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Dehghan S, Rabiei R, Choobineh H, Maghooli K, Nazari M, Vahidi-Asl M. Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction. PLoS One 2024; 19:e0310829. [PMID: 39392832 PMCID: PMC11469510 DOI: 10.1371/journal.pone.0310829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 09/08/2024] [Indexed: 10/13/2024] Open
Abstract
INTRODUCTION IVF is a widely-used assisted reproductive technology with a consistent success rate of around 30%, and improving this rate is crucial due to emotional, financial, and health-related implications for infertile couples. This study aimed to develop a model for predicting IVF outcome by comparing five machine-learning techniques. METHOD The research approached five prominent machine learning algorithms, including Random Forest, Artificial Neural Network (ANN), Support Vector Machine (SVM), Recursive Partitioning and Regression Trees (RPART), and AdaBoost, in the context of IVF success prediction. The study also incorporated GA as a feature selection method to enhance the predictive models' robustness. RESULTS Findings demonstrate that AdaBoost, particularly when combined with GA feature selection, achieved the highest accuracy rate of 89.8%. Using GA, Random Forest also demonstrated strong performance, achieving an accuracy rate of 87.4%. Genetic Algorithm significantly improved the performance of all classifiers, emphasizing the importance of feature selection. Ten crucial features, including female age, AMH, endometrial thickness, sperm count, and various indicators of oocyte and embryo quality, were identified as key determinants of IVF success. CONCLUSION These findings underscore the potential of machine learning and feature selection techniques to assist IVF clinicians in providing more accurate predictions, enabling tailored treatment plans for each patient. Future research and validation can further enhance the practicality and reliability of these predictive models in clinical IVF practice.
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Affiliation(s)
- Shirin Dehghan
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Rabiei
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Choobineh
- Department of Laboratory Sciences, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering Science and Research Branch Islamic Azad University, Tehran, Iran
| | - Mozhdeh Nazari
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mojtaba Vahidi-Asl
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
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Yao MWM, Jenkins J, Nguyen ET, Swanson T, Menabrito M. Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist. Semin Reprod Med 2024. [PMID: 39379046 DOI: 10.1055/s-0044-1791536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2024]
Abstract
Although in vitro fertilization (IVF) has become an extremely effective treatment option for infertility, there is significant underutilization of IVF by patients who could benefit from such treatment. In order for patients to choose to consider IVF treatment when appropriate, it is critical for them to be provided with an accurate, understandable IVF prognosis. Machine learning (ML) can meet the challenge of personalized prognostication based on data available prior to treatment. The development, validation, and deployment of ML prognostic models and related patient counseling report delivery require specialized human and platform expertise. This review article takes a pragmatic approach to review relevant reports of IVF prognostic models and draws from extensive experience meeting patients' and providers' needs with the development of data and model pipelines to implement validated ML models at scale, at the point-of-care. Requirements of using ML-based IVF prognostics at point-of-care will be considered alongside clinical ML implementation factors critical for success. Finally, we discuss health, social, and economic objectives that may be achieved by leveraging combined human expertise and ML prognostics to expand fertility care access and advance health and social good.
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Wang X, Tian PZ, Zhao YJ, Lu J, Dong CY, Zhang CL. The association between female age and pregnancy outcomes in patients receiving first elective single embryo transfer cycle: a retrospective cohort study. Sci Rep 2024; 14:19216. [PMID: 39160203 PMCID: PMC11333704 DOI: 10.1038/s41598-024-70249-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 08/14/2024] [Indexed: 08/21/2024] Open
Abstract
This study aimed to explore the relationship between female age and pregnancy outcomes in patients undergoing their first elective single embryo transfer (eSET) of in vitro fertilization (IVF) cycles. The retrospective cohort study encompassed 7089 IVF/intracytoplasmic sperm injection (ICSI) patients of the Reproductive Medicine Center, Henan Provincial Peoples' Hospital of China, from September 1, 2016, to May 31, 2022. Patients all received the first eSET in their IVF/ICSI cycles. A generalized additive model (GAM) was employed to examine the the dose-response correlation between age and pregnancy outcomes, namely the clinical pregnancy rate (CPR) and ongoing pregnancy rate (OPR). Logistic regression model was employed to ascertain the correlation between the CPR/OPR and age. The study cohort has an average age of 30.74; 3843 patients got clinical pregnancy rate of 61.40% and ongoing pregnancy rate of 54.21%. The multiple pregnancy rate of is 1.24%. For patients aged 34 and above, the CPR decreased by 10% for every 1-year increase in age (adjusted OR 0.90, 95% CI 0.84-0.96, p < 0.0001). Similarly, the OPR decreased by 16% for every 1-year increase in age (adjusted OR 0.84, 95% CI 0.81-0.88, p < 0.0001). Patients aged 35-37 years had an acceptable OPR of 52.4% after eSET, with a low multiple pregnancy rate (1.1%). Pregnancy outcomes were significantly better in blastocyst cycles compared to cleavage embryo cycles, and this trend was more pronounced in older patients. There was a non-linear relationship between female age and pregnancy outcomes in patients undergoing their first eSET cycles. The clinical pregnancy rate and ongoing pregnancy rate decreased significantly with age, especially in women older than 34 years. For patients under 37 years old, single embryo transfer should be prioritized. For patients over 38 years old with available blastocysts, eSET is also recommended.
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Affiliation(s)
- Xue Wang
- Reproductive Medicine Center, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Pei-Zhe Tian
- Reproductive Medicine Center, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Yi-Jun Zhao
- Reproductive Medicine Center, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Jin Lu
- Reproductive Medicine Center, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Chen-Yue Dong
- Reproductive Medicine Center, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Cui-Lian Zhang
- Reproductive Medicine Center, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China.
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Ramasamy T, Varughese B, Singh M, Tailor P, Rao A, Misra S, Sharma N, Desiraju K, Thiruvengadam R, Wadhwa N, Kapoor S, Bhatnagar S, Kshetrapal P. Post-natal gestational age assessment using targeted metabolites of neonatal heel prick and umbilical cord blood: A GARBH-Ini cohort study from North India. J Glob Health 2024; 14:04115. [PMID: 38968007 PMCID: PMC11225965 DOI: 10.7189/jogh.14.04115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2024] Open
Abstract
Background Accurate assessment of gestational age (GA) and identification of preterm birth (PTB) at delivery is essential to guide appropriate post-natal clinical care. Undoubtedly, dating ultrasound sonography (USG) is the gold standard to ascertain GA, but is not accessible to the majority of pregnant women in low- and middle-income countries (LMICs), particularly in rural areas and small secondary care hospitals. Conventional methods of post-natal GA assessment are not reliable at delivery and are further compounded by a lack of trained personnel to conduct them. We aimed to develop a population-specific GA model using integrated clinical and biochemical variables measured at delivery. Methods We acquired metabolic profiles on paired neonatal heel prick (nHP) and umbilical cord blood (uCB) dried blood spot (DBS) samples (n = 1278). The master data set consists of 31 predictors from nHP and 24 from uCB after feature selection. These selected predictors including biochemical analytes, birth weight, and placental weight were considered for the development of population-specific GA estimation and birth outcome classification models using eXtreme Gradient Boosting (XGBoost) algorithm. Results The nHP and uCB full model revealed root mean square error (RMSE) of 1.14 (95% confidence interval (CI) = 0.82-1.18) and of 1.26 (95% CI = 0.88-1.32) to estimate the GA as compared to actual GA, respectively. In addition, these models correctly estimated 87.9 to 92.5% of the infants within ±2 weeks of the actual GA. The classification models also performed as the best fit to discriminate the PTB from term birth (TB) infants with an area under curve (AUC) of 0.89 (95% CI = 0.84-0.94) for nHP and an AUC of 0.89 (95% CI = 0.85-0.95) for uCB. Conclusion The biochemical analytes along with clinical variables in the nHP and uCB data sets provide higher accuracy in predicting GA. These models also performed as the best fit to identify PTB infants at delivery.
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Affiliation(s)
- Thirunavukkarasu Ramasamy
- Lab of Perinatal Research, Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Bijo Varughese
- Genetics Laboratory, Department of Paediatrics, Maulana Azad Medical College, New Delhi, India
| | - Mukesh Singh
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
| | - Pragya Tailor
- Lab of Perinatal Research, Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Archana Rao
- Lab of Perinatal Research, Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Sumit Misra
- Gurugram Civil Hospital, GCH, Haryana, India
| | - Nikhil Sharma
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Koundiya Desiraju
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Ramachandran Thiruvengadam
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Nitya Wadhwa
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - GARBH-Ini Study Group6
- Lab of Perinatal Research, Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
- Genetics Laboratory, Department of Paediatrics, Maulana Azad Medical College, New Delhi, India
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
- Gurugram Civil Hospital, GCH, Haryana, India
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
- Interdisciplinary Group for Advanced Research on Birth Outcomes - DBT India Initiative, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Seema Kapoor
- Genetics Laboratory, Department of Paediatrics, Maulana Azad Medical College, New Delhi, India
| | - Shinjini Bhatnagar
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Pallavi Kshetrapal
- Lab of Perinatal Research, Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
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Lee CI, Tzeng CR, Li M, Lai HH, Chen CH, Huang Y, Chang TA, Chen CH, Huang CC, Lee MS, Liu M. Leveraging federated learning for boosting data privacy and performance in IVF embryo selection. J Assist Reprod Genet 2024; 41:1811-1820. [PMID: 38834757 PMCID: PMC11263320 DOI: 10.1007/s10815-024-03148-z] [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: 03/26/2024] [Accepted: 05/18/2024] [Indexed: 06/06/2024] Open
Abstract
PURPOSE To study the effectiveness of federated learning in in vitro fertilization on embryo evaluation tasks. METHODS This is a retrospective cohort analysis. Two datasets were used in this study. The ploidy status dataset consisted of 10,065 embryo records, 3760 treatments, and 2479 infertile couples from 5 hospitals. The clinical pregnancy dataset consisted of 4495 embryo records, 4495 treatments, and 3704 infertile couples from 4 hospitals. Federated learning and the gradient boosting decision tree algorithm were utilized for modeling. RESULTS On the ploidy status dataset, the areas under the receiver operating characteristic curves of our model trained with federated learning were 71.78%, 73.10%, 69.39%, 69.72%, and 73.46% for 5 hospitals respectively, showing an average increase of 2.5% compared to those of our model trained without federated learning. On the clinical pregnancy dataset, the areas under the receiver operating characteristic curves of our model trained with federated learning were 72.03%, 56.77%, 61.63%, and 58.58% for 4 hospitals respectively, showing an average increase of 3.08%. CONCLUSIONS Federated learning can improve data privacy and data security and meanwhile improve the performance of embryo selection tasks by leveraging data from multiple sources. This study demonstrates the effectiveness of federated learning in embryo evaluation, and the results show the promise for future application.
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Affiliation(s)
- Chun-I Lee
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Obstetrics and Gynecology, Chung Shan Medical University, Taichung, Taiwan
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | | | - Monty Li
- Becoming Reproductive Center, Taipei, Taiwan
| | - Hsing-Hua Lai
- Stork Fertility Center, Stork Ladies Clinic, Hsinchu, Taiwan
| | - Chi-Huang Chen
- Division of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Obstetrics and Gynecology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yulun Huang
- Binflux, Inc, 4F.-1, No. 9, Dehui St., Zhongshan Dist, Taipei, 10461, Taiwan
| | - T Arthur Chang
- Department of Obstetrics and Gynecology, University of Texas Health Science Center, San Antonio, TX, USA
| | - Chien-Hong Chen
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | - Chun-Chia Huang
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | - Maw-Sheng Lee
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Obstetrics and Gynecology, Chung Shan Medical University, Taichung, Taiwan
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | - Mark Liu
- Binflux, Inc, 4F.-1, No. 9, Dehui St., Zhongshan Dist, Taipei, 10461, Taiwan.
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Yao MWM, Nguyen ET, Retzloff MG, Gago LA, Copland S, Nichols JE, Payne JF, Opsahl M, Cadesky K, Meriano J, Donesky BW, Bird J, Peavey M, Beesley R, Neal G, Bird JS, Swanson T, Chen X, Walmer DK. Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective Multicenter Experience. J Clin Med 2024; 13:3560. [PMID: 38930089 PMCID: PMC11204457 DOI: 10.3390/jcm13123560] [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: 05/05/2024] [Revised: 06/07/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
Objectives: In vitro fertilization (IVF) has the potential to give babies to millions more people globally, yet it continues to be underutilized. We established a globally applicable and locally adaptable IVF prognostics report and framework to support patient-provider counseling and enable validated, data-driven treatment decisions. This study investigates the IVF utilization rates associated with the usage of machine learning, center-specific (MLCS) prognostic reports (the Univfy® report) in provider-patient pre-treatment and IVF counseling. Methods: We used a retrospective cohort comprising 24,238 patients with new patient visits (NPV) from 2016 to 2022 across seven fertility centers in 17 locations in seven US states and Ontario, Canada. We tested the association of Univfy report usage and first intra-uterine insemination (IUI) and/or first IVF usage (a.k.a. conversion) within 180 days, 360 days, and "Ever" of NPV as primary outcomes. Results: Univfy report usage was associated with higher direct IVF conversion (without prior IUI), with odds ratios (OR) 3.13 (95% CI 2.83, 3.46), 2.89 (95% CI 2.63, 3.17), and 2.04 (95% CI 1.90, 2.20) and total IVF conversion (with or without prior IUI), OR 3.41 (95% CI 3.09, 3.75), 3.81 (95% CI 3.49, 4.16), and 2.78 (95% CI 2.59, 2.98) in 180-day, 360-day, and Ever analyses, respectively; p < 0.05. Among patients with Univfy report usage, after accounting for center as a factor, older age was a small yet independent predictor of IVF conversion. Conclusions: Usage of a patient-centric, MLCS-based prognostics report was associated with increased IVF conversion among new fertility patients. Further research to study factors influencing treatment decision making and real-world optimization of patient-centric workflows utilizing the MLCS reports is warranted.
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Affiliation(s)
- Mylene W. M. Yao
- Department of R&D, Univfy Inc., 117 Main Street, #139, Los Altos, CA 94022, USA
| | - Elizabeth T. Nguyen
- Department of R&D, Univfy Inc., 117 Main Street, #139, Los Altos, CA 94022, USA
| | | | | | | | - John E. Nichols
- Piedmont Reproductive Endocrinology Group, Greenville, SC 29615, USA (J.F.P.)
| | - John F. Payne
- Piedmont Reproductive Endocrinology Group, Greenville, SC 29615, USA (J.F.P.)
| | | | - Ken Cadesky
- TRIO Fertility Partners, Toronto, ON M5G 2K4, Canada
| | - Jim Meriano
- TRIO Fertility Partners, Toronto, ON M5G 2K4, Canada
| | | | - Joseph Bird
- My Fertility Center, Chattanooga, TN 37421, USA
| | - Mary Peavey
- Atlantic Reproductive Medicine, Raleigh, NC 27617, USA
| | | | - Gregory Neal
- Fertility Center of San Antonio, San Antonio, TX 78229, USA
| | | | - Trevor Swanson
- Department of R&D, Univfy Inc., 117 Main Street, #139, Los Altos, CA 94022, USA
| | - Xiaocong Chen
- Department of R&D, Univfy Inc., 117 Main Street, #139, Los Altos, CA 94022, USA
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Shingshetty L, Cameron NJ, Mclernon DJ, Bhattacharya S. Predictors of success after in vitro fertilization. Fertil Steril 2024; 121:742-751. [PMID: 38492930 DOI: 10.1016/j.fertnstert.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/01/2024] [Accepted: 03/04/2024] [Indexed: 03/18/2024]
Abstract
The last few decades have witnessed a rise in the global uptake of in vitro fertilization (IVF) treatment. To ensure optimal use of this technology, it is important for patients and clinicians to have access to tools that can provide accurate estimates of treatment success and understand the contribution of key clinical and laboratory parameters that influence the chance of conception after IVF treatment. The focus of this review was to identify key predictors of IVF treatment success and assess their impact in terms of live birth rates. We have identified 11 predictors that consistently feature in currently available prediction models, including age, duration of infertility, ethnicity, body mass index, antral follicle count, previous pregnancy history, cause of infertility, sperm parameters, number of oocytes collected, morphology of transferred embryos, and day of embryo transfer.
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Affiliation(s)
- Laxmi Shingshetty
- Aberdeen Centre for Reproductive Medicine, NHS Grampian, Aberdeen, Aberdeenshire, United Kingdom; School of Medicine, Nutrition Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, Aberdeenshire, United Kingdom.
| | - Natalie J Cameron
- School of Medicine, Nutrition Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, Aberdeenshire, United Kingdom; Aberdeen Maternity Hospital, NHS Grampian and University of Aberdeen, Aberdeen, Aberdeenshire, United Kingdom
| | - David J Mclernon
- Medical Statistics Team, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, Aberdeenshire, United Kingdom
| | - Siladitya Bhattacharya
- School of Medicine, Nutrition Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, Aberdeenshire, United Kingdom
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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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Liu CH, Chao WT, Wang PH. Risk factors for persistent stress urinary incontinence after pregnancy. Taiwan J Obstet Gynecol 2023; 62:389-390. [PMID: 37188439 DOI: 10.1016/j.tjog.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/03/2023] [Indexed: 05/17/2023] Open
Affiliation(s)
- Chia-Hao Liu
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Obstetrics and Gynecology, National Yang Ming Chiao Tung University, Taipei, Taiwan; Female Cancer Foundation, Taipei, Taiwan
| | - Wei-Ting Chao
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Obstetrics and Gynecology, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Peng-Hui Wang
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Obstetrics and Gynecology, National Yang Ming Chiao Tung University, Taipei, Taiwan; Female Cancer Foundation, Taipei, Taiwan; Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
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