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Norton C, Pollard C, Stalker K, Aston K, Jenkins T. Novel bioinformatic analyses of somatic cell contamination in sperm samples. Syst Biol Reprod Med 2024; 70:174-182. [PMID: 38908909 DOI: 10.1080/19396368.2024.2368716] [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: 07/17/2023] [Accepted: 06/11/2024] [Indexed: 06/24/2024]
Abstract
The assessment of epigenetic profiles in sperm is sensitive to somatic cell contamination, which can influence methylation signals at gene promoters. This contamination is particularly problematic in the assessment of DNA methylation in samples with low sperm counts, where fractional amounts of somatic cell DNA can lead to significant shifts in measured methylation state. In this study, a new method of detecting possible somatic cell contamination is proposed through two multi-region bioinformatic models: a traditional differential methylation analysis and a machine learning logistic regression model. These models were trained on publicly available sperm (n = 489) and blood (n = 1029) DNA methylation array data and tested on a contamination set, wherein the sperm of four donors with normal sperm counts were run on a 450k methylation array with four permutations each, including pure blood, half blood and half sperm by DNA concentration, half blood and half sperm by cell count, and pure sperm (n = 16). The DMR and logistic regression model classified the contamination testing set with 100% and 94% accuracy, respectively. These new methods of detecting the effects of somatic cell contamination allow for more accurate differentiation between epigenetic profiles that contain a biological somatic-like shift and those that have somatic-like signatures because of contamination.
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Affiliation(s)
- Carter Norton
- Department of Cell Biology, Brigham Young University, Provo, UT, USA
| | - Chad Pollard
- Department of Cell Biology, Brigham Young University, Provo, UT, USA
| | - Kelaney Stalker
- Department of Cell Biology, Brigham Young University, Provo, UT, USA
| | - Kenneth Aston
- Department of Surgery, University of Utah, Salt Lake City, UT, USA
| | - Timothy Jenkins
- Department of Cell Biology, Brigham Young University, Provo, UT, USA
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2
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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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3
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Gül M, Russo GI, Kandil H, Boitrelle F, Saleh R, Chung E, Kavoussi P, Mostafa T, Shah R, Agarwal A. Male Infertility: New Developments, Current Challenges, and Future Directions. World J Mens Health 2024; 42:502-517. [PMID: 38164030 PMCID: PMC11216957 DOI: 10.5534/wjmh.230232] [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: 08/16/2023] [Accepted: 08/27/2023] [Indexed: 01/03/2024] Open
Abstract
There have been many significant scientific advances in the diagnostics and treatment modalities in the field of male infertility in recent decades. Examples of these include assisted reproductive technologies, sperm selection techniques for intracytoplasmic sperm injection, surgical procedures for sperm retrieval, and novel tests of sperm function. However, there is certainly a need for new developments in this field. In this review, we discuss advances in the management of male infertility, such as seminal oxidative stress testing, sperm DNA fragmentation testing, genetic and epigenetic tests, genetic manipulations, artificial intelligence, personalized medicine, and telemedicine. The role of the reproductive urologist will continue to expand in future years to address different topzics related to diverse questions and controversies of pathophysiology, diagnosis, and therapy of male infertility, training researchers and physicians in medical and scientific research in reproductive urology/andrology, and further development of andrology as an independent specialty.
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Affiliation(s)
- Murat Gül
- Department of Urology, Selcuk University School of Medicine, Konya, Turkey
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Giorgio Ivan Russo
- Urology Section, University of Catania, Catania, Italy
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Hussein Kandil
- Fakih IVF Fertility Center, Abu Dhabi, UAE
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Florence Boitrelle
- Reproductive Biology, Fertility Preservation, Andrology, CECOS, Poissy Hospital, Poissy, France
- Paris Saclay University, UVSQ, INRAE, BREED, Jouy-en-Josas, France
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Ramadan Saleh
- Department of Dermatology, Venereology and Andrology, Faculty of Medicine, Sohag University, Sohag, Egypt
- Ajyal IVF Center, Ajyal Hospital, Sohag, Egypt
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Eric Chung
- Department of Urology, Princess Alexandra Hospital, University of Queensland, Brisbane, QLD, Australia
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Parviz Kavoussi
- Department of Reproductive Urology, Austin Fertility & Reproductive Medicine/Westlake IVF, Austin, TX, USA
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Taymour Mostafa
- Department of Andrology, Sexology and STIs, Faculty of Medicine, Cairo University, Cairo, Egypt
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Rupin Shah
- Department of Urology, Lilavati Hospital and Research Centre, Mumbai, India
- Well Women's Centre, Sir HN Reliance Foundation Hospital, Mumbai, India
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Ashok Agarwal
- Global Andrology Forum, Moreland Hills, OH, USA
- Cleveland Clinic, Cleveland, OH, USA.
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4
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Ma J, Xie Q, Zhang Y, Xiao Q, Liu X, Qiao C, Tian Y. Advances in microfluidic technology for sperm screening and in vitro fertilization. Anal Bioanal Chem 2024; 416:3717-3735. [PMID: 38189916 DOI: 10.1007/s00216-023-05120-9] [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: 11/02/2023] [Revised: 12/09/2023] [Accepted: 12/19/2023] [Indexed: 01/09/2024]
Abstract
About 18% of reproductive-age adults worldwide are affected by infertility. In vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) are widely used assisted reproductive technologies (ARTs) aimed at improving clinical outcomes. Efficient and noninvasive selection and isolation of highly motile sperm with intact DNA are essential for the success of IVF and ICSI and can potentially impact the therapeutic efficacy and the health of the offspring. Compared to traditional methods, microfluidic technology offers significant advantages such as low sample consumption, high efficiency, minimal damage, high integration, similar microenvironment, and high automation, providing a new platform for ARTs. Here, we review the current situation of microfluidic technology in the field of sperm motility screening and evaluation and IVF research. First, we focus on the working principle, structural design, and screening results of sperm selection microfluidic platforms. We then highlight how the multiple steps of the IVF process can be facilitated and integrated into a microfluidic chip, including oocyte capture, sperm collection and isolation, sperm sorting, fertilization, and embryo culture. Ultimately, we summarize how microfluidics can complement and optimize current sperm sorting and IVF protocols, and challenges and possible solutions are discussed.
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Affiliation(s)
- Jingtong Ma
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
| | - Qianlin Xie
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
| | - Yusongjia Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
| | - Qirui Xiao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
| | - Xiaoyu Liu
- Department of Obstetrics and Gynaecology, General Hospital of Northern Theater Command, Shenyang, 110003, China.
| | - Chong Qiao
- Department of Obstetrics and Gynecology of Shengjing Hospital of China Medical University, Shenyang, 110022, China.
- Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Shenyang, 110022, China.
| | - Ye Tian
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
- Foshan Graduate School of Innovation, Northeastern University, Foshan, 528300, China.
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5
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Brisard BM, Cashwell KD, Stewart SM, Harrison LM, Charles AC, Dennis CV, Henslee IR, Carrow EL, Belcher HA, Bhowmick D, Vos P, Bier M, Hart DM, Schmidt CA. "Modeling Diffusive Search by Non-Adaptive Sperm: Empirical and Computational Insights". BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.17.599386. [PMID: 38948799 PMCID: PMC11212867 DOI: 10.1101/2024.06.17.599386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
During fertilization, mammalian sperm undergo a winnowing selection process that reduces the candidate pool of potential fertilizers from ~106-1011 cells to 101-102 cells (depending on the species). Classical sperm competition theory addresses the positive or 'stabilizing' selection that acts on sperm phenotypes within populations of organisms but does not strictly address the developmental consequences of sperm traits among individual organisms that are under purifying selection during fertilization. It is the latter that is of utmost concern for improving assisted reproductive technologies (ART) because 'low fitness' sperm may be inadvertently used for fertilization during interventions that rely heavily on artificial sperm selection, such as intracytoplasmic sperm injection (ICSI). Importantly, some form of sperm selection is used in nearly all forms of ART (e.g., differential centrifugation, swim-up, or hyaluronan binding assays, etc.). To date, there is no unifying quantitative framework (i.e., theory of sperm selection) that synthesizes causal mechanisms of selection with observed natural variation in individual sperm traits. In this report, we reframe the physiological function of sperm as a collective diffusive search process and develop multi-scale computational models to explore the causal dynamics that constrain sperm 'fitness' during fertilization. Several experimentally useful concepts are developed, including a probabilistic measure of sperm 'fitness' as well as an information theoretic measure of the magnitude of sperm selection, each of which are assessed under systematic increases in microenvironmental selective pressure acting on sperm motility patterns.
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Affiliation(s)
| | | | | | | | | | | | - Ivie R. Henslee
- Department of Biology, East Carolina University, Greenville NC
| | - Ethan L. Carrow
- Department of Biology, East Carolina University, Greenville NC
| | - Heather A. Belcher
- Department of Anatomy and Cell Biology, Brody School of Medicine, East Carolina University
| | - Debajit Bhowmick
- Flow Cytometry Core Facility, Brody School of Medicine, East Carolina University
| | - Paul Vos
- Department of Public Health, East Carolina University, Greenville NC
| | - Martin Bier
- Department of Physics, East Carolina University, Greenville NC
| | - David M. Hart
- Department of Computer Science, East Carolina University, Greenville, NC
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6
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García-Vázquez FA. Artificial intelligence and porcine breeding. Anim Reprod Sci 2024:107538. [PMID: 38926001 DOI: 10.1016/j.anireprosci.2024.107538] [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: 03/29/2024] [Revised: 06/13/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024]
Abstract
Livestock management is evolving into a new era, characterized by the analysis of vast quantities of data (Big Data) collected from both traditional breeding methods and new technologies such as sensors, automated monitoring system, and advanced analytics. Artificial intelligence (A-In), which refers to the capability of machines to mimic human intelligence, including subfields like machine learning and deep learning, is playing a pivotal role in this transformation. A wide array of A-In techniques, successfully employed in various industrial and scientific contexts, are now being integrated into mainstream livestock management practices. In the case of swine breeding, while traditional methods have yielded considerable success, the increasing amount of information requires the adoption of new technologies such as A-In to drive productivity, enhance animal welfare, and reduce environmental impact. Current findings suggest that these techniques have the potential to match or exceed the performance of traditional methods, often being more scalable in terms of efficiency and sustainability within the breeding industry. This review provides insights into the application of A-In in porcine breeding, from the perspectives of both sows (including welfare and reproductive management) and boars (including semen quality and health), and explores new approaches which are already being applied in other species.
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Affiliation(s)
- Francisco A García-Vázquez
- Departamento de Fisiología, Facultad de Veterinaria, Campus de Excelencia Mare Nostrum, Universidad de Murcia, Murcia 30100, Spain; Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Murcia, Spain.
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7
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Chow DJX, Tan TCY, Upadhya A, Lim M, Dholakia K, Dunning KR. Viewing early life without labels: optical approaches for imaging the early embryo†. Biol Reprod 2024; 110:1157-1174. [PMID: 38647415 PMCID: PMC11180623 DOI: 10.1093/biolre/ioae062] [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: 01/28/2024] [Revised: 03/26/2024] [Accepted: 04/18/2024] [Indexed: 04/25/2024] Open
Abstract
Embryo quality is an important determinant of successful implantation and a resultant live birth. Current clinical approaches for evaluating embryo quality rely on subjective morphology assessments or an invasive biopsy for genetic testing. However, both approaches can be inherently inaccurate and crucially, fail to improve the live birth rate following the transfer of in vitro produced embryos. Optical imaging offers a potential non-invasive and accurate avenue for assessing embryo viability. Recent advances in various label-free optical imaging approaches have garnered increased interest in the field of reproductive biology due to their ability to rapidly capture images at high resolution, delivering both morphological and molecular information. This burgeoning field holds immense potential for further development, with profound implications for clinical translation. Here, our review aims to: (1) describe the principles of various imaging systems, distinguishing between approaches that capture morphological and molecular information, (2) highlight the recent application of these technologies in the field of reproductive biology, and (3) assess their respective merits and limitations concerning the capacity to evaluate embryo quality. Additionally, the review summarizes challenges in the translation of optical imaging systems into routine clinical practice, providing recommendations for their future development. Finally, we identify suitable imaging approaches for interrogating the mechanisms underpinning successful embryo development.
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Affiliation(s)
- Darren J X Chow
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
| | - Tiffany C Y Tan
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
| | - Avinash Upadhya
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
- School of Biological Sciences, The University of Adelaide, Adelaide, Australia
| | - Megan Lim
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
- School of Biological Sciences, The University of Adelaide, Adelaide, Australia
| | - Kishan Dholakia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
- School of Biological Sciences, The University of Adelaide, Adelaide, Australia
- Scottish Universities Physics Alliance, School of Physics and Astronomy, University of St Andrews, St Andrews, United Kingdom
| | - Kylie R Dunning
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
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8
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Wang J, Jin Y, Jiang A, Chen W, Shan G, Gu Y, Ming Y, Li J, Yue C, Huang Z, Librach C, Lin G, Wang X, Zhao H, Sun Y, Zhang Z. Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study. Reprod Biol Endocrinol 2024; 22:59. [PMID: 38778327 PMCID: PMC11110326 DOI: 10.1186/s12958-024-01232-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment of such deep learning models, different clinics use different image acquisition hardware and different sample preprocessing protocols, raising the concern over whether the reported accuracy of a deep learning model by one clinic could be reproduced in another clinic. Here we aim to investigate the effect of each imaging factor on the generalizability of object detection models, using sperm analysis as a pilot example. METHODS Ablation studies were performed using state-of-the-art models for detecting human sperm to quantitatively assess how model precision (false-positive detection) and recall (missed detection) were affected by imaging magnification, imaging mode, and sample preprocessing protocols. The results led to the hypothesis that the richness of image acquisition conditions in a training dataset deterministically affects model generalizability. The hypothesis was tested by first enriching the training dataset with a wide range of imaging conditions, then validated through internal blind tests on new samples and external multi-center clinical validations. RESULTS Ablation experiments revealed that removing subsets of data from the training dataset significantly reduced model precision. Removing raw sample images from the training dataset caused the largest drop in model precision, whereas removing 20x images caused the largest drop in model recall. by incorporating different imaging and sample preprocessing conditions into a rich training dataset, the model achieved an intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.94-0.99) for precision, and an ICC of 0.97 (95% CI: 0.93-0.99) for recall. Multi-center clinical validation showed no significant differences in model precision or recall across different clinics and applications. CONCLUSIONS The results validated the hypothesis that the richness of data in the training dataset is a key factor impacting model generalizability. These findings highlight the importance of diversity in a training dataset for model evaluation and suggest that future deep learning models in andrology and reproductive medicine should incorporate comprehensive feature sets for enhanced generalizability across clinics.
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Affiliation(s)
- Jiaqi Wang
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
| | - Yufei Jin
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
| | - Aojun Jiang
- Department of Mechanical Engineering, University of Toronto, Toronto, Canada
| | - Wenyuan Chen
- Department of Mechanical Engineering, University of Toronto, Toronto, Canada
| | - Guanqiao Shan
- Department of Mechanical Engineering, University of Toronto, Toronto, Canada
| | - Yifan Gu
- Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, China
- Reproductive & Genetic Hospital of Citic-Xiangya, Changsha, China
| | - Yue Ming
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, China
| | - Jichang Li
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, China
| | - Chunfeng Yue
- Suzhou Boundless Medical Technology Ltd., Co., Suzhou, China
| | - Zongjie Huang
- Suzhou Boundless Medical Technology Ltd., Co., Suzhou, China
| | | | - Ge Lin
- Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, China
- Reproductive & Genetic Hospital of Citic-Xiangya, Changsha, China
| | - Xibu Wang
- The 3rd Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Huan Zhao
- The 3rd Affiliated Hospital of Shenzhen University, Shenzhen, China.
| | - Yu Sun
- Department of Mechanical Engineering, University of Toronto, Toronto, Canada.
- Department of Computer Science, University of Toronto, Toronto, Canada.
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada.
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada.
| | - Zhuoran Zhang
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China.
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Li X, Zhu H, Gu B, Yao C, Gu Y, Xu W, Zhang J, He J, Liu X, Li D. Advancing Intelligent Organ-on-a-Chip Systems with Comprehensive In Situ Bioanalysis. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305268. [PMID: 37688520 DOI: 10.1002/adma.202305268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/03/2023] [Indexed: 09/11/2023]
Abstract
In vitro models are essential to a broad range of biomedical research, such as pathological studies, drug development, and personalized medicine. As a potentially transformative paradigm for 3D in vitro models, organ-on-a-chip (OOC) technology has been extensively developed to recapitulate sophisticated architectures and dynamic microenvironments of human organs by applying the principles of life sciences and leveraging micro- and nanoscale engineering capabilities. A pivotal function of OOC devices is to support multifaceted and timely characterization of cultured cells and their microenvironments. However, in-depth analysis of OOC models typically requires biomedical assay procedures that are labor-intensive and interruptive. Herein, the latest advances toward intelligent OOC (iOOC) systems, where sensors integrated with OOC devices continuously report cellular and microenvironmental information for comprehensive in situ bioanalysis, are examined. It is proposed that the multimodal data in iOOC systems can support closed-loop control of the in vitro models and offer holistic biomedical insights for diverse applications. Essential techniques for establishing iOOC systems are surveyed, encompassing in situ sensing, data processing, and dynamic modulation. Eventually, the future development of iOOC systems featuring cross-disciplinary strategies is discussed.
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Affiliation(s)
- Xiao Li
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Hui Zhu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Bingsong Gu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Cong Yao
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Yuyang Gu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Wangkai Xu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jia Zhang
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jiankang He
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Xinyu Liu
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, M5S 3G8, Canada
| | - Dichen Li
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
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10
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Bouloorchi Tabalvandani M, Saeidpour Z, Habibi Z, Javadizadeh S, Firoozabadi SA, Badieirostami M. Microfluidics as an emerging paradigm for assisted reproductive technology: A sperm separation perspective. Biomed Microdevices 2024; 26:23. [PMID: 38652182 DOI: 10.1007/s10544-024-00705-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/08/2024] [Indexed: 04/25/2024]
Abstract
Millions of people are subject to infertility worldwide and one in every six people, regardless of gender, experiences infertility at some period in their life, according to the World Health Organization. Assisted reproductive technologies are defined as a set of procedures that can address the infertility issue among couples, culminating in the alleviation of the condition. However, the costly conventional procedures of assisted reproduction and the inherent vagaries of the processes involved represent a setback for its successful implementation. Microfluidics, an emerging tool for processing low-volume samples, have recently started to play a role in infertility diagnosis and treatment. Given its host of benefits, including manipulating cells at the microscale, repeatability, automation, and superior biocompatibility, microfluidics have been adopted for various procedures in assisted reproduction, ranging from sperm sorting and analysis to more advanced processes such as IVF-on-a-chip. In this review, we try to adopt a more holistic approach and cover different uses of microfluidics for a variety of applications, specifically aimed at sperm separation and analysis. We present various sperm separation microfluidic techniques, categorized as natural and non-natural methods. A few of the recent developments in on-chip fertilization are also discussed.
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Affiliation(s)
| | - Zahra Saeidpour
- MEMS Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 1439957131, Iran
| | - Zahra Habibi
- MEMS Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 1439957131, Iran
| | - Saeed Javadizadeh
- MEMS Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 1439957131, Iran
| | - Seyed Ahmadreza Firoozabadi
- MEMS Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 1439957131, Iran
| | - Majid Badieirostami
- MEMS Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 1439957131, Iran.
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11
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Schmeis Arroyo V, Iosa M, Antonucci G, De Bartolo D. Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature. Healthcare (Basel) 2024; 12:781. [PMID: 38610202 PMCID: PMC11011284 DOI: 10.3390/healthcare12070781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/27/2024] [Accepted: 03/31/2024] [Indexed: 04/14/2024] Open
Abstract
Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%.
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Affiliation(s)
- Vivian Schmeis Arroyo
- Department of Psychology, University Sapienza of Rome, 00185 Rome, Italy (M.I.); (G.A.)
| | - Marco Iosa
- Department of Psychology, University Sapienza of Rome, 00185 Rome, Italy (M.I.); (G.A.)
- Santa Lucia Foundation, Scientific Institute for Research, Hospitalization and Health Care (IRCCS), 00179 Rome, Italy
| | - Gabriella Antonucci
- Department of Psychology, University Sapienza of Rome, 00185 Rome, Italy (M.I.); (G.A.)
- Santa Lucia Foundation, Scientific Institute for Research, Hospitalization and Health Care (IRCCS), 00179 Rome, Italy
| | - Daniela De Bartolo
- Santa Lucia Foundation, Scientific Institute for Research, Hospitalization and Health Care (IRCCS), 00179 Rome, Italy
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12
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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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13
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Kimmins S, Anderson RA, Barratt CLR, Behre HM, Catford SR, De Jonge CJ, Delbes G, Eisenberg ML, Garrido N, Houston BJ, Jørgensen N, Krausz C, Lismer A, McLachlan RI, Minhas S, Moss T, Pacey A, Priskorn L, Schlatt S, Trasler J, Trasande L, Tüttelmann F, Vazquez-Levin MH, Veltman JA, Zhang F, O'Bryan MK. Frequency, morbidity and equity - the case for increased research on male fertility. Nat Rev Urol 2024; 21:102-124. [PMID: 37828407 DOI: 10.1038/s41585-023-00820-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/31/2023] [Indexed: 10/14/2023]
Abstract
Currently, most men with infertility cannot be given an aetiology, which reflects a lack of knowledge around gamete production and how it is affected by genetics and the environment. A failure to recognize the burden of male infertility and its potential as a biomarker for systemic illness exists. The absence of such knowledge results in patients generally being treated as a uniform group, for whom the strategy is to bypass the causality using medically assisted reproduction (MAR) techniques. In doing so, opportunities to prevent co-morbidity are missed and the burden of MAR is shifted to the woman. To advance understanding of men's reproductive health, longitudinal and multi-national centres for data and sample collection are essential. Such programmes must enable an integrated view of the consequences of genetics, epigenetics and environmental factors on fertility and offspring health. Definition and possible amelioration of the consequences of MAR for conceived children are needed. Inherent in this statement is the necessity to promote fertility restoration and/or use the least invasive MAR strategy available. To achieve this aim, protocols must be rigorously tested and the move towards personalized medicine encouraged. Equally, education of the public, governments and clinicians on the frequency and consequences of infertility is needed. Health options, including male contraceptives, must be expanded, and the opportunities encompassed in such investment understood. The pressing questions related to male reproductive health, spanning the spectrum of andrology are identified in the Expert Recommendation.
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Affiliation(s)
- Sarah Kimmins
- Department of Pharmacology and Therapeutics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- The Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Quebec, Canada
- The Département de Pathologie et Biologie Cellulaire, Université de Montréal, Montreal, Quebec, Canada
| | - Richard A Anderson
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - Christopher L R Barratt
- Division of Systems Medicine, School of Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Hermann M Behre
- Center for Reproductive Medicine and Andrology, University Hospital, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Sarah R Catford
- Hudson Institute of Medical Research, Melbourne, Victoria, Australia
- Department of Obstetrics and Gynaecology, The Royal Women's Hospital, Melbourne, Victoria, Australia
| | | | - Geraldine Delbes
- Institut National de la Recherche Scientifique, Centre Armand-Frappier Sante Biotechnologie, Laval, Quebec, Canada
| | - Michael L Eisenberg
- Department of Urology and Obstetrics and Gynecology, Stanford University, Stanford, CA, USA
| | - Nicolas Garrido
- IVI Foundation, Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Brendan J Houston
- School of BioSciences and Bio21 Institute, The University of Melbourne, Parkville, Melbourne, Australia
| | - Niels Jørgensen
- Department of Growth and Reproduction, International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Csilla Krausz
- Department of Experimental and Clinical Biomedical Sciences, 'Mario Serio', University of Florence, University Hospital of Careggi Florence, Florence, Italy
| | - Ariane Lismer
- Department of Pharmacology and Therapeutics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Robert I McLachlan
- Hudson Institute of Medical Research and the Department of Obstetrics and Gynaecology, Monash University, Melbourne, Australia
- Monash IVF Group, Richmond, Victoria, Australia
| | - Suks Minhas
- Department of Surgery and Cancer Imperial, London, UK
| | - Tim Moss
- Healthy Male and the Department of Obstetrics and Gynaecology, Monash University, Melbourne, Victoria, Australia
| | - Allan Pacey
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Lærke Priskorn
- Department of Growth and Reproduction, International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Stefan Schlatt
- Centre for Reproductive Medicine and Andrology, University of Münster, Münster, Germany
| | - Jacquetta Trasler
- Departments of Paediatrics, Human Genetics and Pharmacology & Therapeutics, McGill University and Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - Leonardo Trasande
- Center for the Investigation of Environmental Hazards, Department of Paediatrics, NYU Grossman School of Medicine, New York, NY, USA
| | - Frank Tüttelmann
- Institute of Reproductive Genetics, University of Münster, Münster, Germany
| | - Mónica Hebe Vazquez-Levin
- Instituto de Biología y Medicina Experimental, Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina, Fundación IBYME, Buenos Aires, Argentina
| | - Joris A Veltman
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Feng Zhang
- Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China
| | - Moira K O'Bryan
- School of BioSciences and Bio21 Institute, The University of Melbourne, Parkville, Melbourne, Australia.
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14
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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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15
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Lewandowska E, Węsierski D, Mazur-Milecka M, Liss J, Jezierska A. Ensembling noisy segmentation masks of blurred sperm images. Comput Biol Med 2023; 166:107520. [PMID: 37804777 DOI: 10.1016/j.compbiomed.2023.107520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 08/11/2023] [Accepted: 09/19/2023] [Indexed: 10/09/2023]
Abstract
BACKGROUND Sperm tail morphology and motility have been demonstrated to be important factors in determining sperm quality for in vitro fertilization. However, many existing computer-aided sperm analysis systems leave the sperm tail out of the analysis, as detecting a few tail pixels is challenging. Moreover, some publicly available datasets for classifying morphological defects contain images limited only to the sperm head. This study focuses on the segmentation of full sperm, which consists of the head and tail parts, and appear alone and in groups. METHODS We re-purpose the Feature Pyramid Network to ensemble an input image with multiple masks from state-of-the-art segmentation algorithms using a scale-specific cross-attention module. We normalize homogeneous backgrounds for improved training. The low field depth of microscopes blurs the images, easily confusing human raters in discerning minuscule sperm from large backgrounds. We thus propose evaluation protocols for scoring segmentation models trained on imbalanced data and noisy ground truth. RESULTS The neural ensembling of noisy segmentation masks outperforms all single, state-of-the-art segmentation algorithms in full sperm segmentation. Human raters agree more on the head than tail masks. The algorithms also segment the head better than the tail. CONCLUSIONS The extensive evaluation of state-of-the-art segmentation algorithms shows that full sperm segmentation is challenging. We release the SegSperm dataset of images from Intracytoplasmic Sperm Injection procedures to spur further progress on full sperm segmentation with noisy and imbalanced ground truth. The dataset is publicly available at https://doi.org/10.34808/6wm7-1159.
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Affiliation(s)
| | - Daniel Węsierski
- Cameras and Algorithms Lab, Gdańsk University of Technology, Poland; Multimedia Systems Department, Faculty of Electronics, Telecommunication, and Informatics, Gdańsk University of Technology, Poland
| | - Magdalena Mazur-Milecka
- Department of Biomedical Engineering, Faculty of Electronics, Telecommunications, and Informatics, Gdańsk University of Technology, Poland
| | - Joanna Liss
- Invicta Research and Development Center, Sopot, Poland; Department of Medical Biology and Genetics, University of Gdańsk, Poland
| | - Anna Jezierska
- Cameras and Algorithms Lab, Gdańsk University of Technology, Poland; Department of Biomedical Engineering, Faculty of Electronics, Telecommunications, and Informatics, Gdańsk University of Technology, Poland; Department of Modelling and Optimization of Dynamical Systems, Systems Research Institute Warsaw, Poland.
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16
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Cardoso E, Mathias MDL, Monarca RI, Gabriel SI. Assessing Optimal Cell Counts in Sperm Shape Abnormality Assays in Rodents. Animals (Basel) 2023; 13:3324. [PMID: 37958079 PMCID: PMC10649842 DOI: 10.3390/ani13213324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 10/01/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
Rodents have been the preferred models for the evaluation of the toxicity of pollutants and drugs and their genotoxic effects, including sperm shape abnormalities. The scientific literature is dominated by studies conducted with model animals in laboratory conditions, but a generally accepted and standardized protocol addressing the optimal number of sperm cells to count is still lacking. In this study, we reviewed the literature regarding the number of counted sperm cells in such assessments, published from 1969 to 2023. To infer the number of counts providing the best cost/benefit regarding the robustness of the assay results, a new dataset involving the analysis of two populations of wild rodents was produced. We evaluated the frequency of sperm shape abnormalities in a total of 50 wild brown rats (Rattus norvegicus) captured in two port cities, aiming to detect the impact of differential sperm cell counts in the obtained results. During necropsy, the fresh epididymis tail of adult male rats was excised, and sperm cells were fixated in slides. For each animal, a total of 300, 500, 1000, and 2000 cells were sequentially counted, and head abnormalities were registered. Counting 300 sperm cells failed to detect significant differences between groups and 500 counts resulted in marginally significant differences. Only when 1000 or 2000 sperm cells were counted, significant differences emerged between groups. We propose that studies addressing sperm shape abnormalities should standardize counts to an optimal value of 1000 cells per animal, warranting robust statistical results while providing the best compromise concerning labor time.
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Affiliation(s)
- Elizandra Cardoso
- CESAM—Centro de Estudos do Ambiente e do Mar, Departamento de Biologia Animal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal; (E.C.); (M.d.L.M.); (R.I.M.)
| | - Maria da Luz Mathias
- CESAM—Centro de Estudos do Ambiente e do Mar, Departamento de Biologia Animal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal; (E.C.); (M.d.L.M.); (R.I.M.)
| | - Rita I. Monarca
- CESAM—Centro de Estudos do Ambiente e do Mar, Departamento de Biologia Animal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal; (E.C.); (M.d.L.M.); (R.I.M.)
| | - Sofia I. Gabriel
- CESAM—Centro de Estudos do Ambiente e do Mar, Departamento de Biologia Animal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal; (E.C.); (M.d.L.M.); (R.I.M.)
- Departamento de Biologia da Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
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17
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Lustgarten Guahmich N, Borini E, Zaninovic N. Improving outcomes of assisted reproductive technologies using artificial intelligence for sperm selection. Fertil Steril 2023; 120:729-734. [PMID: 37307892 DOI: 10.1016/j.fertnstert.2023.06.009] [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: 06/06/2023] [Accepted: 06/06/2023] [Indexed: 06/14/2023]
Abstract
Within the field of assisted reproductive technology, artificial intelligence has become an attractive tool for potentially improving success rates. Recently, artificial intelligence-based tools for sperm evaluation and selection during intracytoplasmic sperm injection (ICSI) have been explored, mainly to improve fertilization outcomes and decrease variability within ICSI procedures. Although significant advances have been achieved in developing algorithms that track and rank single sperm in real-time during ICSI, the clinical benefits these might have in improving pregnancy rates from a single assisted reproductive technology cycle remain to be established.
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Affiliation(s)
- Nicole Lustgarten Guahmich
- Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York
| | - Elena Borini
- Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York
| | - Nikica Zaninovic
- Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York.
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18
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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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19
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Choucair F, Avella M. Basic, translational and clinical studies in reproductive medicine and clinical reproductive sciences. J Transl Med 2023; 21:534. [PMID: 37563678 PMCID: PMC10416518 DOI: 10.1186/s12967-023-04108-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023] Open
Affiliation(s)
- Fadi Choucair
- Reproductive Medicine Unit, Sidra Medicine, Doha, Qatar.
| | - Matteo Avella
- Maternal & Child Health Division, Sidra Medicine, Doha, Qatar.
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20
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Thambawita V, Hicks SA, Storås AM, Nguyen T, Andersen JM, Witczak O, Haugen TB, Hammer HL, Halvorsen P, Riegler MA. VISEM-Tracking, a human spermatozoa tracking dataset. Sci Data 2023; 10:260. [PMID: 37156762 PMCID: PMC10167330 DOI: 10.1038/s41597-023-02173-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 04/20/2023] [Indexed: 05/10/2023] Open
Abstract
A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-aided sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet semen preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa.
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Affiliation(s)
| | - Steven A Hicks
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | - Andrea M Storås
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Oslo Metropolitan University, Oslo, Norway
| | - Thu Nguyen
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | | | | | | | - Hugo L Hammer
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Oslo Metropolitan University, Oslo, Norway
| | - Pål Halvorsen
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Oslo Metropolitan University, Oslo, Norway
| | - Michael A Riegler
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Oslo Metropolitan University, Oslo, Norway
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21
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Golan R, Reddy R, Muthigi A, Ramasamy R. Artificial intelligence in academic writing: a paradigm-shifting technological advance. Nat Rev Urol 2023:10.1038/s41585-023-00746-x. [PMID: 36829078 DOI: 10.1038/s41585-023-00746-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Affiliation(s)
- Roei Golan
- Department of Clinical Sciences, Florida State University College of Medicine, Tallahassee, FL, USA
| | - Rohit Reddy
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Akhil Muthigi
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Ranjith Ramasamy
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL, USA.
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22
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Jiang VS, Kartik D, Thirumalaraju P, Kandula H, Kanakasabapathy MK, Souter I, Dimitriadis I, Bormann CL, Shafiee H. Advancements in the future of automating micromanipulation techniques in the IVF laboratory using deep convolutional neural networks. J Assist Reprod Genet 2023; 40:251-257. [PMID: 36586006 PMCID: PMC9935764 DOI: 10.1007/s10815-022-02685-9] [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: 06/17/2022] [Accepted: 12/06/2022] [Indexed: 01/01/2023] Open
Abstract
PURPOSE To determine if deep learning artificial intelligence algorithms can be used to accurately identify key morphologic landmarks on oocytes and cleavage stage embryo images for micromanipulation procedures such as intracytoplasmic sperm injection (ICSI) or assisted hatching (AH). METHODS Two convolutional neural network (CNN) models were trained, validated, and tested over three replicates to identify key morphologic landmarks used to guide embryologists when performing micromanipulation procedures. The first model (CNN-ICSI) was trained (n = 13,992), validated (n = 1920), and tested (n = 3900) to identify the optimal location for ICSI through polar body identification. The second model (CNN-AH) was trained (n = 13,908), validated (n = 1908), and tested (n = 3888) to identify the optimal location for AH on the zona pellucida that maximizes distance from healthy blastomeres. RESULTS The CNN-ICSI model accurately identified the polar body and corresponding optimal ICSI location with 98.9% accuracy (95% CI 98.5-99.2%) with a receiver operator characteristic (ROC) with micro and macro area under the curves (AUC) of 1. The CNN-AH model accurately identified the optimal AH location with 99.41% accuracy (95% CI 99.11-99.62%) with a ROC with micro and macro AUCs of 1. CONCLUSION Deep CNN models demonstrate powerful potential in accurately identifying key landmarks on oocytes and cleavage stage embryos for micromanipulation. These findings are novel, essential stepping stones in the automation of micromanipulation procedures.
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Affiliation(s)
- Victoria S Jiang
- Division of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA
| | - Deeksha Kartik
- Division of Engineering in Medicine, Division of Renal Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Prudhvi Thirumalaraju
- Division of Engineering in Medicine, Division of Renal Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Hemanth Kandula
- Division of Engineering in Medicine, Division of Renal Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Manoj Kumar Kanakasabapathy
- Division of Engineering in Medicine, Division of Renal Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Irene Souter
- Division of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA
| | - Irene Dimitriadis
- Division of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA
| | - Charles L Bormann
- Division of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA.
| | - Hadi Shafiee
- Division of Engineering in Medicine, Division of Renal Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA.
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23
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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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24
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Shen X, Xiao T, Han W, Ye H, Zhang Y, Huang G. The bibliometric analysis of studies on intracytoplasmic sperm injection from 2002 to 2021. Front Endocrinol (Lausanne) 2023; 14:1109623. [PMID: 36998479 PMCID: PMC10043366 DOI: 10.3389/fendo.2023.1109623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/28/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND Infertility is estimated to occur in 1 out of every 4-7 couples. Intracytoplasmic sperm injection (ICSI), a type of assisted reproduction introduced in 1992, has been used across the world for almost all indications of infertility, yielding high pregnancy rates. There is a growing concern worldwide about ICSI since semen quality has declined in recent years, accompanied with the potential risks of this technology. This study aims to analyze the current status and hotspots of ICSI via a bibliometric analysis. METHODS We retrieved publications on ICSI from the Web of Science Core Collection database from 2002 to 2021. CiteSpace was used to summarize knowledge mapping of subject categories, keywords, and co-citation relationships with the strongest citation bursts. VOSviewer was used to explore co-citation and co-occurrence relationships for countries, organizations, references, authors, and keywords. RESULTS A total of 8271 publications were analyzed between 2002 and 2021. The major findings are as follows: the USA, China, Italy, Japan, and Belgium are the top five prolific countries. The Free University of Brussels, University of Copenhagen, University of Valencia, Ghent University, and the University of California San Francisco are the top five contributing organizations. Fertility and Sterility and Human Reproduction are the most productive and cited journals. The hotspot topics are risks of ICSI, oocyte preservation, live birth rate, infertile men, and embryo quality in the past two decades. CONCLUSION This study presents a research overview of ICSI from different perspectives. These findings will contribute to a better understanding of the current status of ICSI research and provide hotspots and trends for future studies.
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Affiliation(s)
- Xiaoli Shen
- Center for Reproductive Medicine, Chongqing Key Laboratory of Human Embryo Engineering, Chongqing Health Center for Women and Children, Women and Children’s Hospital of Chongqing Medical University Chongqing, Chongqing, China
| | - Tianbing Xiao
- Department of Urology, People’s Hospital of Fengjie, Chongqing, China
| | - Wei Han
- Center for Reproductive Medicine, Chongqing Key Laboratory of Human Embryo Engineering, Chongqing Health Center for Women and Children, Women and Children’s Hospital of Chongqing Medical University Chongqing, Chongqing, China
| | - Hong Ye
- Center for Reproductive Medicine, Chongqing Key Laboratory of Human Embryo Engineering, Chongqing Health Center for Women and Children, Women and Children’s Hospital of Chongqing Medical University Chongqing, Chongqing, China
| | - Yuanfeng Zhang
- Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Yuanfeng Zhang, ; Guoning Huang,
| | - Guoning Huang
- Center for Reproductive Medicine, Chongqing Key Laboratory of Human Embryo Engineering, Chongqing Health Center for Women and Children, Women and Children’s Hospital of Chongqing Medical University Chongqing, Chongqing, China
- *Correspondence: Yuanfeng Zhang, ; Guoning Huang,
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25
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Acharya D, Das DK. A novel Human Conception Optimizer for solving optimization problems. Sci Rep 2022; 12:21631. [PMID: 36517488 PMCID: PMC9751073 DOI: 10.1038/s41598-022-25031-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/23/2022] [Indexed: 12/23/2022] Open
Abstract
Computational techniques are widely used to solve complex optimization problems in different fields such as engineering, finance, biology, and so on. In this paper, the Human Conception Optimizer (HCO) is proposed as a novel metaheuristic algorithm to solve any optimization problems. The idea of this algorithm is based on some biological principles of the human conception process, such as the selective nature of cervical gel in the female reproductive system to allow only healthy sperm cells into the cervix, the guidance nature of mucus gel to help sperm track a genital tracking path towards the egg in the Fallopian tube, the asymmetric nature of flagellar movement which allows sperm cells to move in the reproductive system, the sperm hyperactivation process to make them able to fertilize an egg. Thus, the strategies pursued by the sperm in searching for the egg in the Fallopian tube are modeled mathematically. The best sperm which will meet the position of the egg will be the solution of the algorithm. The performance of the proposed HCO algorithm is examined with a set of basic benchmark test functions called IEEE CEC-2005 and IEEE CEC-2020. A comparative study is also performed between the HCO algorithm and other available algorithms. The significance of the results is verified with statistical test methods. To validate the proposed HCO algorithm, two real-world engineering optimization problems are examined. For this purpose, a complex 14 over-current relay based IEEE 8 bus distribution system is considered. With the proposed algorithm, an improvement of 50% to 60% in total relay operating times is observed comparing with some existing results for the same system. Another engineering problem of designing an optimal proportional integral derivative (PID) controller for a blower driven patient hose mechanical ventilator (MV) is examined. A significant improvement in terms of response time, settling time is observed in the MV system by comparing with existing results.
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Affiliation(s)
- Debasis Acharya
- Department of Electrical and Electronics Engineering, National Institute of Technology Nagaland, Dimapur, 797103, India
| | - Dushmanta Kumar Das
- Department of Electrical and Electronics Engineering, National Institute of Technology Nagaland, Dimapur, 797103, India.
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26
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Moretti E, Signorini C, Noto D, Corsaro R, Collodel G. The relevance of sperm morphology in male infertility. FRONTIERS IN REPRODUCTIVE HEALTH 2022; 4:945351. [PMID: 36303645 PMCID: PMC9580829 DOI: 10.3389/frph.2022.945351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/11/2022] [Indexed: 11/23/2022] Open
Abstract
This brief report concerns the role of human sperm morphology assessment in different fields of male infertility: basic research, genetics, assisted reproduction technologies, oxidative stress. One of the best methods in studying sperm morphology is transmission electron microscopy (TEM) that enables defining the concept of sperm pathology and classifying alterations in non-systematic and systematic. Non-systematic sperm defects affect head and tail in variable ratio, whereas the rare systematic defects are characterized by a particular anomaly that marks most sperm of an ejaculate. TEM analysis and fluorescence in situ hybridization represent outstanding methods in the study of sperm morphology and cytogenetic in patients with altered karyotype characterizing their semen quality before intracytoplasmic sperm injection. In recent years, the genetic investigations on systematic sperm defects, made extraordinary progress identifying candidate genes whose mutations induce morphological sperm anomalies. The question if sperm morphology has an impact on assisted fertilization outcome is debated. Nowadays, oxidative stress represents one of the most important causes of altered sperm morphology and function and can be analyzed from two points of view: 1) spermatozoa with cytoplasmic residue produce reactive oxygen species, 2) the pathologies with inflammatory/oxidative stress background cause morphological alterations. Finally, sperm morphology is also considered an important endpoint in in vitro experiments where toxic substances, drugs, antioxidants are tested. We think that the field of sperm morphology is far from being exhausted and needs other research. This parameter can be still considered a valuable indicator of sperm dysfunction both in basic and clinical research.
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27
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Sarabi MR, Yigci D, Alseed MM, Mathyk BA, Ata B, Halicigil C, Tasoglu S. Disposable Paper-Based Microfluidics for Fertility Testing. iScience 2022; 25:104986. [PMID: 36105592 PMCID: PMC9465368 DOI: 10.1016/j.isci.2022.104986] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Fifteen percent of couples of reproductive age suffer from infertility globally and the burden of infertility disproportionately impacts residents of developing countries. Assisted reproductive technologies (ARTs), including in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI), have been successful in overcoming various reasons for infertility including borderline and severe male factor infertility which consists of 20%–30% of all infertile cases. Approximately half of male infertility cases stem from suboptimal sperm parameters. Therefore, healthy/normal sperm enrichment and sorting remains crucial in advancing reproductive medicine. Microfluidic technologies have emerged as promising tools to develop in-home rapid fertility tests and point-of-care (POC) diagnostic tools. Here, we review advancements in fabrication methods for paper-based microfluidic devices and their emerging fertility testing applications assessing sperm concentration, sperm motility, sperm DNA analysis, and other sperm functionalities, and provide a glimpse into future directions for paper-based fertility microfluidic systems. Paper-based technologies are emerging to develop in-home rapid fertility tests Fabrication methods for paper-based microfluidic devices are presented Emerging disposable paper-based fertility testing applications are reviewed
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Affiliation(s)
| | - Defne Yigci
- School of Medicine, Koç University, Istanbul, Türkiye 34450
| | - M. Munzer Alseed
- Boğaziçi Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Türkiye 34684
| | - Begum Aydogan Mathyk
- Department of Obstetrics and Gynecology, HCA Healthcare, University of South Florida Morsani College of Medicine GME, Brandon Regional Hospital, Florida 33511, USA
| | - Baris Ata
- School of Medicine, Koç University, Istanbul, Türkiye 34450
- ART Fertility Clinics, Dubai, United Arab Emirates 337-1500
| | - Cihan Halicigil
- Yale School of Medicine, Yale University, Connecticut 06520, USA
| | - Savas Tasoglu
- School of Mechanical Engineering, Koç University, Istanbul, Türkiye 34450
- Boğaziçi Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Türkiye 34684
- Koç University Translational Medicine Research Center (KUTTAM), Koç University, Istanbul, Türkiye 34450
- Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Istanbul, Türkiye 34450
- Koç University Is Bank Artificial Intelligence Lab (KUIS AI Lab), Koç University, Istanbul, Türkiye 34450
- Corresponding author
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28
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Nosrati R. Lab on a chip devices for fertility: from proof-of-concept to clinical impact. LAB ON A CHIP 2022; 22:1680-1689. [PMID: 35417508 DOI: 10.1039/d1lc01144h] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Microfluidics offers tremendous opportunities to understand the underlying biology of fertilization at the single-cell level and improve infertility management, however, its true clinical impact is yet to be realized. Lab-on-a-chip devices have generally failed to diffuse into clinical practice due to issues associated with their translation or their practicality and performance in clinical settings. In this perspective, I reflect on how the full potential of microfluidic technologies for fertility can be realized by considering regulatory and manufacturing considerations at the development stage and by redefining our developmental goals to directly target the ultimate clinical needs. I also challenge the common rationale around developing technologies for infertility treatment based on reducing cost and complexity in operation as the ultimate outcome is invaluable, human life.
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Affiliation(s)
- Reza Nosrati
- Department of Mechanical and Aerospace Engineering, Monash University, Clayton Campus, VIC 3800, Australia.
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29
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Barratt CLR, Wang C, Baldi E, Toskin I, Kiarie J, Lamb DJ. What advances may the future bring to the diagnosis, treatment, and care of male sexual and reproductive health? Fertil Steril 2022; 117:258-267. [PMID: 35125173 PMCID: PMC8877074 DOI: 10.1016/j.fertnstert.2021.12.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 12/15/2021] [Accepted: 12/15/2021] [Indexed: 12/11/2022]
Abstract
Over the past 40 years, since the publication of the original WHO Laboratory Manual for the Examination and Processing of Human Semen, the laboratory methods used to evaluate semen markedly changed and benefited from improved precision and accuracy, as well as the development of new tests and improved, standardized methodologies. Herein, we present the impact of the changes put forth in the sixth edition together with our views of evolving technologies that may change the methods used for the routine semen analysis, up-and-coming areas for the development of new procedures, and diagnostic approaches that will help to extend the often-descriptive interpretations of several commonly performed semen tests that promise to provide etiologies for the abnormal semen parameters observed. As we look toward the publication of the seventh edition of the manual in approximately 10 years, we describe potential advances that could markedly impact the field of andrology in the future.
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Affiliation(s)
- Christopher L R Barratt
- Division of Systems Medicine, University of Dundee Medical School, Ninewells Hospital, Dundee, Scotland.
| | - Christina Wang
- Clinical and Translational Science Institute, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California
| | - Elisabetta Baldi
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Igor Toskin
- Department of Sexual and Reproductive Health and Research, World Health Organization, Geneva, Switzerland
| | - James Kiarie
- Department of Sexual and Reproductive Health and Research, World Health Organization, Geneva, Switzerland
| | - Dolores J Lamb
- The James Buchanan Brady Foundation Department of Urology, Center for Reproductive Genomics and Englander Institute for Personalized Medicine, Weill Cornell Medical College, New York, New York
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30
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Nosrati R, Sinton D. How to select ICSI-viable sperm from the most challenging samples. Nat Rev Urol 2021; 19:135-136. [PMID: 34845346 DOI: 10.1038/s41585-021-00546-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Reza Nosrati
- Department of Mechanical and Aerospace Engineering, Monash University, Clayton Campus, Melbourne, Victoria, Australia.
| | - David Sinton
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.
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31
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Punjani N, Kang C, Lee RK, Goldstein M, Li PS. Technological Advancements in Male Infertility Microsurgery. J Clin Med 2021; 10:jcm10184259. [PMID: 34575370 PMCID: PMC8471566 DOI: 10.3390/jcm10184259] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/12/2021] [Accepted: 09/15/2021] [Indexed: 11/16/2022] Open
Abstract
There have been significant advancements in male infertility microsurgery over time, and there continues to be significant promise for new and emerging techniques, technologies, and methodologies. In this review, we discuss the history of male infertility and the evolution of microsurgery, the essential role of education and training in male infertility microsurgery, and new technologies in this space. We also review the potentially important role of artificial intelligence (AI) in male infertility and microsurgery.
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Affiliation(s)
- Nahid Punjani
- Center for Male Reproductive Medicine and Microsurgery, Cornell Institute for Reproductive Medicine, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (N.P.); (C.K.); (M.G.)
- Department of Urology, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA;
| | - Caroline Kang
- Center for Male Reproductive Medicine and Microsurgery, Cornell Institute for Reproductive Medicine, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (N.P.); (C.K.); (M.G.)
- Department of Urology, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA;
| | - Richard K. Lee
- Department of Urology, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA;
| | - Marc Goldstein
- Center for Male Reproductive Medicine and Microsurgery, Cornell Institute for Reproductive Medicine, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (N.P.); (C.K.); (M.G.)
- Department of Urology, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA;
| | - Philip S. Li
- Center for Male Reproductive Medicine and Microsurgery, Cornell Institute for Reproductive Medicine, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (N.P.); (C.K.); (M.G.)
- Department of Urology, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA;
- Correspondence:
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