1
|
Luong TMT, Ho NT, Hwu YM, Lin SY, Ho JYP, Wang RS, Lee YX, Tan SJ, Lee YR, Huang YL, Hsu YC, Le NQK, Tzeng CR. Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation. J Assist Reprod Genet 2024; 41:2349-2358. [PMID: 38963605 PMCID: PMC11405599 DOI: 10.1007/s10815-024-03178-7] [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: 02/27/2024] [Accepted: 06/13/2024] [Indexed: 07/05/2024] Open
Abstract
PURPOSE To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data. METHODS This retrospective study utilized a dataset of 1908 blastocyst embryos. The dataset includes ploidy status, morphokinetic features, morphology grades, and 11 clinical variables. Six machine learning (ML) models including Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost (ADA), and Light Gradient-Boosting Machine (LGBM) were trained to predict ploidy status probabilities across three distinct datasets: high-grade embryos (HGE, n = 1107), low-grade embryos (LGE, n = 364), and all-grade embryos (AGE, n = 1471). The model's performance was interpreted using XAI, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) techniques. RESULTS The mean maternal age was 38.5 ± 3.85 years. The Random Forest (RF) model exhibited superior performance compared to the other five ML models, achieving an accuracy of 0.749 and an AUC of 0.808 for AGE. In the external test set, the RF model achieved an accuracy of 0.714 and an AUC of 0.750 (95% CI, 0.702-0.796). SHAP's feature impact analysis highlighted that maternal age, paternal age, time to blastocyst (tB), and day 5 morphology grade significantly impacted the predictive model. In addition, LIME offered specific case-ploidy prediction probabilities, revealing the model's assigned values for each variable within a finite range. CONCLUSION The model highlights the potential of using XAI algorithms to enhance ploidy prediction, optimize embryo selection as patient-centric consultation, and provides reliability and transparent insights into the decision-making process.
Collapse
Affiliation(s)
- Thi-My-Trang Luong
- International Master Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan
- Taipei Fertility Centre, Taipei, Taiwan
| | - Nguyen-Tuong Ho
- Taipei Fertility Centre, Taipei, Taiwan
- IVFMD, My Duc Hospital, Ho Chi Minh, Vietnam
| | | | | | | | | | | | | | | | | | | | - Nguyen-Quoc-Khanh Le
- AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan.
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan.
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
| | | |
Collapse
|
2
|
Chavez-Badiola A, Farías AFS, Mendizabal-Ruiz G, Silvestri G, Griffin DK, Valencia-Murillo R, Drakeley AJ, Cohen J. Use of artificial intelligence embryo selection based on static images to predict first-trimester pregnancy loss. Reprod Biomed Online 2024; 49:103934. [PMID: 38824762 DOI: 10.1016/j.rbmo.2024.103934] [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: 10/18/2023] [Revised: 02/16/2024] [Accepted: 02/27/2024] [Indexed: 06/04/2024]
Abstract
RESEARCH QUESTION Can an artificial intelligence embryo selection assistant predict the incidence of first-trimester spontaneous abortion using static images of IVF embryos? DESIGN In a blind, retrospective study, a cohort of 172 blastocysts from IVF cases with single embryo transfer and a positive biochemical pregnancy test was ranked retrospectively by the artificial intelligence morphometric algorithm ERICA. Making use of static embryo images from a light microscope, each blastocyst was assigned to one of four possible groups (optimal, good, fair or poor), and linear regression was used to correlate the results with the presence or absence of a normal fetal heart beat as an indicator of ongoing pregnancy or spontaneous abortion, respectively. Additional analyses included modelling for recipient age and chromosomal status established by preimplantation genetic testing for aneuploidy (PGT-A). RESULTS Embryos classified as optimal/good had a lower incidence of spontaneous abortion (16.1%) compared with embryos classified as fair/poor (25%; OR = 0.46, P = 0.005). The incidence of spontaneous abortion in chromosomally normal embryos (determined by PGT-A) was 13.3% for optimal/good embryos and 20.0% for fair/poor embryos, although the difference was not significant (P = 0.531). There was a significant association between embryo rank and recipient age (P = 0.018), in that the incidence of spontaneous abortion was unexpectedly lower in older recipients (21.3% for age ≤35 years, 17.9% for age 36-38 years, 16.4% for age ≥39 years; OR = 0.354, P = 0.0181). Overall, these results support correlation between risk of spontaneous abortion and embryo rank as determined by artificial intelligence; classification accuracy was calculated to be 67.4%. CONCLUSIONS This preliminary study suggests that artificial intelligence (ERICA), which was designed as a ranking system to assist with embryo transfer decisions and ploidy prediction, may also be useful to provide information for couples on the risk of spontaneous abortion. Future work will include a larger sample size and karyotyping of miscarried pregnancy tissue.
Collapse
Affiliation(s)
- Alejandro Chavez-Badiola
- University of Kent, School of Biosciences, Canterbury, UK; IVF 2.0 Ltd, London, UK; New Hope Fertility Center, Guadalajara, Mexico; Conceivable Life Sciences, New York, NY, USA
| | | | - Gerardo Mendizabal-Ruiz
- Conceivable Life Sciences, New York, NY, USA; Departamento de Ciencias Computacionales, Universidad de Guadalajara, Guadalajara, Mexico
| | - Giuseppe Silvestri
- University of Kent, School of Biosciences, Canterbury, UK; Conceivable Life Sciences, New York, NY, USA
| | | | | | - Andrew J Drakeley
- IVF 2.0 Ltd, London, UK; Hewitt Fertility Centre, Liverpool Women's NHS Foundation Trust, Liverpool, UK
| | - Jacques Cohen
- IVF 2.0 Ltd, London, UK; Conceivable Life Sciences, New York, NY, USA
| |
Collapse
|
3
|
Bachir BG, Yacoubian A, Nasrallah OG, El Taha L, Choucair F. Are urologists underrepresented on fertility clinic websites? A web-based analysis. Urol Ann 2024; 16:210-214. [PMID: 39290218 PMCID: PMC11404711 DOI: 10.4103/ua.ua_97_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 09/19/2024] Open
Abstract
Introduction Infertile couples frequently utilize the Internet to find various reproductive clinics and research their alternatives. Patients are increasingly using self-referral because of online information on health-care providers. The objective is to compare the image of infertility specialists to other team members on the websites of reproductive clinics. Methods Information was gathered during November and December 2022 from two publicly accessible online registries which include the Human Fertilization and Embryology Authority located in the United Kingdom and the Society for Assisted Reproductive Technology located in the United States. We looked over every website that was accessible, paying close attention to how each team member was portrayed online. Results We examined a total of 447 clinic websites. Only 8% of the profiles of male infertility doctors were included. Contrarily, most websites (96%), which specialize in reproductive endocrinology and infertility, feature the profiles of female infertility experts. Male infertility professionals also had significantly lower representation than other clinic employees, such as nurses (55.7%, P < 0.0001), directors of embryology laboratories (46.5%, P < 0.0001), office personnel (39.6%, P < 0.0001), and embryology specialists (29.7%, P < 0.0001). Conclusion Although male factor infertility explains the existence of over half of all cases of infertility, urologists who specialize in male infertility are glaringly understated on websites for fertility clinics. By improving this issue, fertility clinics can draw in more patients by making all members of the care team more visible.
Collapse
Affiliation(s)
- Bassel G Bachir
- Division of Urology, Department of Surgery, American University of Beirut Medical Center, Beirut, Lebanon, Qatar
- Division of Reproductive Medicine, Sidra Medicine, Doha, Qatar
| | - Aline Yacoubian
- Division of Urology, Department of Surgery, American University of Beirut Medical Center, Beirut, Lebanon, Qatar
| | - Oussama Ghassan Nasrallah
- Division of Urology, Department of Surgery, American University of Beirut Medical Center, Beirut, Lebanon, Qatar
| | - Lina El Taha
- Division of Reproductive Medicine, Sidra Medicine, Doha, Qatar
| | - Fadi Choucair
- Division of Reproductive Medicine, Sidra Medicine, Doha, Qatar
| |
Collapse
|
4
|
Lee T, Natalwala J, Chapple V, Liu Y. A brief history of artificial intelligence embryo selection: from black-box to glass-box. Hum Reprod 2024; 39:285-292. [PMID: 38061074 PMCID: PMC11016335 DOI: 10.1093/humrep/dead254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 11/21/2023] [Indexed: 02/02/2024] Open
Abstract
With the exponential growth of computing power and accumulation of embryo image data in recent years, artificial intelligence (AI) is starting to be utilized in embryo selection in IVF. Amongst different AI technologies, machine learning (ML) has the potential to reduce operator-related subjectivity in embryo selection while saving labor time on this task. However, as modern deep learning (DL) techniques, a subcategory of ML, are increasingly used, its integrated black-box attracts growing concern owing to the well-recognized issues regarding lack of interpretability. Currently, there is a lack of randomized controlled trials to confirm the effectiveness of such black-box models. Recently, emerging evidence has shown underperformance of black-box models compared to the more interpretable traditional ML models in embryo selection. Meanwhile, glass-box AI, such as interpretable ML, is being increasingly promoted across a wide range of fields and is supported by its ethical advantages and technical feasibility. In this review, we propose a novel classification system for traditional and AI-driven systems from an embryology standpoint, defining different morphology-based selection approaches with an emphasis on subjectivity, explainability, and interpretability.
Collapse
Affiliation(s)
- Tammy Lee
- Fertility North, Joondalup Private Hospital, Joondalup, Western Australia, Australia
- School of Human Sciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Jay Natalwala
- Fertility North, Joondalup Private Hospital, Joondalup, Western Australia, Australia
| | - Vincent Chapple
- Fertility North, Joondalup Private Hospital, Joondalup, Western Australia, Australia
| | - Yanhe Liu
- Fertility North, Joondalup Private Hospital, Joondalup, Western Australia, Australia
- School of Human Sciences, University of Western Australia, Crawley, Western Australia, Australia
- Faculty of Health Sciences and Medicine, Bond University, Robina, Queensland, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| |
Collapse
|
5
|
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.
Collapse
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
| | | |
Collapse
|
6
|
Liang X, He J, He L, Lin Y, Li Y, Cai K, Wei J, Lu Y, Chen Z. An ultrasound-based deep learning radiomic model combined with clinical data to predict clinical pregnancy after frozen embryo transfer: a pilot cohort study. Reprod Biomed Online 2023; 47:103204. [PMID: 37248145 DOI: 10.1016/j.rbmo.2023.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/16/2023] [Accepted: 03/21/2023] [Indexed: 03/29/2023]
Abstract
RESEARCH QUESTION Can a multi-modal fusion model based on ultrasound-based deep learning radiomics combined with clinical parameters provide personalized evaluation of endometrial receptivity and predict the occurrence of clinical pregnancy after frozen embryo transfer (FET)? DESIGN Prospective cohort study of women (n = 326) who underwent FET between August 2019 and December 2021. Input quantitative variables and input image data for radiomic feature extraction were collected to establish a multi-modal fusion prediction model. An additional independent dataset of 453 ultrasound endometrial images was used to establish the segmentation model to determine the endometrial region on ultrasound images for analysis. The performance of different algorithms and different input data for prediction of FET outcome were compared. RESULTS A total of 240 patients with complete data were included in the final cohort. The proposed multi-modal fusion model performed significantly better than the use of either image or quantitative variables alone to predict the occurrence of clinical pregnancy after FET (P ≤ 0.034). Its area under the curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the proposed model were 0.825, 72.5%, 96.2%, 58.3%, 72.3% and 89.5%, respectively. The Dice coefficient of the multi-task endometrial ultrasound segmentation model was 0.89. Use of endometrial segmentation features significantly improved the prediction performance of the model (P = 0.041). CONCLUSIONS The multi-modal fusion model based on ultrasound-based deep learning radiomics combined with clinical quantitative variables offers a favourable and rapid non-invasive approach for personalized prediction of FET outcome.
Collapse
Affiliation(s)
- Xiaowen Liang
- Institution of Medical Imaging, University of South China, Hengyang, China; The Seventh Affiliated Hospital, Hengyang Medical School, University of South China, Changsha, China; The First Affiliated Hospital, Medical Imaging Centre, Hengyang Medical School, University of South China, Hengyang, China
| | - Jianchong He
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Lu He
- The First Affiliated Hospital, Department of Obstetrics and Gynecology, Hengyang Medical School, University of South China, Hengyang, China
| | - Yan Lin
- Department of Ultrasound Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuewei Li
- Department of Ultrasound Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Kuan Cai
- Department of Ultrasound Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jun Wei
- Institution of Medical Imaging, University of South China, Hengyang, China
| | - Yao Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
| | - Zhiyi Chen
- Institution of Medical Imaging, University of South China, Hengyang, China; The Seventh Affiliated Hospital, Hengyang Medical School, University of South China, Changsha, China; The First Affiliated Hospital, Medical Imaging Centre, Hengyang Medical School, University of South China, Hengyang, China.
| |
Collapse
|
7
|
Calogero AE, Cannarella R, Agarwal A, Hamoda TAAAM, Rambhatla A, Saleh R, Boitrelle F, Ziouziou I, Toprak T, Gul M, Avidor-Reiss T, Kavoussi P, Chung E, Birowo P, Ghayda RA, Ko E, Colpi G, Dimitriadis F, Russo GI, Martinez M, Calik G, Kandil H, Salvio G, Mostafa T, Lin H, Park HJ, Gherabi N, Phuoc NHV, Quang N, Adriansjah R, La Vignera S, Micic S, Durairajanayagam D, Serefoglu EC, Karthikeyan VS, Kothari P, Atmoko W, Shah R. The Renaissance of Male Infertility Management in the Golden Age of Andrology. World J Mens Health 2023; 41:237-254. [PMID: 36649928 PMCID: PMC10042649 DOI: 10.5534/wjmh.220213] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 10/15/2022] [Indexed: 01/18/2023] Open
Abstract
Infertility affects nearly 186 million people worldwide and the male partner is the cause in about half of the cases. Meta-regression data indicate an unexplained decline in sperm concentration and total sperm count over the last four decades, with an increasing prevalence of male infertility. This suggests an urgent need to implement further basic and clinical research in Andrology. Andrology developed as a branch of urology, gynecology, endocrinology, and, dermatology. The first scientific journal devoted to andrological sciences was founded in 1969. Since then, despite great advancements, andrology has encountered several obstacles in its growth. In fact, for cultural reasons, the male partner has often been neglected in the diagnostic and therapeutic workup of the infertile couple. Furthermore, the development of assisted reproductive techniques (ART) has driven a strong impression that this biotechnology can overcome all forms of infertility, with a common belief that having a spermatozoon from a male partner (a sort of sperm donor) is all that is needed to achieve pregnancy. However, clinical practice has shown that the quality of the male gamete is important for a successful ART outcome. Furthermore, the safety of ART has been questioned because of the high prevalence of comorbidities in the offspring of ART conceptions compared to spontaneous conceptions. These issues have paved the way for more research and a greater understanding of the mechanisms of spermatogenesis and male infertility. Consequently, numerous discoveries have been made in the field of andrology, ranging from genetics to several "omics" technologies, oxidative stress and sperm DNA fragmentation, the sixth edition of the WHO manual, artificial intelligence, management of azoospermia, fertility in cancers survivors, artificial testis, 3D printing, gene engineering, stem cells therapy for spermatogenesis, and reconstructive microsurgery and seminal microbiome. Nevertheless, as many cases of male infertility remain idiopathic, further studies are required to improve the clinical management of infertile males. A multidisciplinary strategy involving both clinicians and scientists in basic, translational, and clinical research is the core principle that will allow andrology to overcome its limits and reach further goals. This state-of-the-art article aims to present a historical review of andrology, and, particularly, male infertility, from its "Middle Ages" to its "Renaissance", a golden age of andrology.
Collapse
Affiliation(s)
- Aldo E Calogero
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Rossella Cannarella
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
- Glickman Urological & Kidney Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ashok Agarwal
- Global Andrology Forum, Moreland Hills, OH, USA
- Cleveland Clinic Foundation, Cleveland, OH, USA.
| | - Taha Abo-Almagd Abdel-Meguid Hamoda
- Department of Urology, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Urology, Faculty of Medicine, Minia University, Minia, Egypt
| | - Amarnath Rambhatla
- Department of Urology, Vattikuti Urology Institute, Henry Ford Health System, Detroit, MI, USA
| | - Ramadan Saleh
- Department of Dermatology, Venereology and Andrology, Faculty of Medicine, Sohag University, Sohag, Egypt
- Ajyal IVF Center, Ajyal Hospital, Sohag, Egypt
| | - 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, Jouy-en-Josas, France
| | - Imad Ziouziou
- Department of Urology, College of Medicine and Pharmacy, Ibn Zohr University, Agadir, Morocco
| | - Tuncay Toprak
- Department of Urology, Fatih Sultan Mehmet Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
| | - Murat Gul
- Department of Urology, Selcuk University School of Medicine, Konya, Turkey
| | - 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
| | - Parviz Kavoussi
- Austin Fertility & Reproductive Medicine/Westlake IVF, Austin, TX, USA
| | - Eric Chung
- Department of Urology, Princess Alexandra Hospital, University of Queensland, Brisbane, Australia
| | - Ponco Birowo
- Department of Urology, Cipto Mangunkusumo General Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Ramy Abou Ghayda
- Urology Institute, University Hospitals, Case Western Reserve University, Cleveland, OH, USA
| | - Edmund Ko
- Department of Urology, Loma Linda University Health, Loma Linda, CA, USA
| | | | - Fotios Dimitriadis
- Department of Urology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Marlon Martinez
- Section of Urology, Department of Surgery, University of Santo Tomas Hospital, Manila, Philippines
| | - Gokhan Calik
- Department of Urology, Faculty of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | | | - Gianmaria Salvio
- Department of Endocrinology, Polytechnic University of Marche, Ancona, Italy
| | - Taymour Mostafa
- Department of Andrology, Sexology and STIs, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Haocheng Lin
- Department of Urology, Peking University Third Hospital, Peking University, Beijing, China
| | - Hyun Jun Park
- Department of Urology, Pusan National University School of Medicine, Busan, Korea
- Medical Research Institute of Pusan National University Hospital, Busan, Korea
| | - Nazim Gherabi
- Faculty of Medicine, Algiers University, Algiers, Algeria
| | | | - Nguyen Quang
- 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
| | - Ricky Adriansjah
- Department of Urology, Faculty of Medicine Universitas Padjadjaran, Hasan Sadikin General Hospital, Banding, Indonesia
| | - Sandro La Vignera
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Sava Micic
- Department of Andrology, Uromedica Polyclinic, Belgrade, Serbia
| | - Damayanthi Durairajanayagam
- Department of Physiology, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Selangor, Malaysia
| | - Ege Can Serefoglu
- Department of Urology, Biruni University School of Medicine, Istanbul, Turkey
| | | | - Priyank Kothari
- Department of Urology, B.Y.L Nair Ch Hospital, Mumbai, India
| | - Widi Atmoko
- Department Department of Urology, Dr. Cipto Mangunkusumo General Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Rupin Shah
- Division of Andrology, Department of Urology, Lilavati Hospital and Research Centre, Mumbai, India
| |
Collapse
|
8
|
Abdullah KAL, Atazhanova T, Chavez-Badiola A, Shivhare SB. Automation in ART: Paving the Way for the Future of Infertility Treatment. Reprod Sci 2023; 30:1006-1016. [PMID: 35922741 PMCID: PMC10160149 DOI: 10.1007/s43032-022-00941-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/09/2022] [Indexed: 01/11/2023]
Abstract
In vitro fertilisation (IVF) is estimated to account for the birth of more than nine million babies worldwide, perhaps making it one of the most intriguing as well as commoditised and industrialised modern medical interventions. Nevertheless, most IVF procedures are currently limited by accessibility, affordability and most importantly multistep, labour-intensive, technically challenging processes undertaken by skilled professionals. Therefore, in order to sustain the exponential demand for IVF on one hand, and streamline existing processes on the other, innovation is essential. This may not only effectively manage clinical time but also reduce cost, thereby increasing accessibility, affordability and efficiency. Recent years have seen a diverse range of technologies, some integrated with artificial intelligence, throughout the IVF pathway, which promise personalisation and, at least, partial automation in the not-so-distant future. This review aims to summarise the rapidly evolving state of these innovations in automation, with or without the integration of artificial intelligence, encompassing the patient treatment pathway, gamete/embryo selection, endometrial evaluation and cryopreservation of gametes/embryos. Additionally, it shall highlight the resulting prospective change in the role of IVF professionals and challenges of implementation of some of these technologies, thereby aiming to motivate continued research in this field.
Collapse
Affiliation(s)
- Kadrina Abdul Latif Abdullah
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Level 3, Women's Centre, John Radcliffe Hospital, Oxford, OX3 9DU, England
| | - Tomiris Atazhanova
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Level 3, Women's Centre, John Radcliffe Hospital, Oxford, OX3 9DU, England
| | | | - Sourima Biswas Shivhare
- TFP Simply Fertility, W Hanningfield Rd, Great Baddow, Chelmsford, CM2 8HN, England.
- The Centre for Reproductive and Genetic Health, London, UK.
| |
Collapse
|
9
|
Houri O, Gil Y, Danieli-Gruber S, Shufaro Y, Sapir O, Hochberg A, Ben-Haroush A, Wertheimer A. Prediction of oocyte maturation rate in the GnRH antagonist flexible IVF protocol using a novel machine learning algorithm - A retrospective study. Eur J Obstet Gynecol Reprod Biol 2023; 284:100-104. [PMID: 36965213 DOI: 10.1016/j.ejogrb.2023.03.022] [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: 07/08/2022] [Revised: 01/17/2023] [Accepted: 03/18/2023] [Indexed: 03/27/2023]
Abstract
Oocyte maturation is affected by various patient and cycle parameters and has a key effect on treatment outcome. A prediction model for oocyte maturation rate formulated by using machine learning and neural network algorithms has not yet been described. A retrospective cohort study that included all women aged ≤ 38 years who underwent their first IVF treatment using a flexible GnRH antagonist protocol in a single tertiary hospital between 2010 and 2015. 462 patients met the inclusion criteria. Median maturation rate was approximately 80%. Baseline characteristics and treatment parameters of cycles with high oocyte maturation rate (≥80%, n = 236) were compared to cycles with low oocyte maturation rate (<80%, n = 226). We used an XGBoost algorithm that fits the training data using decision trees and rates factors according to their influence on the prediction. For the machine training phase, 80% of the cohort was randomly selected, while rest of the samples were used to evaluate our model's accuracy. We demonstrated an accuracy rate of 75% in predicting high oocyte maturation rate in GnRH antagonist cycles. Our model showed an operating characteristic curve with AUC of 0.78 (95% CI 0.73-0.82). The most predictive parameters were peak estradiol level on trigger day, estradiol level on antagonist initiation day, average dose of gonadotropins per day and progesterone level on trigger day. A state-of-the-art machine learning algorithm presented promising ability to predict oocyte maturation rate in the first GnRH antagonist flexible protocol using simple parameters before final trigger for ovulation. A prospective study to evaluate this model is needed.
Collapse
Affiliation(s)
- Ohad Houri
- IVF and Infertility Unit, Helen Schneider Hospital for Women, Rabin Medical Center-Beilinson Hospital, Petach Tikva, 4941492, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6901128, Israel.
| | - Yotam Gil
- IVF and Infertility Unit, Helen Schneider Hospital for Women, Rabin Medical Center-Beilinson Hospital, Petach Tikva, 4941492, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6901128, Israel
| | - Shir Danieli-Gruber
- IVF and Infertility Unit, Helen Schneider Hospital for Women, Rabin Medical Center-Beilinson Hospital, Petach Tikva, 4941492, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6901128, Israel
| | - Yoel Shufaro
- IVF and Infertility Unit, Helen Schneider Hospital for Women, Rabin Medical Center-Beilinson Hospital, Petach Tikva, 4941492, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6901128, Israel
| | - Onit Sapir
- IVF and Infertility Unit, Helen Schneider Hospital for Women, Rabin Medical Center-Beilinson Hospital, Petach Tikva, 4941492, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6901128, Israel
| | - Alyssa Hochberg
- IVF and Infertility Unit, Helen Schneider Hospital for Women, Rabin Medical Center-Beilinson Hospital, Petach Tikva, 4941492, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6901128, Israel
| | - Avi Ben-Haroush
- IVF and Infertility Unit, Helen Schneider Hospital for Women, Rabin Medical Center-Beilinson Hospital, Petach Tikva, 4941492, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6901128, Israel
| | - Avital Wertheimer
- IVF and Infertility Unit, Helen Schneider Hospital for Women, Rabin Medical Center-Beilinson Hospital, Petach Tikva, 4941492, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6901128, Israel
| |
Collapse
|
10
|
Cobos-Campos R, Cordero-Guevara JA, Apiñaniz A, de Lafuente AS, Bermúdez Ampudia C, Argaluza Escudero J, Pérez Llanos I, Parraza Diez N. The Impact of Digital Health on Smoking Cessation. Interact J Med Res 2023; 12:e41182. [PMID: 36920468 PMCID: PMC10131696 DOI: 10.2196/41182] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 12/05/2022] [Accepted: 01/03/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND Smartphones have become useful tools for medicine, with the use of specific apps making it possible to bring health care closer to inaccessible areas, continuously monitor a patient's pathology at any time and place, promote healthy habits, and ultimately improve patients' quality of life and the efficiency of the health care system. Since 2020, the use of smartphones has reached unprecedented levels. There are more than 350,000 health apps, according to a 2021 IQVIA Institute report, that address, among other things, the management of patient appointments; communication among different services or professionals; the promotion of lifestyle changes related to adopting healthy habits; and the monitoring of different pathologies and chronic conditions, including smoking cessation. The number of mobile apps for quitting smoking is high. As early as 2017, a total of 177 unique smoking cessation-relevant apps were identified in the iPhone App Store, 139 were identified in Google Play, 70 were identified in the BlackBerry app store, and 55 were identified in the Windows Phone Store, but very few have adequate scientific support. It seems clear that efforts are needed to assess the quality of these apps, as well as their effectiveness in different population groups, to have tools that offer added value to standard practices. OBJECTIVE This viewpoint aims to highlight the benefits of mobile health (mHealth) and its potential as an adjuvant tool in health care. METHODS A review of literature and other data sources was performed in order to show the current status of mobile apps that can offer support for smoking cessation. For this purpose, the PubMed, Embase, and Cochrane databases were explored between May and November 2022. RESULTS In terms of smoking cessation, mHealth has become a powerful coadjuvant tool that allows health workers to perform exhaustive follow-ups for the process of quitting tobacco and provide support anytime and anywhere. mHealth tools are effective for different groups of smokers (eg, pregnant women, patients with chronic obstructive pulmonary disease, patients with mental illness, and the general population) and are cost-effective, generating savings for the health system. However, there are some patient characteristics that can predict the success of using mobile apps in the smoking cessation process, such as the lower age of patients, dependence on tobacco, the number of quit attempts, and the previous use of mobile apps, among others. Therefore, it is preferable to offer these tools to patients with a higher probability of quitting tobacco. CONCLUSIONS mHealth is a promising tool for helping smokers in the smoking cessation process. There is a need for well-designed clinical studies and economic evaluations to jointly assess the effectiveness of new interventions in different population groups, as well as their impact on health care resources.
Collapse
Affiliation(s)
- Raquel Cobos-Campos
- Epidemiology and Public Health Group, Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain
| | | | - Antxon Apiñaniz
- Epidemiology and Public Health Group, Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain.,Osakidetza Basque Health Service, Vitoria-Gasteiz, Spain.,Department of Preventive Medicine and Public Health, University of the Basque Country, Vitoria-Gasteiz, Spain.,Research Network on Chronicity, Primary Care and Health Promotion, Madrid, Spain
| | - Arantza Sáez de Lafuente
- Epidemiology and Public Health Group, Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain
| | | | - Julene Argaluza Escudero
- Epidemiology and Public Health Group, Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain
| | - Iraida Pérez Llanos
- Epidemiology and Public Health Group, Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain.,Osakidetza Basque Health Service, Vitoria-Gasteiz, Spain
| | - Naiara Parraza Diez
- Epidemiology and Public Health Group, Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain.,Research Network on Chronicity, Primary Care and Health Promotion, Madrid, Spain
| |
Collapse
|
11
|
Theilgaard Lassen J, Fly Kragh M, Rimestad J, Nygård Johansen M, Berntsen J. Development and validation of deep learning based embryo selection across multiple days of transfer. Sci Rep 2023; 13:4235. [PMID: 36918648 PMCID: PMC10015019 DOI: 10.1038/s41598-023-31136-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 03/07/2023] [Indexed: 03/16/2023] Open
Abstract
This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of human embryos incubated for 2, 3, and 5 or more days. We trained and evaluated the model on an extensive and diverse dataset including 181,428 embryos from 22 IVF clinics across the world. To discriminate the transferred embryos with known outcome, we show areas under the receiver operating curve ranging from 0.621 to 0.707 depending on the day of transfer. Predictive performance increased over time and showed a strong correlation with morphokinetic parameters. The model's performance is equivalent to the KIDScore D3 model on day 3 embryos while it significantly surpasses the performance of KIDScore D5 v3 on day 5+ embryos. This model provides an analysis of time-lapse sequences without the need for user input, and provides a reliable method for ranking embryos for their likelihood of implantation, at both cleavage and blastocyst stages. This greatly improves embryo grading consistency and saves time compared to traditional embryo evaluation methods.
Collapse
|
12
|
Szubert M, Rycerz A, Wilczyński JR. How to Improve Non-Invasive Diagnosis of Endometriosis with Advanced Statistical Methods. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:499. [PMID: 36984500 PMCID: PMC10059817 DOI: 10.3390/medicina59030499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/26/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023]
Abstract
Background and Objectives: Endometriosis is one of the most common gynecological disorders in women of reproductive age. Causing pelvic pain and infertility, it is considered one of the most serious health problems, being responsible for work absences or productivity loss. Its diagnosis is often delayed because of the need for an invasive laparoscopic approach. Despite years of studies, no single marker for endometriosis has been discovered. The aim of this research was to find an algorithm based on symptoms and laboratory tests that could diagnose endometriosis in a non-invasive way. Materials and Methods: The research group consisted of 101 women hospitalized for diagnostic laparoscopy, among which 71 had confirmed endometriosis. Data on reproductive history were collected in detail. CA125 (cancer antigen-125) level and VEGF1(vascular endothelial growth factor 1) were tested in blood samples. Among the used statistical methods, the LASSO regression-a new important statistical tool eliminating the least useful features-was the only method to have significant results. Results: Out of 19 features based on results of LASSO, 7 variables were chosen: body mass index, age of menarche, cycle length, painful periods, information about using contraception, CA125, and VEGF1. After multivariate logistic regression with a backward strategy, the three most significant features were evaluated. The strongest impact on endometriosis prediction had information about painful periods, CA125 over 15 u/mL, and the lowest BMI, with a sensitivity of 0.8800 and a specificity of 0.8000, respectively. Conclusions: Advanced statistical methods are crucial when creating non-invasive tests for endometriosis. An algorithm based on three easy features, including painful menses, BMI level, and CA125 concentration could have an important place in the non-invasive diagnosis of endometriosis. If confirmed in a prospective study, implementing such an algorithm in populations with a high risk of endometriosis will allow us to cover patients suspected of endometriosis with proper treatment.
Collapse
Affiliation(s)
- Maria Szubert
- Department of Surgical and Oncological Gynecology, 1st Department of Gynecology and Obstetrics, Medical University of Lodz, M. Pirogow’s Teaching Hospital, Wilenska 37 St., 94-029 Lodz, Poland
- Club 35, Polish Society of Gynecologists and Obstetricians, ul. Cybernetyki 7F/87, 02-677 Warszawa, Poland
| | - Aleksander Rycerz
- Department of Surgical and Oncological Gynecology, 1st Department of Gynecology and Obstetrics, Medical University of Lodz, M. Pirogow’s Teaching Hospital, Wilenska 37 St., 94-029 Lodz, Poland
- Faculty of Mathematics and Computer Science, University of Lodz, Banacha 22, 90-238 Lodz, Poland
| | - Jacek R. Wilczyński
- Department of Surgical and Oncological Gynecology, 1st Department of Gynecology and Obstetrics, Medical University of Lodz, M. Pirogow’s Teaching Hospital, Wilenska 37 St., 94-029 Lodz, Poland
| |
Collapse
|
13
|
Glatstein I, Chavez-Badiola A, Curchoe CL. New frontiers in embryo selection. J Assist Reprod Genet 2023; 40:223-234. [PMID: 36609943 PMCID: PMC9935769 DOI: 10.1007/s10815-022-02708-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 12/23/2022] [Indexed: 01/09/2023] Open
Abstract
Human infertility is a major global public health issue estimated to affect one out of six couples, while the number of assisted reproduction cycles grows impressively year over year. Efforts to alleviate infertility using advanced technology are gaining traction rapidly as infertility has an enormous impact on couples and the potential to destabilize entire societies if replacement birthrates are not achieved. Artificial intelligence (AI) technologies, leveraged by the highly advanced assisted reproductive technology (ART) industry, are a promising addition to the armamentarium of tools available to combat global infertility. This review provides a background for current methodologies in embryo selection, which is a manual, time-consuming, and poorly reproducible task. AI has the potential to improve this process (among many others) in both the clinician's office and the IVF laboratory. Embryo selection is evolving through digital methodologies into an automated procedure, with superior reliability and reproducibility, that is likely to result in higher pregnancy rates for patients. There is an emerging body of data demonstrating the utility of AI applications in multiple areas in the IVF laboratory. AI platforms have been developed to evaluate individual embryologist performance; to provide quality assurance for culture systems; to correlate embryologist's assessments and AI systems; to predict embryo ploidy, implantation, fetal heartbeat, and live birth outcome; and to replace the current "analogue" system of embryo selection with a digital paradigm. AI capability will distinguish high performing, high profit margin, low-cost, and highly successful IVF clinic business models. We think it will become the standard, "new normal" in IVF labs, as rapidly and thoroughly as vitrification, blastocyst culture, and intracytoplasmic sperm injection replaced their predecessor technologies. At the time of this review, the AI technology to automate embryo evaluation and selection has robustly matured, and therefore, it is the main focus of this review.
Collapse
Affiliation(s)
| | - Alejandro Chavez-Badiola
- IVF 2.0 LTD, 1 Liverpool Road, Maghull, L31 2HB, Merseyside, UK
- New Hope Fertility Center, Av. Prado Norte 135, Lomas de Chapultepec, CP11000, Mexico City, Mexico
- Reproductive Genetics, School of Biosciences, University of Kent, Canterbury, CT2 7NZ, Kent, UK
| | - Carol Lynn Curchoe
- ART Compass, a Fertility Guidance Technology, Newport Beach, CA, 92660, USA
| |
Collapse
|
14
|
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
| |
Collapse
|
15
|
Hasanzadeh A, Hamblin MR, Kiani J, Noori H, Hardie JM, Karimi M, Shafiee H. Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines? NANO TODAY 2022; 47:101665. [PMID: 37034382 PMCID: PMC10081506 DOI: 10.1016/j.nantod.2022.101665] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.
Collapse
Affiliation(s)
- Akbar Hasanzadeh
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Michael R Hamblin
- Laser Research Centre, Faculty of Health Science, University of Johannesburg, Doornfontein 2028, South Africa
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Jafar Kiani
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Noori
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Joseph M. Hardie
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
| | - Mahdi Karimi
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran 141556559, Iran
- Applied Biotechnology Research Centre, Tehran Medical Science, Islamic Azad University, Tehran 1584743311, Iran
| | - Hadi Shafiee
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
| |
Collapse
|
16
|
Curchoe CL, Tarafdar O, Aquilina MC, Seifer DB. SART CORS IVF registry: looking to the past to shape future perspectives. J Assist Reprod Genet 2022; 39:2607-2616. [PMID: 36269502 PMCID: PMC9722991 DOI: 10.1007/s10815-022-02634-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 10/03/2022] [Indexed: 10/24/2022] Open
Abstract
PURPOSE The SART CORS database is an informative source of IVF clinic-specific linked data that provides cumulative live birth rates from medically assisted reproduction in the United States (US). These data are used to develop best practice guidelines, for research, quality assurance, and post-market surveillance of assisted reproductive technologies. Here, we sought to investigate the key areas of current research focus (higher-order categories), discover gaps or underserved areas of ART research, and examine the potential application and impact of newer ART adjuvants, future data collection, and analysis needs. METHODS We conducted a systematic review (PRISMA guidelines) to quantify unique output metrics of the SART CORS database. Included were SART member reporting clinics: full-length publications from 2004 to 2021 and conference abstracts from 2015 to 2021, the two key timepoints when the SART CORS database underwent transformative shifts in data collection. RESULTS We found 206 abstracts presented from 2015 to 2021, 189 full-length peer-reviewed publications since 2004, with 654 unique authors listed on these publications. A total of 19 publications have been highly impactful, garnering over 100 citations at the time of writing. Several higher-order categories, such as endometriosis and tubal infertility, have few publications. The conversion of conference abstracts to full-length papers ranged from 15 to 35% from 2015 to 2021. CONCLUSIONS A substantial body of literature has been generated by analyzing the SART CORS database. Full-length publications have increased year over year. Some topic areas, such as endometriosis and tubal infertility, may be underrepresented. Conversion of conference abstracts to full-length publications has been low, indicating that more organizational support may be needed to ensure that research is methodologically sound and researchers supported to reach full publication status.
Collapse
Affiliation(s)
| | | | | | - David B Seifer
- Gynecology and Reproductive Sciences, Division of Reproductive Endocrinology and Infertility, Department of Obstetrics, Yale University School of Medicine, New Haven, CT, USA
| |
Collapse
|
17
|
Shingshetty L, Maheshwari A, McLernon DJ, Bhattacharya S. Should we adopt a prognosis-based approach to unexplained infertility? Hum Reprod Open 2022; 2022:hoac046. [PMID: 36382011 PMCID: PMC9662706 DOI: 10.1093/hropen/hoac046] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 09/09/2022] [Indexed: 08/27/2023] Open
Abstract
The treatment of unexplained infertility is a contentious topic that continues to attract a great deal of interest amongst clinicians, patients and policy makers. The inability to identify an underlying pathology makes it difficult to devise effective treatments for this condition. Couples with unexplained infertility can conceive on their own and any proposed intervention needs to offer a better chance of having a baby. Over the years, several prognostic and prediction models based on routinely collected clinical data have been developed, but these are not widely used by clinicians and patients. In this opinion paper, we propose a prognosis-based approach such that a decision to access treatment is based on the estimated chances of natural and treatment-related conception, which, in the same couple, can change over time. This approach avoids treating all couples as a homogeneous group and minimizes unnecessary treatment whilst ensuring access to those who need it early.
Collapse
Affiliation(s)
- Laxmi Shingshetty
- Aberdeen Centre for Reproductive Medicine, NHS Grampian, Aberdeen, UK
| | - Abha Maheshwari
- Aberdeen Centre for Reproductive Medicine, NHS Grampian, Aberdeen, UK
| | - David J McLernon
- Medical Statistics Team, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | | |
Collapse
|
18
|
Comparison of Machine Learning Classification Techniques to Predict Implantation Success in an In Vitro Fertilization Treatment Cycle. Reprod Biomed Online 2022; 45:923-934. [DOI: 10.1016/j.rbmo.2022.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 06/12/2022] [Accepted: 06/20/2022] [Indexed: 11/21/2022]
|
19
|
A Federated Blockchain Approach for Fertility Preservation and Assisted Reproduction in Smart Cities. SMART CITIES 2022. [DOI: 10.3390/smartcities5020031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Modern life is making people infertile. Giving birth later in life is wreaking havoc on our fertility and threatening human survival. Smart cities intend to optimize the quality of life of their citizens by utilizing technology for smarter living. This research first identifies the requirements and business opportunities of using advanced technology for smarter fertility preservation and assisted reproduction in smart cities. A federated blockchain approach is proposed for the alliance of integrated commercial egg banks (ICEBs). In particular, we designed a membership fee rebate (MFR) mechanism that offers incentives for blockchain creations in the egg banking alliance. We formulated the MFR problem into a leader–followers Stackelberg game whose objectives are (1) to maximize the benefits of forming the alliance (the leader) and (2) to maximize the benefits in each ICEB (the follower). We developed an iterative scheme that utilizes mathematical programming techniques to solve the two-level, Stackelberg game problem. With a given set of parameters of the alliance and membership fee function, and the average number of blocks generated for an oocyte, the iterative scheme achieves the optimal solution for the MFR rate per block created. A numerical example demonstrates the feasibility and applicability of the proposed iterative scheme. Numerical results show that it achieves good solutions in adding a small to medium-sized new ICEB to the existing alliance. The proposed federated approach lays the foundation for developing a blockchain-based egg banking platform.
Collapse
|
20
|
Katler QS, Kawwass JF, Hurst BS, Sparks AE, McCulloh DH, Wantman E, Toner JP. Vanquishing multiple pregnancy in in vitro fertilization in the United States-a 25-year endeavor. Am J Obstet Gynecol 2022; 227:129-135. [PMID: 35150636 DOI: 10.1016/j.ajog.2022.02.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 02/03/2022] [Accepted: 02/04/2022] [Indexed: 11/18/2022]
Abstract
The practice of in vitro fertilization has changed tremendously since the birth of the first in vitro fertilization infant in 1978. With the success of early in vitro fertilization programs in the United States, there was a substantial rise in twin births nationwide. In the mid-1990s, more than 30% of in vitro fertilization cycles resulted in twin or higher-order multifetal pregnancies. Since that time, we not only have witnessed improvements in laboratory and treatment efficacy but also have seen a dramatic impact on pregnancy outcomes, specifically regarding twin pregnancies. Because the field evolved and the risks of multifetal pregnancies became more salient, in 2019, the rate of twin pregnancies had dropped to <7% of cycles. This improvement was largely because of technical advancements and revised professional guidance: culturing embryos longer before transfer, improved freezing technology, embryo preimplantation genetic testing, and revised professional guidance regarding the number of embryos to transfer. These developments have led to single-embryo transfer becoming the standard of care in most scenarios. We used national in vitro fertilization surveillance data of all autologous in vitro fertilization cycles from 1996 to 2019 to illustrate trends in the following improved outcomes: autologous embryo transfer cycles involving blastocyst-stage embryos, vitrified embryos, preimplantation genetic testing cycles, total number of embryos being transferred per cycle, and single-embryo transfer usage over time. Among deliveries from autologous embryo transfers, we highlighted trends in singleton births over time and proportion of deliveries involving twins, triplets, quadruplets, or greater. The notable progress in reducing the rate of multifetal pregnancies with in vitro fertilization was largely attributed to a series of technical and clinical actions, culminating in an 80% reduction in the incidence of multiple births without a loss in overall treatment effectiveness.
Collapse
Affiliation(s)
- Quinton S Katler
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Emory University School of Medicine, Atlanta, GA.
| | - Jennifer F Kawwass
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Emory University School of Medicine, Atlanta, GA
| | - Bradley S Hurst
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Atrium Health Carolinas Medical Center, Charlotte, NC
| | - Amy E Sparks
- Division Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA
| | - David H McCulloh
- Department of Obstetrics and Gynecology, New York University Langone Fertility Center, New York University Langone Health, New York, NY
| | | | - James P Toner
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Emory University School of Medicine, Atlanta, GA
| |
Collapse
|
21
|
Kragh MF, Rimestad J, Lassen JT, Berntsen J, Karstoft H. Predicting Embryo Viability Based on Self-Supervised Alignment of Time-Lapse Videos. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:465-475. [PMID: 34596537 DOI: 10.1109/tmi.2021.3116986] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
With self-supervised learning, both labeled and unlabeled data can be used for representation learning and model pretraining. This is particularly relevant when automating the selection of a patient's fertilized eggs (embryos) during a fertility treatment, in which only the embryos that were transferred to the female uterus may have labels of pregnancy. In this paper, we apply a self-supervised video alignment method known as temporal cycle-consistency (TCC) on 38176 time-lapse videos of developing embryos, of which 14550 were labeled. We show how TCC can be used to extract temporal similarities between embryo videos and use these for predicting pregnancy likelihood. Our temporal similarity method outperforms the time alignment measurement (TAM) with an area under the receiver operating characteristic (AUC) of 0.64 vs. 0.56. Compared to existing embryo evaluation models, it places in between a pure temporal and a spatio-temporal model that both require manual annotations. Furthermore, we use TCC for transfer learning in a semi-supervised fashion and show significant performance improvements compared to standard supervised learning, when only a small subset of the dataset is labeled. Specifically, two variants of transfer learning both achieve an AUC of 0.66 compared to 0.63 for supervised learning when 16% of the dataset is labeled.
Collapse
|
22
|
Ueno S, Berntsen J, Ito M, Uchiyama K, Okimura T, Yabuuchi A, Kato K. Pregnancy prediction performance of an annotation-free embryo scoring system on the basis of deep learning after single vitrified-warmed blastocyst transfer: a single-center large cohort retrospective study. Fertil Steril 2021; 116:1172-1180. [PMID: 34246469 DOI: 10.1016/j.fertnstert.2021.06.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 12/26/2022]
Abstract
OBJECTIVE To analyze the performance of an annotation-free embryo scoring system on the basis of deep learning for pregnancy prediction after single vitrified blastocyst transfer (SVBT) compared with the performance of other blastocyst grading systems dependent on annotation or morphology scores. DESIGN A single-center large cohort retrospective study from an independent validation test. SETTING Infertility clinic. PATIENT(S) Patients who underwent SVBT cycles (3,018 cycles, mean ± SD patient age 39.3 ± 4.0 years). INTERVENTION(S) None. MAIN OUTCOME MEASURE(S) The pregnancy prediction performances of each embryo scoring model were compared using the area under curve (AUC) for predicting the fetal heartbeat status for each maternal age group. RESULT(S) The AUCs of the <35 years age group (n = 389) for pregnancy prediction were 0.72 for iDAScore, 0.66 for KIDScore, and 0.64 for the Gardner criteria. The AUC of iDAScore was significantly greater than those of the other two models. For the 35-37 years age group (n = 514), the AUCs were 0.68, 0.68, and 0.65 for iDAScore, KIDScore, and the Gardner criteria, respectively, and were not significantly different. The AUCs of the 38-40 years age group (n = 796) were 0.67 for iDAScore, 0.65 for KIDScore, and 0.64 for the Gardner criteria, and there were no significant differences. The AUCs of the 41-42 years age group (n = 636) were 0.66, 0.66, and 0.63 for iDAScore, KIDScore, and the Gardner criteria, respectively, and there were no significant differences among the pregnancy prediction models. For the >42 years age group (n = 389), the AUCs were 0.76 for iDAScore, 0.75 for KIDScore, and 0.75 for the Gardner criteria, and there were no significant differences. Thus, iDAScore AUC was either the highest or equal to the highest AUC for all age groups, although a significant difference was observed only in the youngest age group. CONCLUSION(S) Our results showed that objective embryo assessment by a completely automatic and annotation-free model, iDAScore, performed as well as or even better than more traditional embryo assessment or annotation-dependent ranking tools. iDAScore could be an optimal pregnancy prediction model after SVBT, especially in young patients.
Collapse
|
23
|
Kragh MF, Karstoft H. Embryo selection with artificial intelligence: how to evaluate and compare methods? J Assist Reprod Genet 2021; 38:1675-1689. [PMID: 34173914 PMCID: PMC8324599 DOI: 10.1007/s10815-021-02254-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 06/02/2021] [Indexed: 12/19/2022] Open
Abstract
Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent years, artificial intelligence (AI) has been used extensively to improve and automate the embryo ranking and selection procedure by extracting relevant information from embryo microscopy images. The AI models are evaluated based on their ability to identify the embryo(s) with the highest chance(s) of achieving a successful pregnancy. Whether such evaluations should be based on ranking performance or pregnancy prediction, however, seems to divide studies. As such, a variety of performance metrics are reported, and comparisons between studies are often made on different outcomes and data foundations. Moreover, superiority of AI methods over manual human evaluation is often claimed based on retrospective data, without any mentions of potential bias. In this paper, we provide a technical view on some of the major topics that divide how current AI models are trained, evaluated and compared. We explain and discuss the most common evaluation metrics and relate them to the two separate evaluation objectives, ranking and prediction. We also discuss when and how to compare AI models across studies and explain in detail how a selection bias is inevitable when comparing AI models against current embryo selection practice in retrospective cohort studies.
Collapse
Affiliation(s)
- Mikkel Fly Kragh
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark.
- Vitrolife A/S, Viby J, Denmark.
| | - Henrik Karstoft
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark
| |
Collapse
|
24
|
Trolice MP, Curchoe C, Quaas AM. Artificial intelligence-the future is now. J Assist Reprod Genet 2021; 38:1607-1612. [PMID: 34231110 PMCID: PMC8260235 DOI: 10.1007/s10815-021-02272-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 06/29/2021] [Indexed: 11/25/2022] Open
Abstract
The pros and cons of artificial intelligence in assisted reproductive technology are presented.
Collapse
Affiliation(s)
- Mark P Trolice
- Obstetrics and Gynecology, University of Central Florida, Orlando, USA.
- The IVF Center, Orlando, FL, USA.
| | | | - Alexander M Quaas
- Division of Reproductive Endocrinology and Infertility, University of California, San Diego, CA, USA
- Reproductive Partners San Diego, San Diego, CA, USA
| |
Collapse
|