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Hanassab S, Abbara A, Yeung AC, Voliotis M, Tsaneva-Atanasova K, Kelsey TW, Trew GH, Nelson SM, Heinis T, Dhillo WS. The prospect of artificial intelligence to personalize assisted reproductive technology. NPJ Digit Med 2024; 7:55. [PMID: 38429464 PMCID: PMC10907618 DOI: 10.1038/s41746-024-01006-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 01/10/2024] [Indexed: 03/03/2024] Open
Abstract
Infertility affects 1-in-6 couples, with repeated intensive cycles of assisted reproductive technology (ART) required by many to achieve a desired live birth. In ART, typically, clinicians and laboratory staff consider patient characteristics, previous treatment responses, and ongoing monitoring to determine treatment decisions. However, the reproducibility, weighting, and interpretation of these characteristics are contentious, and highly operator-dependent, resulting in considerable reliance on clinical experience. Artificial intelligence (AI) is ideally suited to handle, process, and analyze large, dynamic, temporal datasets with multiple intermediary outcomes that are generated during an ART cycle. Here, we review how AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART.
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Affiliation(s)
- Simon Hanassab
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
- Department of Computing, Imperial College London, London, UK
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK
| | - Ali Abbara
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Arthur C Yeung
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Margaritis Voliotis
- Department of Mathematics and Statistics, University of Exeter, Exeter, UK
- Living Systems Institute, University of Exeter, Exeter, UK
- EPSRC Hub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK
| | - Krasimira Tsaneva-Atanasova
- Department of Mathematics and Statistics, University of Exeter, Exeter, UK
- Living Systems Institute, University of Exeter, Exeter, UK
- EPSRC Hub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK
| | - Tom W Kelsey
- School of Computer Science, University of St Andrews, St Andrews, UK
| | - Geoffrey H Trew
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
- The Fertility Partnership, Oxford, UK
| | - Scott M Nelson
- The Fertility Partnership, Oxford, UK
- School of Medicine, University of Glasgow, Glasgow, UK
- Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Thomas Heinis
- Department of Computing, Imperial College London, London, UK
| | - Waljit S Dhillo
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK.
- Imperial College Healthcare NHS Trust, London, UK.
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2
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Luong TMT, Le NQK. Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine. J Assist Reprod Genet 2024; 41:239-252. [PMID: 37880512 PMCID: PMC10894798 DOI: 10.1007/s10815-023-02973-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/13/2023] [Indexed: 10/27/2023] Open
Abstract
With the rising demand for in vitro fertilization (IVF) cycles, there is a growing need for innovative techniques to optimize procedure outcomes. One such technique is time-lapse system (TLS) for embryo incubation, which minimizes environmental changes in the embryo culture process. TLS also significantly advances predicting embryo quality, a crucial determinant of IVF cycle success. However, the current subjective nature of embryo assessments is due to inter- and intra-observer subjectivity, resulting in highly variable results. To address this challenge, reproductive medicine has gradually turned to artificial intelligence (AI) to establish a standardized and objective approach, aiming to achieve higher success rates. Extensive research is underway investigating the utilization of AI in TLS to predict multiple outcomes. These studies explore the application of popular AI algorithms, their specific implementations, and the achieved advancements in TLS. This review aims to provide an overview of the advances in AI algorithms and their particular applications within the context of TLS and the potential challenges and opportunities for further advancements in reproductive medicine.
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Affiliation(s)
- Thi-My-Trang Luong
- International Master Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei, 110, Taiwan
| | - Nguyen Quoc Khanh Le
- AIBioMed Research Group, Taipei Medical University, Taipei, 110, Taiwan.
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan.
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 110, Taiwan.
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, 110, Taiwan.
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3
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Yuan G, Lv B, Hao C. Application of artificial neural networks in reproductive medicine. HUM FERTIL 2023; 26:1195-1201. [PMID: 36628627 DOI: 10.1080/14647273.2022.2156301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 09/01/2022] [Indexed: 01/12/2023]
Abstract
With the emergence of the age of information, the data on reproductive medicine has improved immensely. Nonetheless, healthcare workers who wish to utilise the relevance and implied value of the various data available to aid clinical decision-making encounter the difficulty of statistically analysing such large data. The application of artificial intelligence becoming widespread in recent years has emerged as a turning point in this regard. Artificial neural networks (ANNs) exhibit beneficial characteristics of comprehensive analysis and autonomous learning, owing to which these are being applied to disease diagnosis, embryo quality assessment, and prediction of pregnancy outcomes. The present report aims to summarise the application of ANNs in the field of reproduction and analyse its further application potential.
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Affiliation(s)
- Guanghui Yuan
- Department of Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| | - Bohan Lv
- Department of Intensive Care Unit, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Cuifang Hao
- Department of Reproductive Medicine, The Affiliated Women and Children's Hospital of Qingdao University, Qingdao, Shandong, China
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Kikuchi Y, Ito D, Wakayama S, Ooga M, Wakayama T. Time-lapse observation of mouse preimplantation embryos using a simple closed glass capillary method. Sci Rep 2023; 13:19893. [PMID: 37963931 PMCID: PMC10646084 DOI: 10.1038/s41598-023-47017-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 11/08/2023] [Indexed: 11/16/2023] Open
Abstract
Time-lapse observation is a popular method for analyzing mammalian preimplantation embryos, but it often requires expensive equipment and skilled techniques. We previously developed a simply and costly embryo-culture system in a sealed tube that does not require a CO2 incubator. In the present study, we developed a new time-lapse observation system using our previous culture method and a glass capillary. Zygotes were placed in a glass capillary and sunk in oil for observation under a stereomicroscope. Warming the capillary using a thermoplate enabled most of the zygotes to develop into blastocysts and produce healthy offspring. This time-lapse observation system captured images every 30 min for up to 5 days, which confirmed that the developmental speed and quality of the embryos were not affected, even with fluorescence. Overall, this new system is a simple time-lapse observation method for preimplantation embryos that does not require dedicated machines and advanced techniques.
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Affiliation(s)
- Yasuyuki Kikuchi
- Faculty of Life and Environmental Science, University of Yamanashi, Kofu, 400-8510, Japan
| | - Daiyu Ito
- Faculty of Life and Environmental Science, University of Yamanashi, Kofu, 400-8510, Japan
| | - Sayaka Wakayama
- Advanced Biotechnology Center, University of Yamanashi, Kofu, 400-8510, Japan
| | - Masatoshi Ooga
- Faculty of Life and Environmental Science, University of Yamanashi, Kofu, 400-8510, Japan
- Department of Animal Science and Biotechnology, School of Veterinary Medicine, Azabu University, Fuchinobe, Chuo-ku, Sagamihara, 252-5201, Japan
| | - Teruhiko Wakayama
- Advanced Biotechnology Center, University of Yamanashi, Kofu, 400-8510, Japan.
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5
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Berman A, Anteby R, Efros O, Klang E, Soffer S. Deep learning for embryo evaluation using time-lapse: a systematic review of diagnostic test accuracy. Am J Obstet Gynecol 2023; 229:490-501. [PMID: 37116822 DOI: 10.1016/j.ajog.2023.04.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 03/28/2023] [Accepted: 04/19/2023] [Indexed: 04/30/2023]
Abstract
OBJECTIVE This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring. DATA SOURCES A systematic search was conducted in PubMed and Web of Science databases from January 2016 to December 2022. The search strategy was carried out by using key words and MeSH (Medical Subject Headings) terms. STUDY ELIGIBILITY CRITERIA Studies were included if they reported the accuracy of convolutional neural network models for embryo evaluation using time-lapse monitoring. The review was registered with PROSPERO (International Prospective Register of Systematic Reviews; identification number CRD42021275916). METHODS Two reviewer authors independently screened results using the Covidence systematic review software. The full-text articles were reviewed when studies met the inclusion criteria or in any uncertainty. Nonconsensus was resolved by a third reviewer. Risk of bias and applicability were evaluated using the QUADAS-2 tool and the modified Joanna Briggs Institute or JBI checklist. RESULTS Following a systematic search of the literature, 22 studies were identified as eligible for inclusion. All studies were retrospective. A total of 522,516 images of 222,998 embryos were analyzed. Three main outcomes were evaluated: successful in vitro fertilization, blastocyst stage classification, and blastocyst quality. Most studies reported >80% accuracy, and embryologists were outperformed in some. Ten studies had a high risk of bias, mostly because of patient bias. CONCLUSION The application of artificial intelligence in time-lapse monitoring has the potential to provide more efficient, accurate, and objective embryo evaluation. Models that examined blastocyst stage classification showed the best predictions. Models that predicted live birth had a low risk of bias, used the largest databases, and had external validation, which heightens their relevance to clinical application. Our systematic review is limited by the high heterogeneity among the included studies. Researchers should share databases and standardize reporting.
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Affiliation(s)
- Aya Berman
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
| | - Roi Anteby
- Department of Surgery and Transplantation B, Chaim Sheba Medical Center, Tel Hashomer, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Orly Efros
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; National Hemophilia Center and Institute of Thrombosis & Hemostasis, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Eyal Klang
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Institute for Health Care Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY; Deep Vision Lab, Chaim Sheba Medical Center, Ramat Gan, Israel; Division of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Shelly Soffer
- Deep Vision Lab, Chaim Sheba Medical Center, Ramat Gan, Israel; Internal Medicine B, Assuta Medical Center, Ashdod, Israel; Ben-Gurion University of the Negev, Be'er Sheva, Israel
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6
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Si K, Huang B, Jin L. Application of artificial intelligence in gametes and embryos selection. HUM FERTIL 2023; 26:757-777. [PMID: 37705466 DOI: 10.1080/14647273.2023.2256980] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/22/2023] [Indexed: 09/15/2023]
Abstract
Gamete and embryo quality are critical to the success rate of Assisted Reproductive Technology (ART) cycles, but there remains a lack of methods to accurately measure the quality of sperm, oocytes and embryos. The ability of Artificial Intelligence (AI) technology to analyze large amounts of data, especially video and images, is particularly useful in gamete and embryo assessment and selection. The well-trained model has fast calculation speed and high accuracy, which can help embryologists to perform more objective gamete and embryo selection. Various artificial intelligence models have been developed for gamete and embryo assessment, some of which exhibit good performance. In this review, we summarize the latest applications of AI technology in semen analysis, as well as selection for sperm, oocyte and embryo, and discuss the existing problems and development directions of artificial intelligence in this field.
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Affiliation(s)
- Keyi Si
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Bo Huang
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Lei Jin
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
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Tran HP, Diem Tuyet HT, Dang Khoa TQ, Lam Thuy LN, Bao PT, Thanh Sang VN. Microscopic Video-Based Grouped Embryo Segmentation: A Deep Learning Approach. Cureus 2023; 15:e45429. [PMID: 37859886 PMCID: PMC10582205 DOI: 10.7759/cureus.45429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2023] [Indexed: 10/21/2023] Open
Abstract
PURPOSE The primary aim of this research is to enhance the utilization of advanced deep learning (DL) techniques in the domain of in vitro fertilization (IVF) by presenting a more refined approach to the segmentation and organization of microscopic embryos. This study also seeks to establish a comprehensive embryo database that can be employed for future research and educational purposes. METHODS This study introduces an advanced methodology for embryo segmentation and organization using DL. The approach comprises three primary steps: Embryo Segmentation Model, Segmented Embryo Image Organization, and Clear and Blur Image Classification. The proposed approach was rigorously evaluated on a sample of 5182 embryos extracted from 362 microscopic embryo videos. RESULTS The study's results show that the proposed method is highly effective in accurately segmenting and organizing embryo images. This is evidenced by the high mean average precision values of 1.0 at an intersection over union threshold of 0.5 and across the range of 0.5 to 0.95, indicating a robust object detection capability that is vital in the IVF process. Segmentation of images based on various factors such as the day of development, patient, growth medium, and embryo facilitates easy comparison and identification of potential issues. Finally, appropriate threshold values for clear and blur image classification are proposed. CONCLUSION The suggested technique represents an indispensable stage of data preparation for IVF training and education. Furthermore, this study provides a solid foundation for future research and adoption of DL in IVF, which is expected to have a significant positive impact on IVF outcomes.
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Affiliation(s)
- Huy Phuong Tran
- Department of Infertility, Hung Vuong Hospital, Ho Chi Minh City, VNM
| | | | | | - Le Nhi Lam Thuy
- IC-IP Lab, Faculty of Information and Technology, Saigon University, Ho Chi Minh City, VNM
| | - Pham The Bao
- IC-IP Lab, Faculty of Information and Technology, Saigon University, Ho Chi Minh City, VNM
| | - Vu Ngoc Thanh Sang
- IC-IP Lab, Faculty of Information and Technology, Saigon University, Ho Chi Minh City, VNM
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8
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Yang J, Wang Y, Li C, Han W, Liu W, Xiong S, Zhang Q, Tong K, Huang G, Zhang X. Variation of Female Pronucleus Reveals Oocyte or Embryo Chromosomal Copy Number Variations. ADVANCED GENETICS (HOBOKEN, N.J.) 2023; 4:2200001. [PMID: 36910589 PMCID: PMC10000260 DOI: 10.1002/ggn2.202200001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 09/20/2022] [Indexed: 11/11/2022]
Abstract
The characteristics of the human pronuclei (PNs), which exist 16-22 h after fertilization, appear to serve as good indicators to evaluate the quality of human oocyte and embryo, and may reflect the status of female and male chromosome composition. Here, a quantitative PN measurement method that is generated by applying expert experience combined with deep learning from large annotated datasets is reported. After mathematic reconstruction of PNs, significant differences are obtained in chromosome-normal rate and chromosomal small errors such as copy number variants by comparing the size of the reconstructive female PN. After integrating the whole procedure of PN dynamics and adjusting for errors that occur during PN identification, the results are robust. Notably, all positive prediction results are obtained from the female propositus population. Thus, the size of female PNs may mirror the internal quality of the chromosomal integrity of the oocyte. Embryos that develop from zygotes with larger female PNs may have a reduced risk of copy number variations.
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Affiliation(s)
- Jingwei Yang
- Center for Reproductive Medicine Women and Children's Hospital of Chongqing Medical University Chongqing Health Center for Women and Children Chongqing 400010 China.,Chongqing Key Laboratory of Human embryo Engineering Chongqing 400010 China
| | - Yikang Wang
- Department of Mechatronics Graduate School of Medicine, Engineering, and Agricultural Sciences University of Yamanashi Yamanashi-ken 400-8510 Japan
| | - Chong Li
- Center for Reproductive Medicine Women and Children's Hospital of Chongqing Medical University Chongqing Health Center for Women and Children Chongqing 400010 China.,Chongqing Key Laboratory of Human embryo Engineering Chongqing 400010 China
| | - Wei Han
- Chongqing Key Laboratory of Human embryo Engineering Chongqing 400010 China.,Chongqing Clinical Research Center for Reprodutive Medicine Chongqing 400010 China
| | - Weiwei Liu
- Chongqing Key Laboratory of Human embryo Engineering Chongqing 400010 China.,Chongqing Clinical Research Center for Reprodutive Medicine Chongqing 400010 China
| | - Shun Xiong
- Chongqing Key Laboratory of Human embryo Engineering Chongqing 400010 China.,Chongqing Clinical Research Center for Reprodutive Medicine Chongqing 400010 China
| | - Qi Zhang
- Center for Reproductive Medicine Women and Children's Hospital of Chongqing Medical University Chongqing Health Center for Women and Children Chongqing 400010 China.,Chongqing Key Laboratory of Human embryo Engineering Chongqing 400010 China
| | - Keya Tong
- Chongqing Key Laboratory of Human embryo Engineering Chongqing 400010 China.,Chongqing Clinical Research Center for Reprodutive Medicine Chongqing 400010 China
| | - Guoning Huang
- Center for Reproductive Medicine Women and Children's Hospital of Chongqing Medical University Chongqing Health Center for Women and Children Chongqing 400010 China.,Chongqing Key Laboratory of Human embryo Engineering Chongqing 400010 China.,Chongqing Clinical Research Center for Reprodutive Medicine Chongqing 400010 China
| | - Xiaodong Zhang
- Center for Reproductive Medicine Women and Children's Hospital of Chongqing Medical University Chongqing Health Center for Women and Children Chongqing 400010 China.,Chongqing Key Laboratory of Human embryo Engineering Chongqing 400010 China.,Chongqing Clinical Research Center for Reprodutive Medicine Chongqing 400010 China
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9
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Medenica S, Zivanovic D, Batkoska L, Marinelli S, Basile G, Perino A, Cucinella G, Gullo G, Zaami S. The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes-The Value of Regulatory Frameworks. Diagnostics (Basel) 2022; 12:diagnostics12122979. [PMID: 36552986 PMCID: PMC9777042 DOI: 10.3390/diagnostics12122979] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 11/22/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022] Open
Abstract
Infertility is a global health issue affecting women and men of reproductive age with increasing incidence worldwide, in part due to greater awareness and better diagnosis. Assisted reproduction technologies (ART) are considered the ultimate step in the treatment of infertility. Recently, artificial intelligence (AI) has been progressively used in the many fields of medicine, integrating knowledge and computer science through machine learning algorithms. AI has the potential to improve infertility diagnosis and ART outcomes estimated as pregnancy and/or live birth rate, especially with recurrent ART failure. A broad-ranging review has been conducted, focusing on clinical AI applications up until September 2022, which could be estimated in terms of possible applications, such as ultrasound monitoring of folliculogenesis, endometrial receptivity, embryo selection based on quality and viability, and prediction of post implantation embryo development, in order to eliminate potential contributing risk factors. Oocyte morphology assessment is highly relevant in terms of successful fertilization rate, as well as during oocyte freezing for fertility preservation, and substantially valuable in oocyte donation cycles. AI has great implications in the assessment of male infertility, with computerised semen analysis systems already in use and a broad spectrum of possible AI-based applications in environmental and lifestyle evaluation to predict semen quality. In addition, considerable progress has been made in terms of harnessing AI in cases of idiopathic infertility, to improve the stratification of infertile/fertile couples based on their biological and clinical signatures. With AI as a very powerful tool of the future, our review is meant to summarise current AI applications and investigations in contemporary reproduction medicine, mainly focusing on the nonsurgical aspects of it; in addition, the authors have briefly explored the frames of reference and guiding principles for the definition and implementation of legal, regulatory, and ethical standards for AI in healthcare.
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Affiliation(s)
- Sanja Medenica
- Department of Endocrinology, Internal Medicine Clinic, Clinical Center of Montenegro, School of Medicine, University of Montenegro, 81000 Podgorica, Montenegro
| | - Dusan Zivanovic
- Clinic of Endocrinology, Diabetes and Metabolic Disorders, University Clinical Center of Serbia, 11000 Belgrade, Serbia
| | - Ljubica Batkoska
- Medical Faculty, Ss. Cyril and Methodius University of Skopje, 1000 Skopje, North Macedonia
| | | | | | - Antonio Perino
- Department of Obstetrics and Gynecology, Villa Sofia Cervello Hospital, IVF UNIT, University of Palermo, 90146 Palermo, Italy
| | - Gaspare Cucinella
- Department of Obstetrics and Gynecology, Villa Sofia Cervello Hospital, IVF UNIT, University of Palermo, 90146 Palermo, Italy
| | - Giuseppe Gullo
- Department of Obstetrics and Gynecology, Villa Sofia Cervello Hospital, IVF UNIT, University of Palermo, 90146 Palermo, Italy
- Correspondence:
| | - Simona Zaami
- Department of Anatomical, Histological, Forensic and Orthopedic Sciences, “Sapienza” University of Rome, 00161 Rome, Italy
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10
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Mushtaq A, Mumtaz M, Raza A, Salem N, Yasir MN. Artificial Intelligence-Based Detection of Human Embryo Components for Assisted Reproduction by In Vitro Fertilization. SENSORS (BASEL, SWITZERLAND) 2022; 22:7418. [PMID: 36236516 PMCID: PMC9573355 DOI: 10.3390/s22197418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 09/21/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
Assisted reproductive technology is helping humans by addressing infertility using different medical procedures that help in a successful pregnancy. In vitro fertilization (IVF) is one of those assisted reproduction methods in which the sperm and eggs are combined outside the human body in a specialized environment and kept for growth. Assisted reproductive technology is helping humans by addressing infertility using different medical procedures that help in a successful pregnancy. The morphology of the embryological components is highly related to the success of the assisted reproduction procedure. In approximately 3-5 days, the embryo transforms into the blastocyst. To prevent the multiple-birth risk and to increase the chance of pregnancy the embryologist manually analyzes the blastocyst components and selects valuable embryos to transfer to the women's uterus. The manual microscopic analysis of blastocyst components, such as trophectoderm, zona pellucida, blastocoel, and inner cell mass, is time-consuming and requires keen expertise to select a viable embryo. Artificial intelligence is easing medical procedures by the successful implementation of deep learning algorithms that mimic the medical doctor's knowledge to provide a better diagnostic procedure that helps in reducing the diagnostic burden. The deep learning-based automatic detection of these blastocyst components can help to analyze the morphological properties to select viable embryos. This research presents a deep learning-based embryo component segmentation network (ECS-Net) that accurately detects trophectoderm, zona pellucida, blastocoel, and inner cell mass for embryological analysis. The proposed method (ECS-Net) is based on a shallow deep segmentation network that uses two separate streams produced by a base convolutional block and a depth-wise separable convolutional block. Both streams are densely concatenated in combination with two dense skip paths to produce powerful features before and after upsampling. The proposed ECS-Net is evaluated on a publicly available microscopic blastocyst image dataset, the experimental segmentation results confirm the efficacy of the proposed method. The proposed ECS-Net is providing a mean Jaccard Index (Mean JI) of 85.93% for embryological analysis.
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Affiliation(s)
- Abeer Mushtaq
- Department of Primary and Secondary Healthcare, Lahore 54000, Pakistan
| | - Maria Mumtaz
- Department of Primary and Secondary Healthcare, Lahore 54000, Pakistan
| | - Ali Raza
- Department of Primary and Secondary Healthcare, Lahore 54000, Pakistan
| | - Nema Salem
- Electrical and Computer Engineering Department, Effat College of Engineering, Effat University, Jeddah 22332, Saudi Arabia
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11
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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: 30] [Impact Index Per Article: 10.0] [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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12
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Riegler MA, Stensen MH, Witczak O, Andersen JM, Hicks SA, Hammer HL, Delbarre E, Halvorsen P, Yazidi A, Holst N, Haugen TB. Artificial intelligence in the fertility clinic: status, pitfalls and possibilities. Hum Reprod 2021; 36:2429-2442. [PMID: 34324672 DOI: 10.1093/humrep/deab168] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/21/2021] [Indexed: 12/15/2022] Open
Abstract
In recent years, the amount of data produced in the field of ART has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, artificial intelligence (AI) is progressively used in medical practice and may become a promising tool to improve success rates with ART. AI models may compensate for the lack of objectivity in several critical procedures in fertility clinics, especially embryo and sperm assessments. Various models have been developed, and even though several of them show promising performance, there are still many challenges to overcome. In this review, we present recent research on AI in the context of ART. We discuss the strengths and weaknesses of the presented methods, especially regarding clinical relevance. We also address the pitfalls hampering successful use of AI in the clinic and discuss future possibilities and important aspects to make AI truly useful for ART.
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Affiliation(s)
- M A Riegler
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | | | - O Witczak
- Department of Life Sciences and Health, Faculty of Health Sciences, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - J M Andersen
- Department of Life Sciences and Health, Faculty of Health Sciences, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - S A Hicks
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.,Department of Computer Science, Faculty of Technology, Art and Design, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - H L Hammer
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.,Department of Computer Science, Faculty of Technology, Art and Design, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - E Delbarre
- Department of Life Sciences and Health, Faculty of Health Sciences, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - P Halvorsen
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.,Department of Computer Science, Faculty of Technology, Art and Design, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - A Yazidi
- Department of Computer Science, Faculty of Technology, Art and Design, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - N Holst
- Fertilitetssenteret, Oslo, Norway
| | - T B Haugen
- Department of Life Sciences and Health, Faculty of Health Sciences, OsloMet-Oslo Metropolitan University, Oslo, Norway
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13
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Targosz A, Przystałka P, Wiaderkiewicz R, Mrugacz G. Semantic segmentation of human oocyte images using deep neural networks. Biomed Eng Online 2021; 20:40. [PMID: 33892725 PMCID: PMC8066497 DOI: 10.1186/s12938-021-00864-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 03/04/2021] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Infertility is a significant problem of humanity. In vitro fertilisation is one of the most effective and frequently applied ART methods. The effectiveness IVF depends on the assessment and selection of gametes and embryo with the highest developmental potential. The subjective nature of morphological assessment of oocytes and embryos is still one of the main reasons for seeking effective and objective methods for assessing quality in automatic manner. The most promising methods to automatic classification of oocytes and embryos are based on image analysis aided by machine learning techniques. The special attention is paid on deep neural networks that can be used as classifiers solving the problem of automatic assessment of the oocytes/embryos. METHODS This paper deals with semantic segmentation of human oocyte images using deep neural networks in order to develop new version of the predefined neural networks. Deep semantic oocyte segmentation networks can be seen as medically oriented predefined networks understanding the content of the image. The research presented in the paper is focused on the performance comparison of different types of convolutional neural networks for semantic oocyte segmentation. In the case study, the merits and limitations of the selected deep neural networks are analysed. RESULTS 71 deep neural models were analysed. The best score was obtained for one of the variants of DeepLab-v3-ResNet-18 model, when the training accuracy (Acc) reached about 85% for training patterns and 79% for validation ones. The weighted intersection over union (wIoU) and global accuracy (gAcc) for test patterns were calculated, as well. The obtained values of these quality measures were 0,897 and 0.93, respectively. CONCLUSION The obtained results prove that the proposed approach can be applied to create deep neural models for semantic oocyte segmentation with the high accuracy guaranteeing their usage as the predefined networks in other tasks.
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Affiliation(s)
- Anna Targosz
- Department of Histology and Embryology, Medical University of Silesia, Faculty of Medical Sciences, 18 Medyków St., 40-752 Katowice, Poland
- Center for Reproductive Medicine Bocian, 26 Akademicka St., 15-267 Białystok, Poland
| | - Piotr Przystałka
- Department of Fundamentals of Machinery Design, Silesian University of Technology, Faculty of Mechanical Engineering, 18a Konarskiego St., 44-100 Gliwice, Poland
| | - Ryszard Wiaderkiewicz
- Department of Histology and Embryology, Medical University of Silesia, Faculty of Medical Sciences, 18 Medyków St., 40-752 Katowice, Poland
| | - Grzegorz Mrugacz
- Center for Reproductive Medicine Bocian, 26 Akademicka St., 15-267 Białystok, Poland
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14
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Zhao M, Xu M, Li H, Alqawasmeh O, Chung JPW, Li TC, Lee TL, Tang PMK, Chan DYL. Application of convolutional neural network on early human embryo segmentation during in vitro fertilization. J Cell Mol Med 2021; 25:2633-2644. [PMID: 33486848 PMCID: PMC7933952 DOI: 10.1111/jcmm.16288] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 01/05/2021] [Indexed: 11/29/2022] Open
Abstract
Selection of the best quality embryo is the key for a faithful implantation in in vitro fertilization (IVF) practice. However, the process of evaluating numerous images captured by time-lapse imaging (TLI) system is time-consuming and some important features cannot be recognized by naked eyes. Convolutional neural network (CNN) is used in medical imaging yet in IVF. The study aims to apply CNN on day-one human embryo TLI. We first presented CNN algorithm for day-one human embryo segmentation on three distinct features: zona pellucida (ZP), cytoplasm and pronucleus (PN). We tested the CNN performance compared side-by-side with manual labelling by clinical embryologist, then measured the segmented day-one human embryo parameters and compared them with literature reported values. The precisions of segmentation were that cytoplasm over 97%, PN over 84% and ZP around 80%. For the morphometrics data of cytoplasm, ZP and PN, the results were comparable with those reported in literatures, which showed high reproducibility and consistency. The CNN system provides fast and stable analytical outcome to improve work efficiency in IVF setting. To conclude, our CNN system is potential to be applied in practice for day-one human embryo segmentation as a robust tool with high precision, reproducibility and speed.
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Affiliation(s)
- Mingpeng Zhao
- Assisted Reproductive Technology Unit, Department of Obstetrics and Gynaecology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Murong Xu
- Assisted Reproductive Technology Unit, Department of Obstetrics and Gynaecology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China.,School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Hanhui Li
- School of Computer Science and Information Security, Guilin University of Electronic and Technology, Guilin, China
| | - Odai Alqawasmeh
- Assisted Reproductive Technology Unit, Department of Obstetrics and Gynaecology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Jacqueline Pui Wah Chung
- Assisted Reproductive Technology Unit, Department of Obstetrics and Gynaecology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Tin Chiu Li
- Assisted Reproductive Technology Unit, Department of Obstetrics and Gynaecology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Tin-Lap Lee
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Patrick Ming-Kuen Tang
- Department of Anatomical and Cellular Pathology, State Key Laboratory of Translational Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - David Yiu Leung Chan
- Assisted Reproductive Technology Unit, Department of Obstetrics and Gynaecology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
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