is defined and calculated to reflect the importance of each impact feature. The proposed models can estimate the number of oocytes retrieved with high precision, with the regression coefficient being 0.882% and 89.84% of the instances having the prediction number ≤ 5. Among the impact features, the antral follicle count has the highest importance, followed by the E2 level on the human chorionic gonadotropin day, the age, and the Anti-Müllerian hormone, with their normalized mean impact value > 0.3. Based on the proposed model, the prognostic results for ovarian response can be predicted, which enables scientific clinical decision support for the customized controlled ovarian stimulation strategies for women, and eventually helps yield better in vitro fertilization outcomes.
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Affiliation(s)
- Liu Liu
- Department of Obstetrics and Gynecology, Renmin Hospital, Wuhan University, Wuhan 430072, China; (L.L.); (F.S.); (H.L.)
| | - Fujin Shen
- Department of Obstetrics and Gynecology, Renmin Hospital, Wuhan University, Wuhan 430072, China; (L.L.); (F.S.); (H.L.)
| | - Hua Liang
- Department of Obstetrics and Gynecology, Renmin Hospital, Wuhan University, Wuhan 430072, China; (L.L.); (F.S.); (H.L.)
| | - Zhe Yang
- Reproductive Medicine Center, Renmin Hospital, Wuhan University, Wuhan 430072, China;
| | - Jing Yang
- Reproductive Medicine Center, Renmin Hospital, Wuhan University, Wuhan 430072, China;
- Correspondence: (J.Y.); (J.C.)
| | - Jiao Chen
- Reproductive Medicine Center, Renmin Hospital, Wuhan University, Wuhan 430072, China;
- Correspondence: (J.Y.); (J.C.)
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30
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Chow DJX, Wijesinghe P, Dholakia K, Dunning KR. Does artificial intelligence have a role in the IVF clinic? REPRODUCTION AND FERTILITY 2022; 2:C29-C34. [PMID: 35118395 PMCID: PMC8801019 DOI: 10.1530/raf-21-0043] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 08/23/2021] [Indexed: 12/29/2022] Open
Abstract
The success of IVF has remained stagnant for a decade. The focus of a great deal of research is to improve on the current ~30% success rate of IVF. Artificial intelligence (AI), or machines that mimic human intelligence, has been gaining traction for its potential to improve outcomes in medicine, such as cancer diagnosis from medical images. In this commentary, we discuss whether AI has the potential to improve fertility outcomes in the IVF clinic. Based on existing research, we examine the potential of adopting AI within multiple facets of an IVF cycle, including egg/sperm and embryo selection, as well as formulation of an IVF treatment regimen. We discuss both the potential benefits and concerns of the patient and clinician in adopting AI in the clinic. We outline hurdles that need to be overcome prior to implementation. We conclude that AI has an important future in improving IVF success.
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Affiliation(s)
- Darren J X Chow
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia.,Australian Research Council Centre of Excellence for Nanoscale Biophotonics, The University of Adelaide, Adelaide, Australia.,Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
| | - Philip Wijesinghe
- SUPA, School of Physics and Astronomy, University of St Andrews, North Haugh, St Andrews, Fife, United Kingdom
| | - Kishan Dholakia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia.,SUPA, School of Physics and Astronomy, University of St Andrews, North Haugh, St Andrews, Fife, United Kingdom.,School of Biological Sciences, The University of Adelaide, Adelaide, Australia.,Department of Physics, College of Science, Yonsei University, Seoul, South Korea
| | - Kylie R Dunning
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia.,Australian Research Council Centre of Excellence for Nanoscale Biophotonics, The University of Adelaide, Adelaide, Australia.,Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
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31
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Huang B, Zheng S, Ma B, Yang Y, Zhang S, Jin L. Using deep learning to predict the outcome of live birth from more than 10,000 embryo data. BMC Pregnancy Childbirth 2022; 22:36. [PMID: 35034623 PMCID: PMC8761300 DOI: 10.1186/s12884-021-04373-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 12/29/2021] [Indexed: 12/04/2022] Open
Abstract
Background Recently, the combination of deep learning and time-lapse imaging provides an objective, standard and scientific solution for embryo selection. However, the reported studies were based on blastocyst formation or clinical pregnancy as the end point. To the best of our knowledge, there is no predictive model that uses the outcome of live birth as the predictive end point. Can a deep learning model predict the probability of live birth from time-lapse system? Methods This study retrospectively analyzed the time-lapse data and live birth outcomes of embryos samples from January 2018 to November 2019. We used the SGD optimizer with an initial learning rate of 0.025 and cosine learning rate reduction strategy. The network is randomly initialized and trained for 200 epochs from scratch. The model is quantitively evaluated over a hold-out test and a 5-fold cross-validation by the average area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results The deep learning model was able to predict live birth outcomes from time-lapse images with an AUC of 0.968 in 5-fold stratified cross-validation. Conclusions This research reported a deep learning model that predicts the live birth outcome of a single blastocyst transfer. This efficient model for predicting the outcome of live births can automatically analyze the time-lapse images of the patient’s embryos without the need for manual embryo annotation and evaluation, and then give a live birth prediction score for each embryo, and sort the embryos by the predicted value. Supplementary Information The online version contains supplementary material available at 10.1186/s12884-021-04373-5.
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Affiliation(s)
- Bo Huang
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Shunyuan Zheng
- School of Computer Science and Technology, Harbin Institute of Technology, Weihai, 264209, China
| | - Bingxin Ma
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Yongle Yang
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Shengping Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Weihai, 264209, 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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Dimitriadis I, Zaninovic N, Badiola AC, Bormann CL. Artificial intelligence in the embryology laboratory: a review. Reprod Biomed Online 2021; 44:435-448. [PMID: 35027326 DOI: 10.1016/j.rbmo.2021.11.003] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 10/07/2021] [Accepted: 11/04/2021] [Indexed: 02/03/2023]
Abstract
The goal of an IVF cycle is a healthy live-born baby. Despite the many advances in the field of assisted reproductive technologies, accurately predicting the outcome of an IVF cycle has yet to be achieved. One reason for this is the method of selecting an embryo for transfer. Morphological assessment of embryos is the traditional method of evaluating embryo quality and selecting which embryo to transfer. However, this subjective method of assessing embryos leads to inter- and intra-observer variability, resulting in less than optimal IVF success rates. To overcome this, it is common practice to transfer more than one embryo, potentially resulting in high-risk multiple pregnancies. Although time-lapse incubators and preimplantation genetic testing for aneuploidy have been introduced to help increase the chances of live birth, the outcomes remain less than ideal. Utilization of artificial intelligence (AI) has become increasingly popular in the medical field and is increasingly being leveraged in the embryology laboratory to help improve IVF outcomes. Many studies have been published investigating the use of AI as an unbiased, automated approach to embryo assessment. This review summarizes recent AI advancements in the embryology laboratory.
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Affiliation(s)
- Irene Dimitriadis
- Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston MA, USA
| | - Nikica Zaninovic
- The Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York NY, USA
| | - Alejandro Chavez Badiola
- New Hope Fertility Center, Av. Prado Norte 135, Lomas de Chapultepec, Mexico City, Mexico; IVF 2.0 LTD, 1 Liverpool Rd, Maghull, Merseyside, UK; School of Biosciences, University of Kent Kent, UK
| | - Charles L Bormann
- Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston MA, USA.
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Afnan MAM, Liu Y, Conitzer V, Rudin C, Mishra A, Savulescu J, Afnan M. Interpretable, not black-box, artificial intelligence should be used for embryo selection. Hum Reprod Open 2021; 2021:hoab040. [PMID: 34938903 PMCID: PMC8687137 DOI: 10.1093/hropen/hoab040] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/18/2021] [Indexed: 11/23/2022] Open
Abstract
Artificial intelligence (AI) techniques are starting to be used in IVF, in particular for selecting which embryos to transfer to the woman. AI has the potential to process complex data sets, to be better at identifying subtle but important patterns, and to be more objective than humans when evaluating embryos. However, a current review of the literature shows much work is still needed before AI can be ethically implemented for this purpose. No randomized controlled trials (RCTs) have been published, and the efficacy studies which exist demonstrate that algorithms can broadly differentiate well between 'good-' and 'poor-' quality embryos but not necessarily between embryos of similar quality, which is the actual clinical need. Almost universally, the AI models were opaque ('black-box') in that at least some part of the process was uninterpretable. This gives rise to a number of epistemic and ethical concerns, including problems with trust, the possibility of using algorithms that generalize poorly to different populations, adverse economic implications for IVF clinics, potential misrepresentation of patient values, broader societal implications, a responsibility gap in the case of poor selection choices and introduction of a more paternalistic decision-making process. Use of interpretable models, which are constrained so that a human can easily understand and explain them, could overcome these concerns. The contribution of AI to IVF is potentially significant, but we recommend that AI models used in this field should be interpretable, and rigorously evaluated with RCTs before implementation. We also recommend long-term follow-up of children born after AI for embryo selection, regulatory oversight for implementation, and public availability of data and code to enable research teams to independently reproduce and validate existing models.
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Affiliation(s)
| | - Yanhe Liu
- Monash IVF Group, Southport, Australia
- School of Human Sciences, University of Western
Australia, Crawley, Australia
- School of Medical and Health Sciences, Edith Cowan
University, Joondalup, Australia
- School of Health Sciences and Medicine, Bond
University, Robina, Australia
| | - Vincent Conitzer
- Department of Computer Science, Duke
University, Durham, NC, USA
- Department of Economics, Duke
University, Durham, NC, USA
- Department of Philosophy, Duke
University, Durham, NC, USA
- Department of Computer Science, Institute for Ethics
in AI, University of Oxford, Oxford, UK
- Department of Philosophy, Institute for Ethics in
AI, University of Oxford, Oxford, UK
| | - Cynthia Rudin
- Department of Computer Science, Duke
University, Durham, NC, USA
- Department of Electrical Engineering, Duke
University, Durham, NC, USA
- Department of Statistical Science, Duke
University, Durham, NC, USA
| | - Abhishek Mishra
- Uehiro Centre for Practical Ethics, University of
Oxford, Oxford, UK
| | - Julian Savulescu
- Uehiro Centre for Practical Ethics, University of
Oxford, Oxford, UK
- Wellcome Centre for Ethics and Humanities,
University of Oxford, Oxford, UK
- Murdoch Children’s Research Institute, Royal
Children's Hospital, Parkville, Australia
| | - Masoud Afnan
- Department of Obstetrics & Gynaecology,
Qingdao United Family Hospital, Qingdao, China
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Kragh MF, Karstoft H. Embryo selection with artificial intelligence: how to evaluate and compare methods? J Assist Reprod Genet 2021; 38:1675-1689. [PMID: 34173914 PMCID: PMC8324599 DOI: 10.1007/s10815-021-02254-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 06/02/2021] [Indexed: 12/19/2022] Open
Abstract
Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent years, artificial intelligence (AI) has been used extensively to improve and automate the embryo ranking and selection procedure by extracting relevant information from embryo microscopy images. The AI models are evaluated based on their ability to identify the embryo(s) with the highest chance(s) of achieving a successful pregnancy. Whether such evaluations should be based on ranking performance or pregnancy prediction, however, seems to divide studies. As such, a variety of performance metrics are reported, and comparisons between studies are often made on different outcomes and data foundations. Moreover, superiority of AI methods over manual human evaluation is often claimed based on retrospective data, without any mentions of potential bias. In this paper, we provide a technical view on some of the major topics that divide how current AI models are trained, evaluated and compared. We explain and discuss the most common evaluation metrics and relate them to the two separate evaluation objectives, ranking and prediction. We also discuss when and how to compare AI models across studies and explain in detail how a selection bias is inevitable when comparing AI models against current embryo selection practice in retrospective cohort studies.
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Affiliation(s)
- Mikkel Fly Kragh
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark.
- Vitrolife A/S, Viby J, Denmark.
| | - Henrik Karstoft
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark
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Trolice MP, Curchoe C, Quaas AM. Artificial intelligence-the future is now. J Assist Reprod Genet 2021; 38:1607-1612. [PMID: 34231110 PMCID: PMC8260235 DOI: 10.1007/s10815-021-02272-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 06/29/2021] [Indexed: 11/25/2022] Open
Abstract
The pros and cons of artificial intelligence in assisted reproductive technology are presented.
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Affiliation(s)
- Mark P Trolice
- Obstetrics and Gynecology, University of Central Florida, Orlando, USA.
- The IVF Center, Orlando, FL, USA.
| | | | - Alexander M Quaas
- Division of Reproductive Endocrinology and Infertility, University of California, San Diego, CA, USA
- Reproductive Partners San Diego, San Diego, CA, USA
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Can methods of artificial intelligence aid in optimizing patient selection in patients undergoing intrauterine inseminations? J Assist Reprod Genet 2021; 38:1665-1673. [PMID: 34031765 PMCID: PMC8324709 DOI: 10.1007/s10815-021-02224-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 05/07/2021] [Indexed: 12/28/2022] Open
Abstract
Purpose AI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the study was to construct several predictive models based on clinical data and select the best models to predict IUI procedure outcomes. Methods Clinical data (patient baseline characteristics, sperm quality, hormonal status, and cycle data) from 1029 IUI procedures performed in 413 couples stimulated by clomiphene citrate, letrozole, or gonadotropins were used to build several models to predict clinical pregnancy. The models included ANN, random forest, PLS, SVM, and linear models using the caret package in R. The models were evaluated using ROC analysis by means of random CV on test data. Results Out of the best performing models, the random forest model achieved an AUC of 0.66, a sensitivity of 0.432, and a specificity of 0.756. This performance was followed by the PLS model, which achieved a sensitivity of 0.459 and specificity of 0.734. The other models achieved significantly lower AUCs. When adjusting the predictive cutoff value, confusion matrices show that clinical pregnancy is twice as likely in the case of positive prediction. Conclusion Among the compared methods, the random forest and PLS models demonstrated superior performance in predicting the clinical outcome of IUI. With additional research and clinical validation, AI methods may be successfully used in improving patient selection and consequently lead to better clinical results.
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Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF. J Assist Reprod Genet 2021; 38:1627-1639. [PMID: 33811587 DOI: 10.1007/s10815-021-02123-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 02/21/2021] [Indexed: 12/30/2022] Open
Abstract
In vitro fertilization has been regarded as a forefront solution in treating infertility for over four decades, yet its effectiveness has remained relatively low. This could be attributed to the lack of advancements for the method of observing and selecting the most viable embryos for implantation. The conventional morphological assessment of embryos exhibits inevitable drawbacks which include time- and effort-consuming, and imminent risks of bias associated with subjective assessments performed by individual embryologists. A combination of these disadvantages, undeterred by the introduction of the time-lapse incubator technology, has been considered as a prominent contributor to the less preferable success rate of IVF cycles. Nonetheless, a recent surge of AI-based solutions for tasks automation in IVF has been observed. An AI-powered assistant could improve the efficiency of performing certain tasks in addition to offering accurate algorithms that behave as baselines to minimize the subjectivity of the decision-making process. Through a comprehensive review, we have discovered multiple approaches of implementing deep learning technology, each with varying degrees of success, for constructing the automated systems in IVF which could evaluate and even annotate the developmental stages of an embryo.
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Liu R, Bai S, Jiang X, Luo L, Tong X, Zheng S, Wang Y, Xu B. Multifactor Prediction of Embryo Transfer Outcomes Based on a Machine Learning Algorithm. Front Endocrinol (Lausanne) 2021; 12:745039. [PMID: 34795639 PMCID: PMC8593232 DOI: 10.3389/fendo.2021.745039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 10/15/2021] [Indexed: 11/17/2022] Open
Abstract
In vitro fertilization-embryo transfer (IVF-ET) technology make it possible for infertile couples to conceive a baby successfully. Nevertheless, IVF-ET does not guarantee success. Frozen embryo transfer (FET) is an important supplement to IVF-ET. Many factors are correlated with the outcome of FET which is unpredictable. Machine learning is a field of study that predict various outcomes by defining data attributes and using relevant data and calculation algorithms. Machine learning algorithm has been widely used in clinical research. The present study focuses on making predictions of early pregnancy outcomes in FET through clinical characters, including age, body mass index (BMI), endometrial thickness (EMT) on the day of progesterone treatment, good-quality embryo rate (GQR), and type of infertility (primary or secondary), serum estradiol level (E2) on the day of embryo transfer, and serum progesterone level (P) on the day of embryo transfer. We applied four representative machine learning algorithms, including logistic regression (LR), conditional inference tree, random forest (RF) and support vector machine (SVM) to build prediction models and identify the predictive factors. We found no significant difference among the models in the sensitivity, specificity, positive predictive rate, negative predictive rate or accuracy in predicting the pregnancy outcome of FET. For example, the positive/negative predictive rate of the SVM (gamma = 1, cost = 100, 10-fold cross validation) is 0.56 and 0.55. This approach could provide a reference for couples considering FET. The prediction accuracy of the present study is limited, which suggests that there may be some other more effective predictors to be developed in future work.
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Affiliation(s)
- Ran Liu
- Reproductive and Genetic Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Shun Bai
- Reproductive and Genetic Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Xiaohua Jiang
- Reproductive and Genetic Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Lihua Luo
- Reproductive and Genetic Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Xianhong Tong
- Reproductive and Genetic Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Shengxia Zheng
- Reproductive and Genetic Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Ying Wang
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- *Correspondence: Ying Wang, ; Bo Xu,
| | - Bo Xu
- Reproductive and Genetic Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- *Correspondence: Ying Wang, ; Bo Xu,
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Valiuškaitė V, Raudonis V, Maskeliūnas R, Damaševičius R, Krilavičius T. Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination. SENSORS (BASEL, SWITZERLAND) 2020; 21:E72. [PMID: 33374461 PMCID: PMC7795243 DOI: 10.3390/s21010072] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/15/2020] [Accepted: 12/21/2020] [Indexed: 12/15/2022]
Abstract
We propose a deep learning method based on the Region Based Convolutional Neural Networks (R-CNN) architecture for the evaluation of sperm head motility in human semen videos. The neural network performs the segmentation of sperm heads, while the proposed central coordinate tracking algorithm allows us to calculate the movement speed of sperm heads. We have achieved 91.77% (95% CI, 91.11-92.43%) accuracy of sperm head detection on the VISEM (A Multimodal Video Dataset of Human Spermatozoa) sperm sample video dataset. The mean absolute error (MAE) of sperm head vitality prediction was 2.92 (95% CI, 2.46-3.37), while the Pearson correlation between actual and predicted sperm head vitality was 0.969. The results of the experiments presented below will show the applicability of the proposed method to be used in automated artificial insemination workflow.
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Affiliation(s)
- Viktorija Valiuškaitė
- Department of Control Systems, Kaunas University of Technology, 51423 Kaunas, Lithuania; (V.V.); (V.R.)
| | - Vidas Raudonis
- Department of Control Systems, Kaunas University of Technology, 51423 Kaunas, Lithuania; (V.V.); (V.R.)
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, 51423 Kaunas, Lithuania;
| | - Robertas Damaševičius
- Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania;
- Faculty of Applied Mathematics, Silesian University of Technology, 444-100 Gliwice, Poland
| | - Tomas Krilavičius
- Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania;
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