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Letterie G. Moonshot. Long shot. Or sure shot. What needs to happen to realize the full potential of AI in the fertility sector? Hum Reprod 2024; 39:1863-1868. [PMID: 38964370 DOI: 10.1093/humrep/deae144] [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: 02/08/2024] [Revised: 06/05/2024] [Indexed: 07/06/2024] Open
Abstract
Quality healthcare requires two critical components: patients' best interests and best decisions to achieve that goal. The first goal is the lodestar, unchanged and unchanging over time. The second component is a more dynamic and rapidly changing paradigm in healthcare. Clinical decision-making has transitioned from an opinion-based paradigm to an evidence-based and data-driven process. A realization that technology and artificial intelligence can bring value adds a third component to the decision process. And the fertility sector is not exempt. The debate about AI is front and centre in reproductive technologies. Launching the transition from a conventional provider-driven decision paradigm to a software-enhanced system requires a roadmap to enable effective and safe implementation. A key nodal point in the ascending arc of AI in the fertility sector is how and when to bring these innovations into the ART routine to improve workflow, outcomes, and bottom-line performance. The evolution of AI in other segments of clinical care would suggest that caution is needed as widespread adoption is urged from several fronts. But the lure and magnitude for the change that these tech tools hold for fertility care remain deeply engaging. Exploring factors that could enhance thoughtful implementation and progress towards a tipping point (or perhaps not) should be at the forefront of any 'next steps' strategy. The objective of this Opinion is to discuss four critical areas (among many) considered essential to successful uptake of any new technology. These four areas include value proposition, innovative disruption, clinical agency, and responsible computing.
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Ortiz JA, Lledó B, Morales R, Máñez-Grau A, Cascales A, Rodríguez-Arnedo A, Castillo JC, Bernabeu A, Bernabeu R. Factors affecting biochemical pregnancy loss (BPL) in preimplantation genetic testing for aneuploidy (PGT-A) cycles: machine learning-assisted identification. Reprod Biol Endocrinol 2024; 22:101. [PMID: 39118049 PMCID: PMC11308629 DOI: 10.1186/s12958-024-01271-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Accepted: 07/29/2024] [Indexed: 08/10/2024] Open
Abstract
PURPOSE To determine the factors influencing the likelihood of biochemical pregnancy loss (BPL) after transfer of a euploid embryo from preimplantation genetic testing for aneuploidy (PGT-A) cycles. METHODS The study employed an observational, retrospective cohort design, encompassing 6020 embryos from 2879 PGT-A cycles conducted between February 2013 and September 2021. Trophectoderm biopsies in day 5 (D5) or day 6 (D6) blastocysts were analyzed by next generation sequencing (NGS). Only single embryo transfers (SET) were considered, totaling 1161 transfers. Of these, 49.9% resulted in positive pregnancy tests, with 18.3% experiencing BPL. To establish a predictive model for BPL, both classical statistical methods and five different supervised classification machine learning algorithms were used. A total of forty-seven factors were incorporated as predictor variables in the machine learning models. RESULTS Throughout the optimization process for each model, various performance metrics were computed. Random Forest model emerged as the best model, boasting the highest area under the ROC curve (AUC) value of 0.913, alongside an accuracy of 0.830, positive predictive value of 0.857, and negative predictive value of 0.807. For the selected model, SHAP (SHapley Additive exPlanations) values were determined for each of the variables to establish which had the best predictive ability. Notably, variables pertaining to embryo biopsy demonstrated the greatest predictive capacity, followed by factors associated with ovarian stimulation (COS), maternal age, and paternal age. CONCLUSIONS The Random Forest model had a higher predictive power for identifying BPL occurrences in PGT-A cycles. Specifically, variables associated with the embryo biopsy procedure (biopsy day, number of biopsied embryos, and number of biopsied cells) and ovarian stimulation (number of oocytes retrieved and duration of stimulation), exhibited the strongest predictive power.
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Affiliation(s)
- José A Ortiz
- Instituto Bernabeu, Molecular Biology Department, Alicante, Spain.
| | - B Lledó
- Instituto Bernabeu, Molecular Biology Department, Alicante, Spain
| | - R Morales
- Instituto Bernabeu, Molecular Biology Department, Alicante, Spain
| | - A Máñez-Grau
- Instituto Bernabeu, Reproductive Biology, Alicante, Spain
| | - A Cascales
- Instituto Bernabeu, Molecular Biology Department, Alicante, Spain
| | | | | | - A Bernabeu
- Instituto Bernabeu, Reproductive Medicine, Alicante, Spain
- Cátedra de Medicina Comunitaria y Salud Reproductiva, Miguel Hernández University, Alicante, Spain
| | - R Bernabeu
- Instituto Bernabeu, Reproductive Medicine, Alicante, Spain
- Cátedra de Medicina Comunitaria y Salud Reproductiva, Miguel Hernández University, Alicante, Spain
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AlSaad R, Abd-Alrazaq A, Choucair F, Ahmed A, Aziz S, Sheikh J. Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review. J Med Internet Res 2024; 26:e53396. [PMID: 38967964 PMCID: PMC11259766 DOI: 10.2196/53396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 04/08/2024] [Accepted: 05/22/2024] [Indexed: 07/06/2024] Open
Abstract
BACKGROUND In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient's response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation. OBJECTIVE The objective of this review is to comprehensively examine the literature to explore the characteristics of AI models used for predicting ovarian stimulation outcomes in the context of IVF. METHODS A total of 6 electronic databases were searched for peer-reviewed literature published before August 2023, using the concepts of IVF and AI, along with their related terms. Records were independently screened by 2 reviewers against the eligibility criteria. The extracted data were then consolidated and presented through narrative synthesis. RESULTS Upon reviewing 1348 articles, 30 met the predetermined inclusion criteria. The literature primarily focused on the number of oocytes retrieved as the main predicted outcome. Microscopy images stood out as the primary ground truth reference. The reviewed studies also highlighted that the most frequently adopted stimulation protocol was the gonadotropin-releasing hormone (GnRH) antagonist. In terms of using trigger medication, human chorionic gonadotropin (hCG) was the most commonly selected option. Among the machine learning techniques, the favored choice was the support vector machine. As for the validation of AI algorithms, the hold-out cross-validation method was the most prevalent. The area under the curve was highlighted as the primary evaluation metric. The literature exhibited a wide variation in the number of features used for AI algorithm development, ranging from 2 to 28,054 features. Data were mostly sourced from patient demographics, followed by laboratory data, specifically hormonal levels. Notably, the vast majority of studies were restricted to a single infertility clinic and exclusively relied on nonpublic data sets. CONCLUSIONS These insights highlight an urgent need to diversify data sources and explore varied AI techniques for improved prediction accuracy and generalizability of AI models for the prediction of ovarian stimulation outcomes. Future research should prioritize multiclinic collaborations and consider leveraging public data sets, aiming for more precise AI-driven predictions that ultimately boost patient care and IVF success rates.
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Affiliation(s)
- Rawan AlSaad
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Fadi Choucair
- Reproductive Medicine Unit, Sidra Medicine, Doha, Qatar
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Javaid Sheikh
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
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Boylan CF, Sambo KM, Neal-Perry G, Brayboy LM. Ex ovo omnia-why don't we know more about egg quality via imaging? Biol Reprod 2024; 110:1201-1212. [PMID: 38767842 PMCID: PMC11180616 DOI: 10.1093/biolre/ioae080] [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: 05/14/2024] [Accepted: 05/17/2024] [Indexed: 05/22/2024] Open
Abstract
Determining egg quality is the foremost challenge in assisted reproductive technology (ART). Although extensive advances have been made in multiple areas of ART over the last 40 years, oocyte quality assessment tools have not much evolved beyond standard morphological observation. The oocyte not only delivers half of the nuclear genetic material and all of the mitochondrial DNA to an embryo but also provides complete developmental support during embryonic growth. Oocyte mitochondrial numbers far exceed those of any somatic cell, yet little work has been done to evaluate the mitochondrial bioenergetics of an oocyte. Current standard oocyte assessment in in vitro fertilization (IVF) centers include the observation of oocytes and their surrounding cell complex (cumulus cells) via stereomicroscope or inverted microscope, which is largely primitive. Additional oocyte assessments include polar body grading and polarized light meiotic spindle imaging. However, the evidence regarding the aforementioned methods of oocyte quality assessment and IVF outcomes is contradictory and non-reproducible. High-resolution microscopy techniques have also been implemented in animal and human models with promising outcomes. The current era of oocyte imaging continues to evolve with discoveries in artificial intelligence models of oocyte morphology selection albeit at a slow rate. In this review, the past, current, and future oocyte imaging techniques will be examined with the goal of drawing attention to the gap which limits our ability to assess oocytes in real time. The implications of improved oocyte imaging techniques on patients undergoing IVF will be discussed as well as the need to develop point of care oocyte assessment testing in IVF labs.
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Affiliation(s)
- Caitlin F Boylan
- University of North Carolina, Chapel Hill, NC, USA
- Eastern Virginia Medical School, Norfolk, VA, USA
| | - Keshia M Sambo
- Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | | | - Lynae M Brayboy
- Department of Neuropediatrics Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Klinik für Pädiatrie m. S. Neurologie, Charité Campus Virchow Klinikum, Berlin, Germany
- Department of Reproductive Biology, Bedford Research Foundation, Bedford, MA, USA
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Silvestris E, D’Oronzo S, Petracca EA, D’Addario C, Cormio G, Loizzi V, Canosa S, Corrado G. Fertility Preservation in the Era of Immuno-Oncology: Lights and Shadows. J Pers Med 2024; 14:431. [PMID: 38673058 PMCID: PMC11050999 DOI: 10.3390/jpm14040431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/05/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
In recent years, immuno-oncology has revolutionized the cancer treatment field by harnessing the immune system's power to counteract cancer cells. While this innovative approach holds great promise for improving cancer outcomes, it also raises important considerations related to fertility and reproductive toxicity. In fact, most young females receiving gonadotoxic anti-cancer treatments undergo iatrogenic ovarian exhaustion, resulting in a permanent illness that precludes the vocation of motherhood as a natural female sexual identity. Although commonly used, oocyte cryopreservation for future in vitro fertilization and even ovarian cortex transplantation are considered unsafe procedures in cancer patients due to their oncogenic risks; whereas, ovarian stem cells might support neo-oogenesis, providing a novel stemness model of regenerative medicine for future fertility preservation programs in oncology. Recent scientific evidence has postulated that immune checkpoint inhibitors (ICIs) might in some way reduce fertility by inducing either primary or secondary hypogonadism, whose incidence and mechanisms are not yet known. Therefore, considering the lack of data, it is currently not possible to define the most suitable FP procedure for young patients who are candidates for ICIs. In this report, we will investigate the few available data concerning the molecular regulation of ICI therapy and their resulting gonadal toxicity, to hypothesize the most suitable fertility preservation strategy for patients receiving these drugs.
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Affiliation(s)
- Erica Silvestris
- Gynecologic Oncology Unit, IRCCS Istituto Tumori “Giovanni Paolo II” Bari, 70124 Bari, Italy; (E.A.P.); (G.C.); (V.L.)
| | - Stella D’Oronzo
- Department of Interdisciplinary Medicine (DIM), University of Bari “Aldo Moro”, 70121 Bari, Italy;
- Division of Medical Oncology, A.O.U. Consorziale Policlinico di Bari, 70124 Bari, Italy
| | - Easter Anna Petracca
- Gynecologic Oncology Unit, IRCCS Istituto Tumori “Giovanni Paolo II” Bari, 70124 Bari, Italy; (E.A.P.); (G.C.); (V.L.)
| | - Claudia D’Addario
- Department of Interdisciplinary Medicine (DIM), University of Bari “Aldo Moro”, 70121 Bari, Italy;
- Division of Medical Oncology, A.O.U. Consorziale Policlinico di Bari, 70124 Bari, Italy
| | - Gennaro Cormio
- Gynecologic Oncology Unit, IRCCS Istituto Tumori “Giovanni Paolo II” Bari, 70124 Bari, Italy; (E.A.P.); (G.C.); (V.L.)
- Department of Interdisciplinary Medicine (DIM), University of Bari “Aldo Moro”, 70121 Bari, Italy;
| | - Vera Loizzi
- Gynecologic Oncology Unit, IRCCS Istituto Tumori “Giovanni Paolo II” Bari, 70124 Bari, Italy; (E.A.P.); (G.C.); (V.L.)
- Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari “Aldo Moro”, 70124 Bari, Italy
| | - Stefano Canosa
- IVIRMA, Global Research Alliance, LIVET, 10126 Turin, Italy;
| | - Giacomo Corrado
- Gynecologic Oncology Unit, Department of Woman, Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00136 Roma, Italy;
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Güell E. Criteria for implementing artificial intelligence systems in reproductive medicine. Clin Exp Reprod Med 2024; 51:1-12. [PMID: 38035589 PMCID: PMC10914497 DOI: 10.5653/cerm.2023.06009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/31/2023] [Indexed: 12/02/2023] Open
Abstract
This review article discusses the integration of artificial intelligence (AI) in assisted reproductive technology and provides key concepts to consider when introducing AI systems into reproductive medicine practices. The article highlights the various applications of AI in reproductive medicine and discusses whether to use commercial or in-house AI systems. This review also provides criteria for implementing new AI systems in the laboratory and discusses the factors that should be considered when introducing AI in the laboratory, including the user interface, scalability, training, support, follow-up, cost, ethics, and data quality. The article emphasises the importance of ethical considerations, data quality, and continuous algorithm updates to ensure the accuracy and safety of AI systems.
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Affiliation(s)
- Enric Güell
- CONSULTFIV, Valls, Spain
- Procrear, Reus, Spain
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Ghayda RA, Cannarella R, Calogero AE, Shah R, Rambhatla A, Zohdy W, Kavoussi P, Avidor-Reiss T, Boitrelle F, Mostafa T, Saleh R, Toprak T, Birowo P, Salvio G, Calik G, Kuroda S, Kaiyal RS, Ziouziou I, Crafa A, Phuoc NHV, Russo GI, Durairajanayagam D, Al-Hashimi M, Hamoda TAAAM, Pinggera GM, Adriansjah R, Maldonado Rosas I, Arafa M, Chung E, Atmoko W, Rocco L, Lin H, Huyghe E, Kothari P, Solorzano Vazquez JF, Dimitriadis F, Garrido N, Homa S, Falcone M, Sabbaghian M, Kandil H, Ko E, Martinez M, Nguyen Q, Harraz AM, Serefoglu EC, Karthikeyan VS, Tien DMB, Jindal S, Micic S, Bellavia M, Alali H, Gherabi N, Lewis S, Park HJ, Simopoulou M, Sallam H, Ramirez L, Colpi G, Agarwal A. Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics. World J Mens Health 2024; 42:39-61. [PMID: 37382282 PMCID: PMC10782130 DOI: 10.5534/wjmh.230050] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/10/2023] [Accepted: 03/17/2023] [Indexed: 06/30/2023] Open
Abstract
Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.
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Affiliation(s)
- Ramy Abou Ghayda
- Urology Institute, University Hospitals, Case Western Reserve University, Cleveland, OH, USA
| | - Rossella Cannarella
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
- Glickman Urological & Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Aldo E. Calogero
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Rupin Shah
- Department of Urology, Lilavati Hospital and Research Centre, Mumbai, India
| | - Amarnath Rambhatla
- Department of Urology, Henry Ford Health System, Vattikuti Urology Institute, Detroit, MI, USA
| | - Wael Zohdy
- Andrology and STDs, Cairo University, Cairo, Egypt
| | - Parviz Kavoussi
- Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Tomer Avidor-Reiss
- Department of Biological Sciences, University of Toledo, Toledo, OH, USA
- Department of Urology, College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Florence Boitrelle
- Reproductive Biology, Fertility Preservation, Andrology, CECOS, Poissy Hospital, Poissy, France
- Department of Biology, Reproduction, Epigenetics, Environment, and Development, Paris Saclay University, UVSQ, INRAE, BREED, Paris, France
| | - Taymour Mostafa
- Andrology, Sexology & STIs Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Ramadan Saleh
- Department of Dermatology, Venereology and Andrology, Faculty of Medicine, Sohag University, Sohag, Egypt
| | - Tuncay Toprak
- Department of Urology, Fatih Sultan Mehmet Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
| | - Ponco Birowo
- Department of Urology, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Gianmaria Salvio
- Department of Endocrinology, Polytechnic University of Marche, Ancona, Italy
| | - Gokhan Calik
- Department of Urology, Istanbul Medipol University, Istanbul, Turkey
| | - Shinnosuke Kuroda
- Glickman Urological & Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
- Department of Urology, Reproduction Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Raneen Sawaid Kaiyal
- Glickman Urological & Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Imad Ziouziou
- Department of Urology, College of Medicine and Pharmacy, Ibn Zohr University, Agadir, Morocco
| | - Andrea Crafa
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Nguyen Ho Vinh Phuoc
- Department of Andrology, Binh Dan Hospital, Ho Chi Minh City, Vietnam
- Department of Urology and Andrology, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Vietnam
| | | | - Damayanthi Durairajanayagam
- Department of Physiology, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Selangor, Malaysia
| | - Manaf Al-Hashimi
- Department of Urology, Burjeel Hospital, Abu Dhabi, United Arab Emirates (UAE)
- Khalifa University, College of Medicine and Health Science, Abu Dhabi, United Arab Emirates (UAE)
| | - Taha Abo-Almagd Abdel-Meguid Hamoda
- Department of Urology, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Urology, Faculty of Medicine, Minia University, El-Minia, Egypt
| | | | - Ricky Adriansjah
- Department of Urology, Hasan Sadikin General Hospital, Universitas Padjadjaran, Banding, Indonesia
| | | | - Mohamed Arafa
- Department of Urology, Hamad Medical Corporation, Doha, Qatar
- Department of Urology, Weill Cornell Medical-Qatar, Doha, Qatar
| | - Eric Chung
- Department of Urology, Princess Alexandra Hospital, University of Queensland, Brisbane QLD, Australia
| | - Widi Atmoko
- Department of Urology, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Lucia Rocco
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”, Caserta, Italy
| | - Haocheng Lin
- Department of Urology, Peking University Third Hospital, Peking University, Beijing, China
| | - Eric Huyghe
- Department of Urology and Andrology, University Hospital of Toulouse, Toulouse, France
| | - Priyank Kothari
- Department of Urology, B.Y.L. Nair Charitable Hospital, Topiwala National Medical College, Mumbai, India
| | | | - Fotios Dimitriadis
- Department of Urology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nicolas Garrido
- IVIRMA Global Research Alliance, IVI Foundation, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
| | - Sheryl Homa
- Department of Biosciences, University of Kent, Canterbury, United Kingdom
| | - Marco Falcone
- Department of Urology, Molinette Hospital, A.O.U. Città della Salute e della Scienza, University of Turin, Torino, Italy
| | - Marjan Sabbaghian
- Department of Andrology, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
| | | | - Edmund Ko
- Department of Urology, Loma Linda University Health, Loma Linda, CA, USA
| | - Marlon Martinez
- Section of Urology, Department of Surgery, University of Santo Tomas Hospital, Manila, Philippines
| | - Quang Nguyen
- Section of Urology, Department of Surgery, University of Santo Tomas Hospital, Manila, Philippines
- Center for Andrology and Sexual Medicine, Viet Duc University Hospital, Hanoi, Vietnam
- Department of Urology, Andrology and Sexual Medicine, University of Medicine and Pharmacy, Vietnam National University, Hanoi, Vietnam
| | - Ahmed M. Harraz
- Urology and Nephrology Center, Mansoura University, Mansoura, Egypt
- Department of Surgery, Urology Unit, Farwaniya Hospital, Farwaniya, Kuwait
- Department of Urology, Sabah Al Ahmad Urology Center, Kuwait City, Kuwait
| | - Ege Can Serefoglu
- Department of Urology, Biruni University School of Medicine, Istanbul, Turkey
| | | | - Dung Mai Ba Tien
- Department of Andrology, Binh Dan Hospital, Ho Chi Minh City, Vietnam
| | - Sunil Jindal
- Department of Andrology and Reproductive Medicine, Jindal Hospital, Meerut, India
| | - Sava Micic
- Department of Andrology, Uromedica Polyclinic, Belgrade, Serbia
| | - Marina Bellavia
- Andrology and IVF Center, Next Fertility Procrea, Lugano, Switzerland
| | - Hamed Alali
- King Fahad Specialist Hospital, Dammam, Saudi Arabia
| | - Nazim Gherabi
- Andrology Committee of the Algerian Association of Urology, Algiers, Algeria
| | - Sheena Lewis
- Examen Lab Ltd., Northern Ireland, United Kingdom
| | - Hyun Jun Park
- Department of Urology, Pusan National University School of Medicine, Busan, Korea
- Medical Research Institute of Pusan National University Hospital, Busan, Korea
| | - Mara Simopoulou
- Department of Experimental Physiology, School of Health Sciences, Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Hassan Sallam
- Alexandria University Faculty of Medicine, Alexandria, Egypt
| | - Liliana Ramirez
- IVF Laboratory, CITMER Reproductive Medicine, Mexico City, Mexico
| | - Giovanni Colpi
- Andrology and IVF Center, Next Fertility Procrea, Lugano, Switzerland
| | - Ashok Agarwal
- Global Andrology Forum, Moreland Hills, OH, USA
- Cleveland Clinic, Cleveland, OH, USA
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Wang S, Chen L, Sun H. Interpretable artificial intelligence-assisted embryo selection improved single-blastocyst transfer outcomes: a prospective cohort study. Reprod Biomed Online 2023; 47:103371. [PMID: 37839212 DOI: 10.1016/j.rbmo.2023.103371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/01/2023] [Accepted: 08/23/2023] [Indexed: 10/17/2023]
Abstract
RESEARCH QUESTION What is the pregnancy and neonatal outcomes of an interpretable artificial intelligence (AI) model for embryo selection in a prospective clinical trial? DESIGN This single-centre prospective cohort study was carried out from October 2021 to March 2022. A total of 330 eligible patients were assigned to their preferred groups, with 250 patients undergoing a fresh single-blastocyst transfer cycle after the exclusion criteria had been applied. For the AI-assisted group (AAG), embryologists selected the embryos for transfer based on the ranking recommendations provided by an interpretable AI system, while with the manual group, embryologists used the Gardner grading system to make their decisions. RESULTS The implantation rate was significantly higher in the AAG than the manual group (80.87% versus 68.15%, P = 0.022). No significant difference was found in terms of monozygotic twin rate, miscarriage rate, live birth rate and ectopic pregnancy rate between the groups. Furthermore, there was no significant difference in terms of neonatal outcomes, including gestational weeks, premature birth rate, birth height, birthweight, sex ratio at birth and newborn malformation rate. The consensus rate between the AI and retrospective analysis by the embryologists was significantly higher for good-quality embryos (i.e. grade 4BB or higher) versus poor-quality embryos (i.e. less than 4BB) (84.71% versus 25%, P < 0.001). CONCLUSIONS These prospective trial results suggest that the proposed AI system could effectively help embryologists to improve the implantation rate with single-blastocyst transfer compared with traditional manual evaluation methods.
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Affiliation(s)
- Shanshan Wang
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Lei Chen
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Haixiang Sun
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
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Gallagher MT, Krasauskaite I, Kirkman-Brown JC. Only the Best of the Bunch-Sperm Preparation Is Not Just about Numbers. Semin Reprod Med 2023; 41:273-278. [PMID: 38113923 DOI: 10.1055/s-0043-1777756] [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: 12/21/2023]
Abstract
In this Seminar, we present an overview of the current and emerging methods and technologies for optimizing the man and the sperm sample for fertility treatment. We argue that sperms are the secret to success, and that there are many avenues for improving both treatment and basic understanding of their role in outcomes. These outcomes encompass not just whether treatment is successful or not, but the wider intergenerational health of the offspring. We discuss outstanding challenges and opportunities of new technologies such as microfluidics and artificial intelligence, including potential pitfalls and advantages. This article aims to provide a comprehensive overview of the importance of sperm in fertility treatment and suggests future directions for research and innovation.
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Affiliation(s)
- Meurig T Gallagher
- Centre for Human Reproductive Science, Institute of Metabolism and Systems Research, University of Birmingham and Birmingham Women's Fertility Centre, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, B15 2TT, United Kingdom
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Ingrida Krasauskaite
- Centre for Human Reproductive Science, Institute of Metabolism and Systems Research, University of Birmingham and Birmingham Women's Fertility Centre, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, B15 2TT, United Kingdom
| | - Jackson C Kirkman-Brown
- Centre for Human Reproductive Science, Institute of Metabolism and Systems Research, University of Birmingham and Birmingham Women's Fertility Centre, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, B15 2TT, United Kingdom
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10
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Li LT, Haley LC, Boyd AK, Bernstam EV. Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review. J Biomed Inform 2023; 147:104531. [PMID: 37884177 DOI: 10.1016/j.jbi.2023.104531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 09/14/2023] [Accepted: 10/22/2023] [Indexed: 10/28/2023]
Abstract
INTRODUCTION The use of artificial intelligence (AI), particularly machine learning and predictive analytics, has shown great promise in health care. Despite its strong potential, there has been limited use in health care settings. In this systematic review, we aim to determine the main barriers to successful implementation of AI in healthcare and discuss potential ways to overcome these challenges. METHODS We conducted a literature search in PubMed (1/1/2001-1/1/2023). The search was restricted to publications in the English language, and human study subjects. We excluded articles that did not discuss AI, machine learning, predictive analytics, and barriers to the use of these techniques in health care. Using grounded theory methodology, we abstracted concepts to identify major barriers to AI use in medicine. RESULTS We identified a total of 2,382 articles. After reviewing the 306 included papers, we developed 19 major themes, which we categorized into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). These themes included: Lack of Explainability, Need for Validation Protocols, Need for Standards for Interoperability, Need for Reporting Guidelines, Need for Standardization of Performance Metrics, Lack of Plan for Updating Algorithm, Job Loss, Skills Loss, Workflow Challenges, Loss of Patient Autonomy and Consent, Disturbing the Patient-Clinician Relationship, Lack of Trust in AI, Logistical Challenges, Lack of strategic plan, Lack of Cost-effectiveness Analysis and Proof of Efficacy, Privacy, Liability, Bias and Social Justice, and Education. CONCLUSION We identified 19 major barriers to the use of AI in healthcare and categorized them into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). Future studies should expand on barriers in pediatric care and focus on developing clearly defined protocols to overcome these barriers.
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Affiliation(s)
- Linda T Li
- Department of Surgery, Division of Pediatric Surgery, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, United States; McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States.
| | - Lauren C Haley
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Alexandra K Boyd
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Elmer V Bernstam
- McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States; McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
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11
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Chiappetta V, Innocenti F, Coticchio G, Ahlström A, Albricci L, Badajoz V, Hebles M, Gallardo M, Benini F, Canosa S, Kumpošt J, Milton K, Montanino Oliva D, Maggiulli R, Rienzi L, Cimadomo D. Discard or not discard, that is the question: an international survey across 117 embryologists on the clinical management of borderline quality blastocysts. Hum Reprod 2023; 38:1901-1909. [PMID: 37649342 DOI: 10.1093/humrep/dead174] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/02/2023] [Indexed: 09/01/2023] Open
Abstract
STUDY QUESTION Do embryologists from different European countries agree on embryo disposition decisions ('use' or 'discard') about Day 7 (>144 h post-insemination) and/or low-quality blastocysts (LQB; SUMMARY ANSWER The prevalence of 'discard' answers was 38.7%; nevertheless, embryologists' agreement was overall just fair (Fleiss-k = 0.26). WHAT IS KNOWN ALREADY The utilization of LQBs and adoption of culture beyond 144 h post-insemination is increasing worldwide. Although morphology and morphokinetics are associated with embryo developmental competence, previous studies demonstrated significant interobserver variability among embryologists regarding embryo quality assessment and disposition decisions for borderline quality blastocysts. STUDY DESIGN, SIZE, DURATION An anonymous survey was run in a large network of IVF centers. A total of 117 embryologists from 6 European countries and 29 IVF centers filled in the survey. Randomly selected anonymous time-lapse videos of 50 Day 7 and/or LQB whole embryo preimplantation development were assessed by the embryologists. The key information on patients/cycles was provided along with each video. All cycles entailed preimplantation genetic testing for aneuploidies. Each embryologist specified whether he/she would have discarded or used ('transfer-fresh'/'cryopreserve'/'biopsy') any embryo. Inter-rater agreement was measured with Fleiss-k. PARTICIPANTS/MATERIALS, SETTING, METHODS Examiners were asked about their years of experience, center location, average number of cycles and average maternal age, number of colleagues, and use of time-lapse incubators at their centers. All participants were blinded to artificial intelligence (AI) scores generated by two commercially available software packages, chromosomal diagnosis (all blastocysts were tested for aneuploidies), and clinical outcomes after vitrified-warmed euploid single blastocyst transfer. These data were known only by one embryologist not involved in the survey. MAIN RESULTS AND THE ROLE OF CHANCE Participants were Italian (40%, N = 47), Spanish (24%, N = 28), Portuguese (5%, N = 6), Czech (5%, N = 6), Swedish (23%, N = 27), and Icelandic (3%, N = 3). In total, 2263 (38.7%) 'discard' and 3587 (61.3%) 'use' decisions were recorded. Czech, Portuguese, and Italian embryologists expressed lower 'discard' decision rates (mean ± SD 17 ± 7%, range 8-24%; 23 ± 14% range 4-46%; and 27 ± 18% range 2-72%, respectively), while Spanish gave intermediate (37 ± 16% range 4-66%) and Nordic gave higher (67 ± 11% range 40-90%) rates. The prevalence of 'discard' answers was 38.7% out of 5850 choices (mean per embryologist: 39 ± 23% range 2-90%). Only embryologists' country and IVF group were associated with this rate. Overall agreement among embryologists was fair (Fleiss-k = 0.26). The prevalence of 'discard' responses per embryo was 37 ± 24% (range 2-87%). Only the number of sibling blastocysts influenced this rate (i.e. the larger the cohort, the higher the inclination to 'discard'). No difference was shown for the two scores between euploid and aneuploid borderline quality blastocysts, while the embryologists were, by chance, more prone to 'discard' the latter (28.3 ± 21% range 9-71% versus 41.6 ± 24.8% range 2-87%, respectively). LIMITATIONS, REASONS FOR CAUTION The survey included only private IVF clinics located in Europe. Moreover, a key variable is missing, namely patients' access to care. Indeed, all embryologists involved in the survey were part of the same network of private IVF clinics, while the embryo disposition decisions might be different in a public setting. WIDER IMPLICATIONS OF THE FINDINGS Decision-making by European embryologists regarding Day 7 embryos or LQBs is inconsistent with putative clinical consequences, especially in patients with low prognosis. Although the embryologists could make decisions independent from their local regulations, their mindset and clinical background influenced their choices. In the future, AI tools should be trained to assess borderline quality embryos and empowered with cost-effectiveness information to support embryologists' decisions with more objective assessments. STUDY FUNDING/COMPETING INTEREST(S) No external funding was obtained for this study. The authors have no conflict of interest to declare. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- Viviana Chiappetta
- IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
| | - Federica Innocenti
- IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
| | | | | | - Laura Albricci
- IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
| | | | - Maria Hebles
- IVIRMA Global Research Alliance, GINEMED, Sevilla, Spain
| | | | | | | | - Jiří Kumpošt
- IVIRMA Global Research Alliance, FERTICARE, Prague, Czech Republic
| | - Katarina Milton
- IVIRMA Global Research Alliance, CARL VON LINNÈ KLINIKEN, Uppsala, Sweden
| | - Diletta Montanino Oliva
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Pavia, Italy
| | - Roberta Maggiulli
- IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
| | - Laura Rienzi
- IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
- Department of Biomolecular Sciences, University of Urbino "Carlo Bo", Urbino, Italy
| | - Danilo Cimadomo
- IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
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Chervenak J, Lieman H, Blanco-Breindel M, Jindal S. The promise and peril of using a large language model to obtain clinical information: ChatGPT performs strongly as a fertility counseling tool with limitations. Fertil Steril 2023; 120:575-583. [PMID: 37217092 DOI: 10.1016/j.fertnstert.2023.05.151] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 05/01/2023] [Accepted: 05/12/2023] [Indexed: 05/24/2023]
Abstract
OBJECTIVE To compare the responses of the large language model-based "ChatGPT" to reputable sources when given fertility-related clinical prompts. DESIGN The "Feb 13" version of ChatGPT by OpenAI was tested against established sources relating to patient-oriented clinical information: 17 "frequently asked questions (FAQs)" about infertility on the Centers for Disease Control (CDC) Website, 2 validated fertility knowledge surveys, the Cardiff Fertility Knowledge Scale and the Fertility and Infertility Treatment Knowledge Score, as well as the American Society for Reproductive Medicine committee opinion "optimizing natural fertility." SETTING Academic medical center. PATIENT(S) Online AI Chatbot. INTERVENTION(S) Frequently asked questions, survey questions and rephrased summary statements were entered as prompts in the chatbot over a 1-week period in February 2023. MAIN OUTCOME MEASURE(S) For FAQs from CDC: words/response, sentiment analysis polarity and objectivity, total factual statements, rate of statements that were incorrect, referenced a source, or noted the value of consulting providers. FOR FERTILITY KNOWLEDGE SURVEYS Percentile according to published population data. FOR COMMITTEE OPINION Whether response to conclusions rephrased as questions identified missing facts. RESULT(S) When administered the CDC's 17 infertility FAQ's, ChatGPT produced responses of similar length (207.8 ChatGPT vs. 181.0 CDC words/response), factual content (8.65 factual statements/response vs. 10.41), sentiment polarity (mean 0.11 vs. 0.11 on a scale of -1 (negative) to 1 (positive)), and subjectivity (mean 0.42 vs. 0.35 on a scale of 0 (objective) to 1 (subjective)). In total, 9 (6.12%) of 147 ChatGPT factual statements were categorized as incorrect, and only 1 (0.68%) statement cited a reference. ChatGPT would have been at the 87th percentile of Bunting's 2013 international cohort for the Cardiff Fertility Knowledge Scale and at the 95th percentile on the basis of Kudesia's 2017 cohort for the Fertility and Infertility Treatment Knowledge Score. ChatGPT reproduced the missing facts for all 7 summary statements from "optimizing natural fertility." CONCLUSION(S) A February 2023 version of "ChatGPT" demonstrates the ability of generative artificial intelligence to produce relevant, meaningful responses to fertility-related clinical queries comparable to established sources. Although performance may improve with medical domain-specific training, limitations such as the inability to reliably cite sources and the unpredictable possibility of fabricated information may limit its clinical use.
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Affiliation(s)
- Joseph Chervenak
- Albert Einstein College of Medicine/Montefiore's Institute for Reproductive Medicine and Health, Hartsdale, New York.
| | - Harry Lieman
- Albert Einstein College of Medicine/Montefiore's Institute for Reproductive Medicine and Health, Hartsdale, New York
| | - Miranda Blanco-Breindel
- Albert Einstein College of Medicine/Montefiore's Institute for Reproductive Medicine and Health, Hartsdale, New York
| | - Sangita Jindal
- Albert Einstein College of Medicine/Montefiore's Institute for Reproductive Medicine and Health, Hartsdale, New York
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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.
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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.
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14
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Cimadomo D, Chiappetta V, Innocenti F, Saturno G, Taggi M, Marconetto A, Casciani V, Albricci L, Maggiulli R, Coticchio G, Ahlström A, Berntsen J, Larman M, Borini A, Vaiarelli A, Ubaldi FM, Rienzi L. Towards Automation in IVF: Pre-Clinical Validation of a Deep Learning-Based Embryo Grading System during PGT-A Cycles. J Clin Med 2023; 12:1806. [PMID: 36902592 PMCID: PMC10002983 DOI: 10.3390/jcm12051806] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/13/2023] [Accepted: 02/21/2023] [Indexed: 02/26/2023] Open
Abstract
Preimplantation genetic testing for aneuploidies (PGT-A) is arguably the most effective embryo selection strategy. Nevertheless, it requires greater workload, costs, and expertise. Therefore, a quest towards user-friendly, non-invasive strategies is ongoing. Although insufficient to replace PGT-A, embryo morphological evaluation is significantly associated with embryonic competence, but scarcely reproducible. Recently, artificial intelligence-powered analyses have been proposed to objectify and automate image evaluations. iDAScore v1.0 is a deep-learning model based on a 3D convolutional neural network trained on time-lapse videos from implanted and non-implanted blastocysts. It is a decision support system for ranking blastocysts without manual input. This retrospective, pre-clinical, external validation included 3604 blastocysts and 808 euploid transfers from 1232 cycles. All blastocysts were retrospectively assessed through the iDAScore v1.0; therefore, it did not influence embryologists' decision-making process. iDAScore v1.0 was significantly associated with embryo morphology and competence, although AUCs for euploidy and live-birth prediction were 0.60 and 0.66, respectively, which is rather comparable to embryologists' performance. Nevertheless, iDAScore v1.0 is objective and reproducible, while embryologists' evaluations are not. In a retrospective simulation, iDAScore v1.0 would have ranked euploid blastocysts as top quality in 63% of cases with one or more euploid and aneuploid blastocysts, and it would have questioned embryologists' ranking in 48% of cases with two or more euploid blastocysts and one or more live birth. Therefore, iDAScore v1.0 may objectify embryologists' evaluations, but randomized controlled trials are required to assess its clinical value.
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Affiliation(s)
- Danilo Cimadomo
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | - Viviana Chiappetta
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | - Federica Innocenti
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | - Gaia Saturno
- Department of Biology and Biotechnology “Lazzaro Spallanzani”, University of Pavia, 27100 Pavia, Italy
| | - Marilena Taggi
- Department of Biology and Biotechnology “Lazzaro Spallanzani”, University of Pavia, 27100 Pavia, Italy
| | - Anabella Marconetto
- University Institute of Reproductive Medicine, National University of Cordoba, Cordoba 5187, Argentina
| | - Valentina Casciani
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | - Laura Albricci
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | - Roberta Maggiulli
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | | | | | | | - Mark Larman
- Vitrolife Sweden AB, 421 32 Göteborg, Sweden
| | | | - Alberto Vaiarelli
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
| | | | - Laura Rienzi
- Clinica Valle Giulia, GeneraLife IVF, Via De Notaris 2B, 00197 Rome, Italy
- Department of Biomolecular Sciences, University of Urbino “Carlo Bo”, 61029 Urbino, Italy
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15
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Glatstein I, Chavez-Badiola A, Curchoe CL. New frontiers in embryo selection. J Assist Reprod Genet 2023; 40:223-234. [PMID: 36609943 PMCID: PMC9935769 DOI: 10.1007/s10815-022-02708-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 12/23/2022] [Indexed: 01/09/2023] Open
Abstract
Human infertility is a major global public health issue estimated to affect one out of six couples, while the number of assisted reproduction cycles grows impressively year over year. Efforts to alleviate infertility using advanced technology are gaining traction rapidly as infertility has an enormous impact on couples and the potential to destabilize entire societies if replacement birthrates are not achieved. Artificial intelligence (AI) technologies, leveraged by the highly advanced assisted reproductive technology (ART) industry, are a promising addition to the armamentarium of tools available to combat global infertility. This review provides a background for current methodologies in embryo selection, which is a manual, time-consuming, and poorly reproducible task. AI has the potential to improve this process (among many others) in both the clinician's office and the IVF laboratory. Embryo selection is evolving through digital methodologies into an automated procedure, with superior reliability and reproducibility, that is likely to result in higher pregnancy rates for patients. There is an emerging body of data demonstrating the utility of AI applications in multiple areas in the IVF laboratory. AI platforms have been developed to evaluate individual embryologist performance; to provide quality assurance for culture systems; to correlate embryologist's assessments and AI systems; to predict embryo ploidy, implantation, fetal heartbeat, and live birth outcome; and to replace the current "analogue" system of embryo selection with a digital paradigm. AI capability will distinguish high performing, high profit margin, low-cost, and highly successful IVF clinic business models. We think it will become the standard, "new normal" in IVF labs, as rapidly and thoroughly as vitrification, blastocyst culture, and intracytoplasmic sperm injection replaced their predecessor technologies. At the time of this review, the AI technology to automate embryo evaluation and selection has robustly matured, and therefore, it is the main focus of this review.
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Affiliation(s)
| | - Alejandro Chavez-Badiola
- IVF 2.0 LTD, 1 Liverpool Road, Maghull, L31 2HB, Merseyside, UK
- New Hope Fertility Center, Av. Prado Norte 135, Lomas de Chapultepec, CP11000, Mexico City, Mexico
- Reproductive Genetics, School of Biosciences, University of Kent, Canterbury, CT2 7NZ, Kent, UK
| | - Carol Lynn Curchoe
- ART Compass, a Fertility Guidance Technology, Newport Beach, CA, 92660, USA
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Buccella A. "AI for all" is a matter of social justice. AI AND ETHICS 2022; 3:1-10. [PMID: 36189174 PMCID: PMC9510536 DOI: 10.1007/s43681-022-00222-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 09/09/2022] [Indexed: 11/29/2022]
Abstract
Artificial intelligence (AI) is a radically transformative technology (or system of technologies) that created new existential possibilities and new standards of well-being in human societies. In this article, I argue that to properly understand the increasingly important role AI plays in our society, we must consider its impacts on social justice. For this reason, I propose to conceptualize AI's transformative role and its socio-political implications through the lens of the theory of social justice known as the Capability Approach. According to the approach, a just society must put its members in a position to acquire and exercise a series of basic capabilities and provide them with the necessary means for these capabilities to be actively realized. Because AI is re-shaping the very definition of some of these basic capabilities, I conclude that AI itself should be considered among the conditions of possession and realization of the capabilities it transforms. In other words, access to AI-in the many forms this access can take-is necessary for social justice.
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Affiliation(s)
- Alessandra Buccella
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA USA
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17
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Current trends in artificial intelligence in reproductive endocrinology. Curr Opin Obstet Gynecol 2022; 34:159-163. [PMID: 35895955 DOI: 10.1097/gco.0000000000000796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
PURPOSE OF REVIEW Artificial Intelligence, a tool that integrates computer science and machine learning to mimic human decision-making processes, is transforming the world and changing the way we live. Recently, the healthcare industry has gradually adopted artificial intelligence in many applications and obtained some degree of success. In this review, we summarize the current applications of artificial intelligence in Reproductive Endocrinology, in both laboratory and clinical settings. RECENT FINDINGS Artificial Intelligence has been used to select the embryos with high implantation potential, proper ploidy status, to predict later embryo development, and to increase pregnancy and live birth rates. Some studies also suggested that artificial intelligence can help improve infertility diagnosis and patient management. Recently, it has been demonstrated that artificial intelligence also plays a role in effective laboratory quality control and performance. SUMMARY In this review, we discuss various applications of artificial intelligence in different areas of reproductive medicine. We summarize the current findings with their potentials and limitations, and also discuss the future direction for research and clinical applications.
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Ezoe K, Shimazaki K, Miki T, Takahashi T, Tanimura Y, Amagai A, Sawado A, Akaike H, Mogi M, Kaneko S, Okimura T, Kato K. Association of a deep learning-based scoring system with morphokinetics and morphological alterations in human embryos. Reprod Biomed Online 2022; 45:1124-1132. [DOI: 10.1016/j.rbmo.2022.08.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 07/19/2022] [Accepted: 08/08/2022] [Indexed: 11/25/2022]
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Ueno S, Berntsen J, Ito M, Okimura T, Kato K. Correlation between an annotation-free embryo scoring system based on deep learning and live birth/neonatal outcomes after single vitrified-warmed blastocyst transfer: a single-centre, large-cohort retrospective study. J Assist Reprod Genet 2022; 39:2089-2099. [PMID: 35881272 PMCID: PMC9475010 DOI: 10.1007/s10815-022-02562-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/04/2022] [Indexed: 11/30/2022] Open
Abstract
Propose Does an annotation-free embryo scoring system based on deep learning and time-lapse sequence images correlate with live birth (LB) and neonatal outcomes? Methods Patients who underwent SVBT cycles (3010 cycles, mean age: 39.3 ± 4.0). Scores were calculated using the iDAScore software module in the Vitrolife Technology Hub (Vitrolife, Gothenburg, Sweden). The correlation between iDAScore, LB rates, and total miscarriage (TM), including 1st- and 2nd-trimester miscarriage, was analysed using a trend test and multivariable logistic regression analysis. Furthermore, the correlation between the iDAScore and neonatal outcomes was analysed. Results LB rates decreased as iDAScore decreased (P < 0.05), and a similar inverse trend was observed for the TM rates. Additionally, multivariate logistic regression analysis showed that iDAScore significantly correlated with increased LB (adjusted odds ratio: 1.811, 95% CI: 1.666–1.976, P < 0.05) and decreased TM (adjusted odds ratio: 0.799, 95% CI: 0.706–0.905, P < 0.05). There was no significant correlation between iDAScore and neonatal outcomes, including congenital malformations, sex, gestational age, and birth weight. Multivariate logistic regression analysis, which included maternal and paternal age, maternal body mass index, parity, smoking, and presence or absence of caesarean section as confounding factors, revealed no significant difference in any neonatal characteristics. Conclusion Automatic embryo scoring using iDAScore correlates with decreased miscarriage and increased LB and has no correlation with neonatal outcomes. Supplementary information The online version contains supplementary material available at 10.1007/s10815-022-02562-5.
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Affiliation(s)
- Satoshi Ueno
- Kato Ladies Clinic, 7-20-3, Nishi-shinjuku, Shinjuku, Tokyo, 160-0023, Japan
| | | | - Motoki Ito
- Kato Ladies Clinic, 7-20-3, Nishi-shinjuku, Shinjuku, Tokyo, 160-0023, Japan
| | - Tadashi Okimura
- Kato Ladies Clinic, 7-20-3, Nishi-shinjuku, Shinjuku, Tokyo, 160-0023, Japan
| | - Keiichi Kato
- Kato Ladies Clinic, 7-20-3, Nishi-shinjuku, Shinjuku, Tokyo, 160-0023, Japan.
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20
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An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Despite the use of new techniques on embryo selection and the presence of equipment on the market, such as EmbryoScope® and Geri®, which help in the evaluation of embryo quality, there is still a subjectivity between the embryologist’s classifications, which are subjected to inter- and intra-observer variability, therefore compromising the successful implantation of the embryo. Nonetheless, with the acquisition of images through the time-lapse system, it is possible to perform digital processing of these images, providing a better analysis of the embryo, in addition to enabling the automatic analysis of a large volume of information. An image processing protocol was developed using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each one representing a different aspect of the embryo and describing a different characteristic of the blastocyst. These variables can be categorized into texture, gray-level average, gray-level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. The image processing protocol can segment human blastocyst images and automatically extract 33 variables that describe quantitative aspects of the blastocyst’s regions, with potential utility in embryo selection for assisted reproductive technology (ART).
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Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis. Biomedicines 2022; 10:biomedicines10030697. [PMID: 35327499 PMCID: PMC8945147 DOI: 10.3390/biomedicines10030697] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/08/2022] [Accepted: 03/13/2022] [Indexed: 12/04/2022] Open
Abstract
Artificial intelligence (AI) has been gaining support in the field of in vitro fertilization (IVF). Despite the promising existing data, AI cannot yet claim gold-standard status, which serves as the rationale for this study. This systematic review and data synthesis aims to evaluate and report on the predictive capabilities of AI-based prediction models regarding IVF outcome. The study has been registered in PROSPERO (CRD42021242097). Following a systematic search of the literature in Pubmed/Medline, Embase, and Cochrane Central Library, 18 studies were identified as eligible for inclusion. Regarding live-birth, the Area Under the Curve (AUC) of the Summary Receiver Operating Characteristics (SROC) was 0.905, while the partial AUC (pAUC) was 0.755. The Observed: Expected ratio was 1.12 (95%CI: 0.26–2.37; 95%PI: 0.02–6.54). Regarding clinical pregnancy with fetal heartbeat, the AUC of the SROC was 0.722, while the pAUC was 0.774. The O:E ratio was 0.77 (95%CI: 0.54–1.05; 95%PI: 0.21–1.62). According to this data synthesis, the majority of the AI-based prediction models are successful in accurately predicting the IVF outcome regarding live birth, clinical pregnancy, clinical pregnancy with fetal heartbeat, and ploidy status. This review attempted to compare between AI and human prediction capabilities, and although studies do not allow for a meta-analysis, this systematic review indicates that the AI-based prediction models perform rather similarly to the embryologists’ evaluations. While AI models appear marginally more effective, they still have some way to go before they can claim to significantly surpass the clinical embryologists’ predictive competence.
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Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos. Fertil Steril 2022; 117:528-535. [PMID: 34998577 DOI: 10.1016/j.fertnstert.2021.11.022] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 11/09/2021] [Accepted: 11/10/2021] [Indexed: 11/21/2022]
Abstract
OBJECTIVE To perform a series of analyses characterizing an artificial intelligence (AI) model for ranking blastocyst-stage embryos. The primary objective was to evaluate the benefit of the model for predicting clinical pregnancy, whereas the secondary objective was to identify limitations that may impact clinical use. DESIGN Retrospective study. SETTING Consortium of 11 assisted reproductive technology centers in the United States. PATIENT(S) Static images of 5,923 transferred blastocysts and 2,614 nontransferred aneuploid blastocysts. INTERVENTION(S) None. MAIN OUTCOME MEASURE(S) Prediction of clinical pregnancy (fetal heartbeat). RESULT(S) The area under the curve of the AI model ranged from 0.6 to 0.7 and outperformed manual morphology grading overall and on a per-site basis. A bootstrapped study predicted improved pregnancy rates between +5% and +12% per site using AI compared with manual grading using an inverted microscope. One site that used a low-magnification stereo zoom microscope did not show predicted improvement with the AI. Visualization techniques and attribution algorithms revealed that the features learned by the AI model largely overlap with the features of manual grading systems. Two sources of bias relating to the type of microscope and presence of embryo holding micropipettes were identified and mitigated. The analysis of AI scores in relation to pregnancy rates showed that score differences of ≥0.1 (10%) correspond with improved pregnancy rates, whereas score differences of <0.1 may not be clinically meaningful. CONCLUSION(S) This study demonstrates the potential of AI for ranking blastocyst stage embryos and highlights potential limitations related to image quality, bias, and granularity of scores.
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Dimitriadis I, Zaninovic N, Badiola AC, Bormann CL. Artificial intelligence in the embryology laboratory: a review. Reprod Biomed Online 2021; 44:435-448. [PMID: 35027326 DOI: 10.1016/j.rbmo.2021.11.003] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 10/07/2021] [Accepted: 11/04/2021] [Indexed: 02/03/2023]
Abstract
The goal of an IVF cycle is a healthy live-born baby. Despite the many advances in the field of assisted reproductive technologies, accurately predicting the outcome of an IVF cycle has yet to be achieved. One reason for this is the method of selecting an embryo for transfer. Morphological assessment of embryos is the traditional method of evaluating embryo quality and selecting which embryo to transfer. However, this subjective method of assessing embryos leads to inter- and intra-observer variability, resulting in less than optimal IVF success rates. To overcome this, it is common practice to transfer more than one embryo, potentially resulting in high-risk multiple pregnancies. Although time-lapse incubators and preimplantation genetic testing for aneuploidy have been introduced to help increase the chances of live birth, the outcomes remain less than ideal. Utilization of artificial intelligence (AI) has become increasingly popular in the medical field and is increasingly being leveraged in the embryology laboratory to help improve IVF outcomes. Many studies have been published investigating the use of AI as an unbiased, automated approach to embryo assessment. This review summarizes recent AI advancements in the embryology laboratory.
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Affiliation(s)
- Irene Dimitriadis
- Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston MA, USA
| | - Nikica Zaninovic
- The Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York NY, USA
| | - Alejandro Chavez Badiola
- New Hope Fertility Center, Av. Prado Norte 135, Lomas de Chapultepec, Mexico City, Mexico; IVF 2.0 LTD, 1 Liverpool Rd, Maghull, Merseyside, UK; School of Biosciences, University of Kent Kent, UK
| | - Charles L Bormann
- Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston MA, USA.
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Omitaomu OA, Klasky HB, Olama M, Ozmen O, Pullum L, Malviya Thakur A, Kuruganti T, Scott JM, Laurio A, Drews F, Sauer BC, Ward M, Nebeker JR. A new methodological framework for hazard detection models in health information technology systems. J Biomed Inform 2021; 124:103937. [PMID: 34687867 DOI: 10.1016/j.jbi.2021.103937] [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: 06/07/2021] [Revised: 10/14/2021] [Accepted: 10/15/2021] [Indexed: 11/29/2022]
Abstract
The adoption of health information technology (HIT) has facilitated efforts to increase the quality and efficiency of health care services and decrease health care overhead while simultaneously generating massive amounts of digital information stored in electronic health records (EHRs). However, due to patient safety issues resulting from the use of HIT systems, there is an emerging need to develop and implement hazard detection tools to identify and mitigate risks to patients. This paper presents a new methodological framework to develop hazard detection models and to demonstrate its capability by using the US Department of Veterans Affairs' (VA) Corporate Data Warehouse, the data repository for the VA's EHR. The overall purpose of the framework is to provide structure for research and communication about research results. One objective is to decrease the communication barriers between interdisciplinary research stakeholders and to provide structure for detecting hazards and risks to patient safety introduced by HIT systems through errors in the collection, transmission, use, and processing of data in the EHR, as well as potential programming or configuration errors in these HIT systems. A nine-stage framework was created, which comprises programs about feature extraction, detector development, and detector optimization, as well as a support environment for evaluating detector models. The framework forms the foundation for developing hazard detection tools and the foundation for adapting methods to particular HIT systems.
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Affiliation(s)
| | | | | | - Ozgur Ozmen
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Laura Pullum
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | | | | | | | | | - Frank Drews
- Department of Veterans Affairs, Washington, DC, USA
| | | | - Merry Ward
- Department of Veterans Affairs, Washington, DC, USA
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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.
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Kragh MF, Karstoft H. Embryo selection with artificial intelligence: how to evaluate and compare methods? J Assist Reprod Genet 2021; 38:1675-1689. [PMID: 34173914 PMCID: PMC8324599 DOI: 10.1007/s10815-021-02254-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 06/02/2021] [Indexed: 12/19/2022] Open
Abstract
Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent years, artificial intelligence (AI) has been used extensively to improve and automate the embryo ranking and selection procedure by extracting relevant information from embryo microscopy images. The AI models are evaluated based on their ability to identify the embryo(s) with the highest chance(s) of achieving a successful pregnancy. Whether such evaluations should be based on ranking performance or pregnancy prediction, however, seems to divide studies. As such, a variety of performance metrics are reported, and comparisons between studies are often made on different outcomes and data foundations. Moreover, superiority of AI methods over manual human evaluation is often claimed based on retrospective data, without any mentions of potential bias. In this paper, we provide a technical view on some of the major topics that divide how current AI models are trained, evaluated and compared. We explain and discuss the most common evaluation metrics and relate them to the two separate evaluation objectives, ranking and prediction. We also discuss when and how to compare AI models across studies and explain in detail how a selection bias is inevitable when comparing AI models against current embryo selection practice in retrospective cohort studies.
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Affiliation(s)
- Mikkel Fly Kragh
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark.
- Vitrolife A/S, Viby J, Denmark.
| | - Henrik Karstoft
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark
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Trolice MP, Curchoe C, Quaas AM. Artificial intelligence-the future is now. J Assist Reprod Genet 2021; 38:1607-1612. [PMID: 34231110 PMCID: PMC8260235 DOI: 10.1007/s10815-021-02272-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 06/29/2021] [Indexed: 11/25/2022] Open
Abstract
The pros and cons of artificial intelligence in assisted reproductive technology are presented.
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Affiliation(s)
- Mark P Trolice
- Obstetrics and Gynecology, University of Central Florida, Orlando, USA.
- The IVF Center, Orlando, FL, USA.
| | | | - Alexander M Quaas
- Division of Reproductive Endocrinology and Infertility, University of California, San Diego, CA, USA
- Reproductive Partners San Diego, San Diego, CA, USA
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