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Rotem O, Schwartz T, Maor R, Tauber Y, Shapiro MT, Meseguer M, Gilboa D, Seidman DS, Zaritsky A. Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization. Nat Commun 2024; 15:7390. [PMID: 39191720 DOI: 10.1038/s41467-024-51136-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 07/31/2024] [Indexed: 08/29/2024] Open
Abstract
The success of deep learning in identifying complex patterns exceeding human intuition comes at the cost of interpretability. Non-linear entanglement of image features makes deep learning a "black box" lacking human meaningful explanations for the models' decision. We present DISCOVER, a generative model designed to discover the underlying visual properties driving image-based classification models. DISCOVER learns disentangled latent representations, where each latent feature encodes a unique classification-driving visual property. This design enables "human-in-the-loop" interpretation by generating disentangled exaggerated counterfactual explanations. We apply DISCOVER to interpret classification of in vitro fertilization embryo morphology quality. We quantitatively and systematically confirm the interpretation of known embryo properties, discover properties without previous explicit measurements, and quantitatively determine and empirically verify the classification decision of specific embryo instances. We show that DISCOVER provides human-interpretable understanding of "black box" classification models, proposes hypotheses to decipher underlying biomedical mechanisms, and provides transparency for the classification of individual predictions.
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Affiliation(s)
- Oded Rotem
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel
| | | | - Ron Maor
- AIVF Ltd., Tel Aviv, 69271, Israel
| | | | | | - Marcos Meseguer
- IVI Foundation Instituto de Investigación Sanitaria La FeValencia, Valencia, 46026, Spain
- Department of Reproductive Medicine, IVIRMA Valencia, 46015, Valencia, Spain
| | | | - Daniel S Seidman
- AIVF Ltd., Tel Aviv, 69271, Israel
- The Faculty of Medicine, Tel Aviv University, Tel-Aviv, 69978, Israel
| | - Assaf Zaritsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.
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2
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Morawiec S, Ajduk A, Stremplewski P, Kennedy BF, Szkulmowski M. Full-field optical coherence microscopy enables high-resolution label-free imaging of the dynamics of live mouse oocytes and early embryos. Commun Biol 2024; 7:1057. [PMID: 39191989 DOI: 10.1038/s42003-024-06745-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 08/16/2024] [Indexed: 08/29/2024] Open
Abstract
High quality label-free imaging of oocytes and early embryos is essential for accurate assessment of their developmental potential, a key element of assisted reproduction procedures. To achieve this goal, we propose full-field optical coherence microscopy (FF-OCM), constructed as a compact module fully integrated with a commercial wide-field fluorescence microscope. Our system achieves optical sectioning in wide-field, high in-plane resolution of 0.5 µm, and high sensitivity to backscattered light. To demonstrate its imaging capabilities, we study live mouse oocytes and embryos at all important stages of meiotic maturation and early embryogenesis. Our system enables visualization of intracellular structures, which are not visible in common bright-field microscopy, i.e., internal structure of nuclear apparatus, cytoskeletal filaments, cellular cortex, cytoplasmic protrusions, or zona pellucida features. Additionally, we visualize and quantify intracellular dynamics like cytoplasmic stirring motion, nuclear envelope fluctuations and nucleolus mobility. Altogether, we demonstrate that FF-OCM is a powerful tool for research in developmental biology that also holds great potential for non-invasive time-lapse monitoring of oocyte and embryo quality in assisted reproduction.
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Affiliation(s)
- Seweryn Morawiec
- Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Torun, Torun, Poland.
| | - Anna Ajduk
- Department of Embryology, Institute of Developmental Biology and Biomedical Sciences, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Patrycjusz Stremplewski
- Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Torun, Torun, Poland
| | - Brendan F Kennedy
- Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Torun, Torun, Poland
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, and Centre for Medical Research, The University of Western Australia, Perth, WA, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA, Australia
| | - Maciej Szkulmowski
- Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Torun, Torun, Poland
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3
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Illingworth PJ, Venetis C, Gardner DK, Nelson SM, Berntsen J, Larman MG, Agresta F, Ahitan S, Ahlström A, Cattrall F, Cooke S, Demmers K, Gabrielsen A, Hindkjær J, Kelley RL, Knight C, Lee L, Lahoud R, Mangat M, Park H, Price A, Trew G, Troest B, Vincent A, Wennerström S, Zujovic L, Hardarson T. Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial. Nat Med 2024:10.1038/s41591-024-03166-5. [PMID: 39122964 DOI: 10.1038/s41591-024-03166-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/29/2024] [Indexed: 08/12/2024]
Abstract
To assess the value of deep learning in selecting the optimal embryo for in vitro fertilization, a multicenter, randomized, double-blind, noninferiority parallel-group trial was conducted across 14 in vitro fertilization clinics in Australia and Europe. Women under 42 years of age with at least two early-stage blastocysts on day 5 were randomized to either the control arm, using standard morphological assessment, or the study arm, employing a deep learning algorithm, intelligent Data Analysis Score (iDAScore), for embryo selection. The primary endpoint was a clinical pregnancy rate with a noninferiority margin of 5%. The trial included 1,066 patients (533 in the iDAScore group and 533 in the morphology group). The iDAScore group exhibited a clinical pregnancy rate of 46.5% (248 of 533 patients), compared to 48.2% (257 of 533 patients) in the morphology arm (risk difference -1.7%; 95% confidence interval -7.7, 4.3; P = 0.62). This study was not able to demonstrate noninferiority of deep learning for clinical pregnancy rate when compared to standard morphology and a predefined prioritization scheme. Australian New Zealand Clinical Trials Registry (ANZCTR) registration: 379161 .
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Affiliation(s)
| | - Christos Venetis
- IVFAustralia, Sydney, New South Wales, Australia
- Unit for Human Reproduction, 1st Dept of Ob/Gyn, Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Centre for Big Data Research in Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
| | - David K Gardner
- Melbourne IVF, Melbourne, Victoria, Australia
- School of BioSciences, University of Melbourne, Parkville, Victoria, Australia
| | - Scott M Nelson
- School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, UK
- TFP Fertility, Institute of Reproductive Sciences, Oxford, UK
| | | | | | | | | | - Aisling Ahlström
- IVIRMA Global Research Alliance, Livio Gothenburg, Gothenburg, Sweden
| | | | - Simon Cooke
- IVFAustralia, Sydney, New South Wales, Australia
| | - Kristy Demmers
- Queensland Fertility Group, Brisbane, Queensland, Australia
| | | | | | | | | | - Lisa Lee
- Melbourne IVF, Melbourne, Victoria, Australia
| | | | | | - Hannah Park
- Dept of Reproductive Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | | | - Geoffrey Trew
- TFP Fertility, Institute of Reproductive Sciences, Oxford, UK
- Imperial College London, London, UK
| | - Bettina Troest
- The Fertility Unit, Aalborg University Hospital, Aalborg, Denmark
| | - Anna Vincent
- TFP Fertility, Institute of Reproductive Sciences, Oxford, UK
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4
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Pavlovic ZJ, Jiang VS, Hariton E. Current applications of artificial intelligence in assisted reproductive technologies through the perspective of a patient's journey. Curr Opin Obstet Gynecol 2024; 36:211-217. [PMID: 38597425 DOI: 10.1097/gco.0000000000000951] [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: 04/11/2024]
Abstract
PURPOSE OF REVIEW This review highlights the timely relevance of artificial intelligence in enhancing assisted reproductive technologies (ARTs), particularly in-vitro fertilization (IVF). It underscores artificial intelligence's potential in revolutionizing patient outcomes and operational efficiency by addressing challenges in fertility diagnoses and procedures. RECENT FINDINGS Recent advancements in artificial intelligence, including machine learning and predictive modeling, are making significant strides in optimizing IVF processes such as medication dosing, scheduling, and embryological assessments. Innovations include artificial intelligence augmented diagnostic testing, predictive modeling for treatment outcomes, scheduling optimization, dosing and protocol selection, follicular and hormone monitoring, trigger timing, and improved embryo selection. These developments promise to refine treatment approaches, enhance patient engagement, and increase the accuracy and scalability of fertility treatments. SUMMARY The integration of artificial intelligence into reproductive medicine offers profound implications for clinical practice and research. By facilitating personalized treatment plans, standardizing procedures, and improving the efficiency of fertility clinics, artificial intelligence technologies pave the way for value-based, accessible, and efficient fertility services. Despite the promise, the full potential of artificial intelligence in ART will require ongoing validation and ethical considerations to ensure equitable and effective implementation.
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Affiliation(s)
- Zoran J Pavlovic
- Department of Obstetrics and Gynecology/Reproductive Endocrinology and Infertility, University of South Florida, Morsani College of Medicine, Tampa, Florida
| | - Victoria S Jiang
- Division of Reproductive Endocrinology & Infertility, Vincent Department of Obstetrics and Gynecology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Eduardo Hariton
- Reproductive Science Center of the San Francisco Bay Area, San Ramon, California, USA
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5
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Mapstone C, Hunter H, Brison D, Handl J, Plusa B. Deep learning pipeline reveals key moments in human embryonic development predictive of live birth after in vitro fertilization. Biol Methods Protoc 2024; 9:bpae052. [PMID: 39114746 PMCID: PMC11305813 DOI: 10.1093/biomethods/bpae052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 07/12/2024] [Accepted: 07/18/2024] [Indexed: 08/10/2024] Open
Abstract
Demand for in vitro fertilization (IVF) treatment is growing; however, success rates remain low partly due to difficulty in selecting the best embryo to be transferred. Current manual assessments are subjective and may not take advantage of the most informative moments in embryo development. Here, we apply convolutional neural networks (CNNs) to identify key windows in pre-implantation human development that can be linked to embryo viability and are therefore suitable for the early grading of IVF embryos. We show how machine learning models trained at these developmental time points can be used to refine overall embryo viability assessment. Exploiting the well-known capabilities of transfer learning, we illustrate the performance of CNN models for very limited datasets, paving the way for the use on a clinic-by-clinic basis, catering for local data heterogeneity.
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Affiliation(s)
- Camilla Mapstone
- Faculty of Biology, Medicine and Health (FBMH), Division of Developmental Biology & Medicine, University of Manchester, Manchester, M13 9PT, United Kingdom
- Alliance Manchester Business School, University of Manchester, Manchester, M15 6PB, United Kingdom
| | - Helen Hunter
- Department of Reproductive Medicine, Old Saint Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, M13 0JH, United Kingdom
| | - Daniel Brison
- Department of Reproductive Medicine, Old Saint Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, M13 0JH, United Kingdom
- Division of Developmental Biology and Medicine, Maternal and Fetal Health Research Centre, School of Medical Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9WL, United Kingdom
| | - Julia Handl
- Alliance Manchester Business School, University of Manchester, Manchester, M15 6PB, United Kingdom
| | - Berenika Plusa
- Faculty of Biology, Medicine and Health (FBMH), Division of Developmental Biology & Medicine, University of Manchester, Manchester, M13 9PT, United Kingdom
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Kim HM, Kang H, Lee C, Park JH, Chung MK, Kim M, Kim NY, Lee HJ. Evaluation of the Clinical Efficacy and Trust in AI-Assisted Embryo Ranking: Survey-Based Prospective Study. J Med Internet Res 2024; 26:e52637. [PMID: 38830209 PMCID: PMC11184268 DOI: 10.2196/52637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/23/2023] [Accepted: 05/06/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Current embryo assessment methods for in vitro fertilization depend on subjective morphological assessments. Recently, artificial intelligence (AI) has emerged as a promising tool for embryo assessment; however, its clinical efficacy and trustworthiness remain unproven. Simulation studies may provide additional evidence, provided that they are meticulously designed to mitigate bias and variance. OBJECTIVE The primary objective of this study was to evaluate the benefits of an AI model for predicting clinical pregnancy through well-designed simulations. The secondary objective was to identify the characteristics of and potential bias in the subgroups of embryologists with varying degrees of experience. METHODS This simulation study involved a questionnaire-based survey conducted on 61 embryologists with varying levels of experience from 12 in vitro fertilization clinics. The survey was conducted via Google Forms (Google Inc) in three phases: (1) phase 1, an initial assessment (December 23, 2022, to January 22, 2023); (2) phase 2, a validation assessment (March 6, 2023, to April 5, 2023); and (3) phase 3 an AI-guided assessment (March 6, 2023, to April 5, 2023). Inter- and intraobserver assessments and the accuracy of embryo selection from 360 day-5 embryos before and after AI guidance were analyzed for all embryologists and subgroups of senior and junior embryologists. RESULTS With AI guidance, the interobserver agreement increased from 0.355 to 0.527 and from 0.440 to 0.524 for junior and senior embryologists, respectively, thus reaching similar levels of agreement. In a test of accurate embryo selection with 90 questions, the numbers of correct responses by the embryologists only, embryologists with AI guidance, and AI only were 34 (38%), 45 (50%), and 59 (66%), respectively. Without AI, the average score (accuracy) of the junior group was 33.516 (37%), while that of the senior group was 35.967 (40%), with P<.001 in the t test. With AI guidance, the average score (accuracy) of the junior group increased to 46.581 (52%), reaching a level similar to that of the senior embryologists of 44.833 (50%), with P=.34. Junior embryologists had a higher level of trust in the AI score. CONCLUSIONS This study demonstrates the potential benefits of AI in selecting embryos with high chances of pregnancy, particularly for embryologists with 5 years or less of experience, possibly due to their trust in AI. Thus, using AI as an auxiliary tool in clinical practice has the potential to improve embryo assessment and increase the probability of a successful pregnancy.
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Affiliation(s)
| | - Hyoeun Kang
- AI Lab, Kai Health, Seoul, Republic of Korea
| | | | - Jong Hyuk Park
- IVF Clinic, Miraewaheemang Hospital, Seoul, Republic of Korea
| | - Mi Kyung Chung
- IVF Clinic, Seoul Rachel Fertility Center, Seoul, Republic of Korea
| | - Miran Kim
- Department of Obstetrics & Gynecology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Na Young Kim
- IVF Clinic, HI Fertility Center, Seoul, Republic of Korea
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7
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Fjeldstad J, Qi W, Mercuri N, Siddique N, Meriano J, Krivoi A, Nayot D. An artificial intelligence tool predicts blastocyst development from static images of fresh mature oocytes. Reprod Biomed Online 2024; 48:103842. [PMID: 38552566 DOI: 10.1016/j.rbmo.2024.103842] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/04/2024] [Accepted: 01/10/2024] [Indexed: 06/11/2024]
Abstract
RESEARCH QUESTION Can a deep learning image analysis model be developed to assess oocyte quality by predicting blastocyst development from images of denuded mature oocytes? DESIGN A deep learning model was developed utilizing 37,133 static oocyte images with associated laboratory outcomes from eight fertility clinics (six countries). A subset of data (n = 7807) was allocated to test model performance. External model validation was conducted to assess generalizability and robustness on new data (n = 12,357) from two fertility clinics (two countries). Performance was assessed by calculating area under the curve (AUC), balanced accuracy, specificity and sensitivity. Subgroup analyses were performed on the test dataset for age group, male factor and geographical location of the clinic. Model probabilities of the external dataset were converted to a 0-10 scoring scale to facilitate analysis of correlation with blastocyst development and quality. RESULTS The deep learning model demonstrated AUC of 0.64, balanced accuracy of 0.60, specificity of 0.55 and sensitivity of 0.65 on the test dataset. Subgroup analyses displayed the highest performance for age group 38-39 years (AUC 0.68), a negligible impact of male factor, and good model generalizability across geographical locations. Model performance was confirmed on external data: AUC of 0.63, balanced accuracy of 0.58, specificity of 0.57 and sensitivity of 0.59. Analysis of the scoring scale revealed that higher scoring oocytes correlated with higher likelihood of blastocyst development and good-quality blastocyst formation. CONCLUSION The deep learning model showed a favourable performance for the evaluation of oocytes in terms of competence to develop into a blastocyst, and when the predictions were converted into scores, they correlated with blastocyst quality. This represents a significant first step in oocyte evaluation for scientific and clinical applications.
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Affiliation(s)
| | - Weikai Qi
- Future Fertility, Toronto, Ontario, Canada
| | | | | | | | | | - Dan Nayot
- Future Fertility, Toronto, Ontario, Canada
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8
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Borna MR, Sepehri MM, Maleki B. An artificial intelligence algorithm to select most viable embryos considering current process in IVF labs. Front Artif Intell 2024; 7:1375474. [PMID: 38881952 PMCID: PMC11177761 DOI: 10.3389/frai.2024.1375474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 05/16/2024] [Indexed: 06/18/2024] Open
Abstract
Background The most common Assisted Reproductive Technology is In-Vitro Fertilization (IVF). During IVF, embryologists commonly perform a morphological assessment to evaluate embryo quality and choose the best embryo for transferring to the uterus. However, embryo selection through morphological assessment is subjective, so various embryologists obtain different conclusions. Furthermore, humans can consider only a limited number of visual parameters resulting in a poor IVF success rate. Artificial intelligence (AI) for embryo selection is objective and can include many parameters, leading to better IVF outcomes. Objectives This study sought to use AI to (1) predict pregnancy results based on embryo images, (2) assess using more than one image of the embryo in the prediction of pregnancy but based on the current process in IVF labs, and (3) compare results of AI-Based methods and embryologist experts in predicting pregnancy. Methods A data set including 252 Time-lapse Videos of embryos related to IVF performed between 2017 and 2020 was collected. Frames related to 19 ± 1, 43 ± 1, and 67 ± 1 h post-insemination were extracted. Well-Known CNN architectures with transfer learning have been applied to these images. The results have been compared with an algorithm that only uses the final image of embryos. Furthermore, the results have been compared with five experienced embryologists. Results To predict the pregnancy outcome, we applied five well-known CNN architectures (AlexNet, ResNet18, ResNet34, Inception V3, and DenseNet121). DeepEmbryo, using three images, predicts pregnancy better than the algorithm that only uses one final image. It also can predict pregnancy better than all embryologists. Different well-known architectures can successfully predict pregnancy chances with up to 75.0% accuracy using Transfer Learning. Conclusion We have developed DeepEmbryo, an AI-based tool that uses three static images to predict pregnancy. Additionally, DeepEmbryo uses images that can be obtained in the current IVF process in almost all IVF labs. AI-based tools have great potential for predicting pregnancy and can be used as a proper tool in the future.
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Affiliation(s)
- Mahdi-Reza Borna
- Department of IT Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Mehdi Sepehri
- Department of IT Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Behnam Maleki
- Infertility Center, Department of Obstetrics and Gynecology, Mazandaran University of Medical Sciences, Sari, Iran
- Research and Clinical Center for Infertility, Yazd Reproductive Sciences Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
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Liao Z, Yan C, Wang J, Zhang N, Yang H, Lin C, Zhang H, Wang W, Li W. A clinical consensus-compliant deep learning approach to quantitatively evaluate human in vitro fertilization early embryonic development with optical microscope images. Artif Intell Med 2024; 149:102773. [PMID: 38462274 DOI: 10.1016/j.artmed.2024.102773] [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: 08/22/2022] [Revised: 12/13/2023] [Accepted: 01/14/2024] [Indexed: 03/12/2024]
Abstract
The selection of embryos is a key for the success of in vitro fertilization (IVF). However, automatic quality assessment on human IVF embryos with optical microscope images is still challenging. In this study, we developed a clinical consensus-compliant deep learning approach, named Esava (Embryo Segmentation and Viability Assessment), to quantitatively evaluate the development of IVF embryos using optical microscope images. In total 551 optical microscope images of human IVF embryos of day-2 to day-3 were collected, preprocessed, and annotated. Using the Faster R-CNN model as baseline, our Esava model was constructed, refined, trained, and validated for precise and robust blastomere detection. A novel algorithm Crowd-NMS was proposed and employed in Esava to enhance the object detection and to precisely quantify the embryonic cells and their size uniformity. Additionally, an innovative GrabCut-based unsupervised module was integrated for the segmentation of blastomeres and embryos. Independently tested on 94 embryo images for blastomere detection, Esava obtained the high rates of 0.9940, 0.9121, and 0.9531 for precision, recall, and mAP respectively, and gained significant advances compared with previous computational methods. Intraclass correlation coefficients indicated the consistency between Esava and three experienced embryologists. Another test on 51 extra images demonstrated that Esava surpassed other tools significantly, achieving the highest average precision 0.9025. Moreover, it also accurately identified the borders of blastomeres with mIoU over 0.88 on the independent testing dataset. Esava is compliant with the Istanbul clinical consensus and compatible to senior embryologists. Taken together, Esava improves the accuracy and efficiency of embryonic development assessment with optical microscope images.
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Affiliation(s)
- Zaowen Liao
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chaoyu Yan
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jianbo Wang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Ningfeng Zhang
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huan Yang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Medical Big Data Center, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chenghao Lin
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Haiyue Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Wenjun Wang
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Weizhong Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China; Key Laboratory of Tropical Disease Control (Ministry of Education), Sun Yat-Sen University, Guangzhou, China.
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10
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Lacconi V, Massimiani M, Carriero I, Bianco C, Ticconi C, Pavone V, Alteri A, Muzii L, Rago R, Pisaturo V, Campagnolo L. When the Embryo Meets the Endometrium: Identifying the Features Required for Successful Embryo Implantation. Int J Mol Sci 2024; 25:2834. [PMID: 38474081 DOI: 10.3390/ijms25052834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/22/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
Evaluation of the optimal number of embryos, their quality, and the precise timing for transfer are critical determinants in reproductive success, although still remaining one of the main challenges in assisted reproduction technologies (ART). Indeed, the success of in vitro fertilization (IVF) treatments relies on a multitude of events and factors involving both the endometrium and the embryo. Despite concerted efforts on both fronts, the overall success rates of IVF techniques continue to range between 25% and 30%. The role of the endometrium in implantation has been recently recognized, leading to the hypothesis that both the "soil" and the "seed" play a central role in a successful pregnancy. In this respect, identification of the molecular signature of endometrial receptivity together with the selection of the best embryo for transfer become crucial in ART. Currently, efforts have been made to develop accurate, predictive, and personalized tests to identify the window of implantation and the best quality embryo. However, the value of these tests is still debated, as conflicting results are reported in the literature. The purpose of this review is to summarize and critically report the available criteria to optimize the success of embryo transfer and to better understand current limitations and potential areas for improvement.
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Affiliation(s)
- Valentina Lacconi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy
- Saint Camillus International University of Health Sciences, Via di Sant'Alessandro 8, 00131 Rome, Italy
| | - Micol Massimiani
- Saint Camillus International University of Health Sciences, Via di Sant'Alessandro 8, 00131 Rome, Italy
| | - Ilenia Carriero
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy
| | - Claudia Bianco
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy
| | - Carlo Ticconi
- Department of Surgical Sciences, Section of Gynaecology and Obstetrics, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy
| | - Valentina Pavone
- Reproductive Sciences Laboratory, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Alessandra Alteri
- Obstetrics and Gynaecology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Ludovico Muzii
- Department of Maternal and Child Health and Urological Sciences, "Sapienza" University of Rome, Policlinico Umberto I, 00161 Rome, Italy
| | - Rocco Rago
- Physiopathology of Reproduction and Andrology Unit, Sandro Pertini Hospital, Via dei Monti Tiburtini 385/389, 00157 Rome, Italy
| | - Valerio Pisaturo
- Department of Maternal and Child Health and Urological Sciences, "Sapienza" University of Rome, Policlinico Umberto I, 00161 Rome, Italy
| | - Luisa Campagnolo
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy
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11
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Fluks M, Collier R, Walewska A, Bruce AW, Ajduk A. How great thou ART: biomechanical properties of oocytes and embryos as indicators of quality in assisted reproductive technologies. Front Cell Dev Biol 2024; 12:1342905. [PMID: 38425501 PMCID: PMC10902081 DOI: 10.3389/fcell.2024.1342905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
Assisted Reproductive Technologies (ART) have revolutionized infertility treatment and animal breeding, but their success largely depends on selecting high-quality oocytes for fertilization and embryos for transfer. During preimplantation development, embryos undergo complex morphogenetic processes, such as compaction and cavitation, driven by cellular forces dependent on cytoskeletal dynamics and cell-cell interactions. These processes are pivotal in dictating an embryo's capacity to implant and progress to full-term development. Hence, a comprehensive grasp of the biomechanical attributes characterizing healthy oocytes and embryos is essential for selecting those with higher developmental potential. Various noninvasive techniques have emerged as valuable tools for assessing biomechanical properties without disturbing the oocyte or embryo physiological state, including morphokinetics, analysis of cytoplasmic movement velocity, or quantification of cortical tension and elasticity using microaspiration. By shedding light on the cytoskeletal processes involved in chromosome segregation, cytokinesis, cellular trafficking, and cell adhesion, underlying oogenesis, and embryonic development, this review explores the significance of embryo biomechanics in ART and its potential implications for improving clinical IVF outcomes, offering valuable insights and research directions to enhance oocyte and embryo selection procedures.
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Affiliation(s)
- Monika Fluks
- Department of Embryology, Institute of Developmental Biology and Biomedical Sciences, Faculty of Biology, University of Warsaw, Warsaw, Poland
- Department of Molecular Biology and Genetics, Faculty of Science, University of South Bohemia in České Budějovice, České Budějovice, Czechia
| | - Rebecca Collier
- Department of Molecular Biology and Genetics, Faculty of Science, University of South Bohemia in České Budějovice, České Budějovice, Czechia
| | - Agnieszka Walewska
- Department of Embryology, Institute of Developmental Biology and Biomedical Sciences, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Alexander W. Bruce
- Department of Molecular Biology and Genetics, Faculty of Science, University of South Bohemia in České Budějovice, České Budějovice, Czechia
| | - Anna Ajduk
- Department of Embryology, Institute of Developmental Biology and Biomedical Sciences, Faculty of Biology, University of Warsaw, Warsaw, Poland
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12
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Kim HM, Ko T, Kang H, Choi S, Park JH, Chung MK, Kim M, Kim NY, Lee HJ. Improved prediction of clinical pregnancy using artificial intelligence with enhanced inner cell mass and trophectoderm images. Sci Rep 2024; 14:3240. [PMID: 38331914 PMCID: PMC10853203 DOI: 10.1038/s41598-024-52241-x] [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: 07/26/2023] [Accepted: 01/16/2024] [Indexed: 02/10/2024] Open
Abstract
This study aimed to assess the performance of an artificial intelligence (AI) model for predicting clinical pregnancy using enhanced inner cell mass (ICM) and trophectoderm (TE) images. In this retrospective study, we included static images of 2555 day-5-blastocysts from seven in vitro fertilization centers in South Korea. The main outcome of the study was the predictive capability of the model to detect clinical pregnancies (gestational sac). Compared with the original embryo images, the use of enhanced ICM and TE images improved the average area under the receiver operating characteristic curve for the AI model from 0.716 to 0.741. Additionally, a gradient-weighted class activation mapping analysis demonstrated that the enhanced image-trained AI model was able to extract features from crucial areas of the embryo in 99% (506/512) of the cases. Particularly, it could extract the ICM and TE. In contrast, the AI model trained on the original images focused on the main areas in only 86% (438/512) of the cases. Our results highlight the potential efficacy of using ICM- and TE-enhanced embryo images when training AI models to predict clinical pregnancy.
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Affiliation(s)
| | - Taehoon Ko
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, South Korea
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea
- CMC Institute for Basic Medical Science, The Catholic Medical Center of The Catholic University of Korea, Seoul, South Korea
| | | | | | | | - Mi Kyung Chung
- Seoul Rachel Fertility Center, IVF Clinic, Seoul, South Korea
| | - Miran Kim
- Department of Obstetrics & Gynecology, Ajou University School of Medicine, Suwon, South Korea
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13
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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.
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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
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14
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Asada Y. Evolution of intracytoplasmic sperm injection: From initial challenges to wider applications. Reprod Med Biol 2024; 23:e12582. [PMID: 38803410 PMCID: PMC11129627 DOI: 10.1002/rmb2.12582] [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: 03/18/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
Background In vitro fertilization (IVF) has revolutionized infertility treatment. Nevertheless, male infertility requires more effective solutions. In 1992, the first-ever case of human birth via intracytoplasmic sperm injection (ICSI) was reported. ICSI involves microscopically injecting a sperm into an ovum. Successful ICSI has become a reliable therapy for couples facing infertility, a significant milestone. However, it has also introduced various challenges. This study also delves into ethical dilemmas arising from widespread ICSI use. Methods This review traces the history of ICSI, presenting pioneering attempts, first successful attempts, and critical reports on account of the initial skepticism toward the technology. The review also focuses on chronological progress until ICSI was recognized as effective and became widely applied. Main findings The review reveals that ICSI, although transformative, presents challenges. Successes include addressing male infertility and aiding fertilization. However, concerns arise regarding optimal sperm and embryo selection, genetic mutations, and long-term health implications. Ethical considerations surrounding ICSI's broad applications also surface. Conclusions Despite its success and effectiveness, ICSI is still evolving as a therapeutic method. By comprehensively evaluating the historical progress and the current status of ICSI and exploring its future prospects, this study highlights the importance of ICSI in infertility treatment.
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Cimadomo D, Forman EJ, Morbeck DE, Liperis G, Miller K, Zaninovic N, Sturmey R, Rienzi L. Day7 and low-quality blastocysts: opt in or opt out? A dilemma with important clinical implications. Fertil Steril 2023; 120:1151-1159. [PMID: 38008467 DOI: 10.1016/j.fertnstert.2023.10.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/28/2023]
Affiliation(s)
| | - Eric J Forman
- Columbia University Fertility Center, New York, New York
| | - Dean E Morbeck
- Morbeck Consulting Ltd., Auckland, New Zealand; Department of Obstetrics and Gynecology, Monash University, Melbourne, Australia
| | - Georgios Liperis
- Westmead Fertility Centre, Institute of Reproductive Medicine, University of Sydney, Westmead, New South Wales, Australia
| | | | - Nikica Zaninovic
- The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York
| | - Roger Sturmey
- Biomedical Institute for Multimorbidity, Hull York Medical School, University of Hull, Hull, United Kingdom
| | - Laura Rienzi
- IVIRMA Global Research Alliance, GENERA, Rome, Italy; Department of Biomolecular Sciences, University of Urbino "Carlo Bo", Urbino, Italy.
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16
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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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17
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Serdarogullari M, Raad G, Yarkiner Z, Bazzi M, Mourad Y, Alpturk S, Fakih F, Fakih C, Liperis G. Identifying predictors of Day 5 blastocyst utilization rate using an artificial neural network. Reprod Biomed Online 2023; 47:103399. [PMID: 37862857 DOI: 10.1016/j.rbmo.2023.103399] [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: 05/10/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 10/22/2023]
Abstract
RESEARCH QUESTION Can artificial intelligence identify predictors of an increased Day 5 blastocyst utilization rate (D5BUR), which is one of the most informative key performance indicators in an IVF laboratory? DESIGN This retrospective, multicentre study evaluated six variables for predicting D5BUR using an artificial neural network (ANN): number of metaphase II (MII) oocytes injected (intracytoplasmic sperm injection); use of autologous/donated gametes; maternal age at oocyte retrieval; sperm concentration; progressive sperm motility rate; and fertilization rate. Cycles were divided into training and testing sets through stratified random sampling. D5BUR on Day 5 was grouped into <60% and ≥60% as per the Vienna consensus benchmark values. RESULTS The area under the receiver operating characteristic curve (AUC) to predict the D5BUR groups was 80.2%. From the ANN model, all six independent variables were found to be of significant value for the prediction of D5BUR (P<0.0001), with the most important variable being the number of MII oocytes injected. Investigation of the effect of MII oocytes injected on D5BUR indicated an inverse correlation, with injection of an increasing number of MII oocytes resulting in a decreasing D5BUR (r=-0.344, P<0.001) and injection of up to six oocytes resulting in D5BUR ≥60%. CONCLUSION The number of MII oocytes injected is the most important predictor of D5BUR. Exploration of additional variables and further validation of models that can predict D5BUR can guide the way towards personalized treatment and increased safety.
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Affiliation(s)
| | - Georges Raad
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon; Faculty of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon
| | - Zalihe Yarkiner
- Cyprus International University, Faculty of Arts and Sciences, Department of Basic Sciences and Humanities, Northern Cyprus via Mersin 10, Turkey
| | - Marwa Bazzi
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | - Youmna Mourad
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | | | - Fadi Fakih
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | - Chadi Fakih
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | - George Liperis
- Westmead Fertility Centre, Institute of Reproductive Medicine, University of Sydney, Westmead, NSW, Australia.
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18
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Berman A, Anteby R, Efros O, Klang E, Soffer S. Deep learning for embryo evaluation using time-lapse: a systematic review of diagnostic test accuracy. Am J Obstet Gynecol 2023; 229:490-501. [PMID: 37116822 DOI: 10.1016/j.ajog.2023.04.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 03/28/2023] [Accepted: 04/19/2023] [Indexed: 04/30/2023]
Abstract
OBJECTIVE This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring. DATA SOURCES A systematic search was conducted in PubMed and Web of Science databases from January 2016 to December 2022. The search strategy was carried out by using key words and MeSH (Medical Subject Headings) terms. STUDY ELIGIBILITY CRITERIA Studies were included if they reported the accuracy of convolutional neural network models for embryo evaluation using time-lapse monitoring. The review was registered with PROSPERO (International Prospective Register of Systematic Reviews; identification number CRD42021275916). METHODS Two reviewer authors independently screened results using the Covidence systematic review software. The full-text articles were reviewed when studies met the inclusion criteria or in any uncertainty. Nonconsensus was resolved by a third reviewer. Risk of bias and applicability were evaluated using the QUADAS-2 tool and the modified Joanna Briggs Institute or JBI checklist. RESULTS Following a systematic search of the literature, 22 studies were identified as eligible for inclusion. All studies were retrospective. A total of 522,516 images of 222,998 embryos were analyzed. Three main outcomes were evaluated: successful in vitro fertilization, blastocyst stage classification, and blastocyst quality. Most studies reported >80% accuracy, and embryologists were outperformed in some. Ten studies had a high risk of bias, mostly because of patient bias. CONCLUSION The application of artificial intelligence in time-lapse monitoring has the potential to provide more efficient, accurate, and objective embryo evaluation. Models that examined blastocyst stage classification showed the best predictions. Models that predicted live birth had a low risk of bias, used the largest databases, and had external validation, which heightens their relevance to clinical application. Our systematic review is limited by the high heterogeneity among the included studies. Researchers should share databases and standardize reporting.
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Affiliation(s)
- Aya Berman
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
| | - Roi Anteby
- Department of Surgery and Transplantation B, Chaim Sheba Medical Center, Tel Hashomer, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Orly Efros
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; National Hemophilia Center and Institute of Thrombosis & Hemostasis, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Eyal Klang
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Institute for Health Care Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY; Deep Vision Lab, Chaim Sheba Medical Center, Ramat Gan, Israel; Division of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Shelly Soffer
- Deep Vision Lab, Chaim Sheba Medical Center, Ramat Gan, Israel; Internal Medicine B, Assuta Medical Center, Ashdod, Israel; Ben-Gurion University of the Negev, Be'er Sheva, Israel
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19
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Salih M, Austin C, Warty RR, Tiktin C, Rolnik DL, Momeni M, Rezatofighi H, Reddy S, Smith V, Vollenhoven B, Horta F. Embryo selection through artificial intelligence versus embryologists: a systematic review. Hum Reprod Open 2023; 2023:hoad031. [PMID: 37588797 PMCID: PMC10426717 DOI: 10.1093/hropen/hoad031] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 07/17/2023] [Indexed: 08/18/2023] Open
Abstract
STUDY QUESTION What is the present performance of artificial intelligence (AI) decision support during embryo selection compared to the standard embryo selection by embryologists? SUMMARY ANSWER AI consistently outperformed the clinical teams in all the studies focused on embryo morphology and clinical outcome prediction during embryo selection assessment. WHAT IS KNOWN ALREADY The ART success rate is ∼30%, with a worrying trend of increasing female age correlating with considerably worse results. As such, there have been ongoing efforts to address this low success rate through the development of new technologies. With the advent of AI, there is potential for machine learning to be applied in such a manner that areas limited by human subjectivity, such as embryo selection, can be enhanced through increased objectivity. Given the potential of AI to improve IVF success rates, it remains crucial to review the performance between AI and embryologists during embryo selection. STUDY DESIGN SIZE DURATION The search was done across PubMed, EMBASE, Ovid Medline, and IEEE Xplore from 1 June 2005 up to and including 7 January 2022. Included articles were also restricted to those written in English. Search terms utilized across all databases for the study were: ('Artificial intelligence' OR 'Machine Learning' OR 'Deep learning' OR 'Neural network') AND ('IVF' OR 'in vitro fertili*' OR 'assisted reproductive techn*' OR 'embryo'), where the character '*' refers the search engine to include any auto completion of the search term. PARTICIPANTS/MATERIALS SETTING METHODS A literature search was conducted for literature relating to AI applications to IVF. Primary outcomes of interest were accuracy, sensitivity, and specificity of the embryo morphology grade assessments and the likelihood of clinical outcomes, such as clinical pregnancy after IVF treatments. Risk of bias was assessed using the Modified Down and Black Checklist. MAIN RESULTS AND THE ROLE OF CHANCE Twenty articles were included in this review. There was no specific embryo assessment day across the studies-Day 1 until Day 5/6 of embryo development was investigated. The types of input for training AI algorithms were images and time-lapse (10/20), clinical information (6/20), and both images and clinical information (4/20). Each AI model demonstrated promise when compared to an embryologist's visual assessment. On average, the models predicted the likelihood of successful clinical pregnancy with greater accuracy than clinical embryologists, signifying greater reliability when compared to human prediction. The AI models performed at a median accuracy of 75.5% (range 59-94%) on predicting embryo morphology grade. The correct prediction (Ground Truth) was defined through the use of embryo images according to post embryologists' assessment following local respective guidelines. Using blind test datasets, the embryologists' accuracy prediction was 65.4% (range 47-75%) with the same ground truth provided by the original local respective assessment. Similarly, AI models had a median accuracy of 77.8% (range 68-90%) in predicting clinical pregnancy through the use of patient clinical treatment information compared to 64% (range 58-76%) when performed by embryologists. When both images/time-lapse and clinical information inputs were combined, the median accuracy by the AI models was higher at 81.5% (range 67-98%), while clinical embryologists had a median accuracy of 51% (range 43-59%). LIMITATIONS REASONS FOR CAUTION The findings of this review are based on studies that have not been prospectively evaluated in a clinical setting. Additionally, a fair comparison of all the studies were deemed unfeasible owing to the heterogeneity of the studies, development of the AI models, database employed and the study design and quality. WIDER IMPLICATIONS OF THE FINDINGS AI provides considerable promise to the IVF field and embryo selection. However, there needs to be a shift in developers' perception of the clinical outcome from successful implantation towards ongoing pregnancy or live birth. Additionally, existing models focus on locally generated databases and many lack external validation. STUDY FUNDING/COMPETING INTERESTS This study was funded by Monash Data Future Institute. All authors have no conflicts of interest to declare. REGISTRATION NUMBER CRD42021256333.
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Affiliation(s)
- M Salih
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - C Austin
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - R R Warty
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
| | - C Tiktin
- School of Engineering, RMIT University, Melbourne, Victoria, Australia
| | - D L Rolnik
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Women’s and Newborn Program, Monash Health, Melbourne, Victoria, Australia
| | - M Momeni
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
| | - H Rezatofighi
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
- Monash Data Future Institute, Monash University, Clayton, Victoria, Australia
| | - S Reddy
- School of Medicine, Deakin University, Geelong, Victoria, Australia
| | - V Smith
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
| | - B Vollenhoven
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Women’s and Newborn Program, Monash Health, Melbourne, Victoria, Australia
- Monash IVF, Melbourne, Victoria, Australia
| | - F Horta
- Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
- Monash Data Future Institute, Monash University, Clayton, Victoria, Australia
- City Fertility, Melbourne, Victoria, Australia
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20
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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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21
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Cherouveim P, Jiang VS, Kanakasabapathy MK, Thirumalaraju P, Souter I, Dimitriadis I, Bormann CL, Shafiee H. Quality assurance (QA) for monitoring the performance of assisted reproductive technology (ART) staff using artificial intelligence (AI). J Assist Reprod Genet 2023; 40:241-249. [PMID: 36374394 PMCID: PMC9935795 DOI: 10.1007/s10815-022-02649-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 10/20/2022] [Indexed: 11/16/2022] Open
Abstract
PURPOSE Deep learning neural networks have been used to predict the developmental fate and implantation potential of embryos with high accuracy. Such networks have been used as an assistive quality assurance (QA) tool to identify perturbations in the embryo culture environment which may impact clinical outcomes. The present study aimed to evaluate the utility of an AI-QA tool to consistently monitor ART staff performance (MD and embryologist) in embryo transfer (ET), embryo vitrification (EV), embryo warming (EW), and trophectoderm biopsy (TBx). METHODS Pregnancy outcomes from groups of 20 consecutive elective single day 5 blastocyst transfers were evaluated for the following procedures: MD performed ET (N = 160 transfers), embryologist performed ET (N = 160 transfers), embryologist performed EV (N = 160 vitrification procedures), embryologist performed EW (N = 160 warming procedures), and embryologist performed TBx (N = 120 biopsies). AI-generated implantation probabilities for the same embryo cohorts were estimated, as were mean AI-predicted and actual implantation rates for each provider and compared using Wilcoxon singed-rank test. RESULTS Actual implantation rates following ET performed by one MD provider: "H" was significantly lower than AI-predicted (20% vs. 61%, p = 0.001). Similar results were observed for one embryologist, "H" (30% vs. 60%, p = 0.011). Embryos thawed by embryologist "H" had lower implantation rates compared to AI prediction (25% vs. 60%, p = 0.004). There were no significant differences between actual and AI-predicted implantation rates for EV, TBx, or for the rest of the clinical staff performing ET or EW. CONCLUSIONS AI-based QA tools could provide accurate, reproducible, and efficient staff performance monitoring in an ART practice.
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Affiliation(s)
- Panagiotis Cherouveim
- Division of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA
| | - Victoria S Jiang
- Division of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA
| | - Manoj Kumar Kanakasabapathy
- Division of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Prudhvi Thirumalaraju
- Division of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Irene Souter
- Division of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA
| | - Irene Dimitriadis
- Division of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA
| | - Charles L Bormann
- Division of Reproductive Endocrinology and Infertility, Vincent Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA, 02114, USA
| | - Hadi Shafiee
- Division of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA.
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22
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Curchoe CL, Bormann C, Hammond E, Salter S, Timlin C, Williams LB, Gilboa D, Seidman D, Campbell A, Morbeck D. Assuring quality in assisted reproduction laboratories: assessing the performance of ART Compass - a digital art staff management platform. J Assist Reprod Genet 2023; 40:265-278. [PMID: 36637586 PMCID: PMC9935773 DOI: 10.1007/s10815-023-02713-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 12/30/2022] [Indexed: 01/14/2023] Open
Abstract
PURPOSE Staff management is the most cited ART/IVF laboratory inspection deficiency. Small ART/IVF clinics may be challenged to perform these activities by low staff volume; similarly, large ART/IVF networks may be challenged by high staff volume and large datasets. Here, we sought to investigate the performance of an automated, digital platform solution to manage this necessary task. METHODS The ART Compass (ARTC) digital staff management platform was used to assess the clinical decision-making of ART laboratory staff. The survey modules presented standardized instructions to technologists and measured inter- and intra-technologist variability for subjective "clinical decision-making" type questions. Internal and external comparisons were achieved by providing technologists two answers: (1) a comparison to their own lab director and (2) to the most popular response collectively provided by all lab director level accounts. The platform is hosted on HIPAA compliant Amazon web servers, accessible via web browser and mobile applications for iOS (Apple) and Android mobile devices. RESULTS Here, we investigated the performance of a digital staff management platform for single embryologist IVF practices and for three IVF lab networks (sites A, B, C) from 2020 to 2022. Embryology dish preparation survey results show variance among respondents in the following: PPE use, media volume, timing of oil overlay, and timing of moving prepared dishes to incubators. Surveying the perceived Gardner score and terms in use for early blastocysts reveals a lack of standardization of terminology and fair to poor agreement. We observed moderate inter-technologist agreement for ICM and TE grade (0.47 and 0.52, respectively). Lastly, the clinical decision of choice to freeze or discard an embryo revealed that agreement to freeze was highest for the top-quality embryos, and that some embryos can be highly contested, evenly split between choice to freeze or discard. CONCLUSIONS We conclude that a digital platform is a novel and effective tool to automate, routinely monitor, and assure quality for staff-related parameters in ART and IVF laboratories. Use of a digital platform can increase regulatory compliance and provide actionable insight for quality assurance in both single embryologist practices and for large networks. Furthermore, clinical decision-making can be augmented with artificial intelligence integration.
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Affiliation(s)
| | - Charles Bormann
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics & Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | - Claire Timlin
- Fertility Guidance Technologies, Newport Beach, CA, USA
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23
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Jiang VS, Kandula H, Thirumalaraju P, Kanakasabapathy MK, Cherouveim P, Souter I, Dimitriadis I, Bormann CL, Shafiee H. The use of voting ensembles to improve the accuracy of deep neural networks as a non-invasive method to predict embryo ploidy status. J Assist Reprod Genet 2023; 40:301-308. [PMID: 36640251 PMCID: PMC9935776 DOI: 10.1007/s10815-022-02707-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/23/2022] [Indexed: 01/15/2023] Open
Abstract
PURPOSE To determine if creating voting ensembles combining convolutional neural networks (CNN), support vector machine (SVM), and multi-layer neural networks (NN) alongside clinical parameters improves the accuracy of artificial intelligence (AI) as a non-invasive method for predicting aneuploidy. METHODS A cohort of 699 day 5 PGT-A tested blastocysts was used to train, validate, and test a CNN to classify embryos as euploid/aneuploid. All embryos were analyzed using a modified FAST-SeqS next-generation sequencing method. Patient characteristics such as maternal age, AMH level, paternal sperm quality, and total number of normally fertilized (2PN) embryos were processed using SVM and NN. To improve model performance, we created voting ensembles using CNN, SVM, and NN to combine our imaging data with clinical parameter variations. Statistical significance was evaluated with a one-sample t-test with 2 degrees of freedom. RESULTS When assessing blastocyst images alone, the CNN test accuracy was 61.2% (± 1.32% SEM, n = 3 models) in correctly classifying euploid/aneuploid embryos (n = 140 embryos). When the best CNN model was assessed as a voting ensemble, the test accuracy improved to 65.0% (AMH; p = 0.1), 66.4% (maternal age; p = 0.06), 65.7% (maternal age, AMH; p = 0.08), 66.4% (maternal age, AMH, number of 2PNs; p = 0.06), and 71.4% (maternal age, AMH, number of 2PNs, sperm quality; p = 0.02) (n = 140 embryos). CONCLUSIONS By combining CNNs with patient characteristics, voting ensembles can be created to improve the accuracy of classifying embryos as euploid/aneuploid from CNN alone, allowing for AI to serve as a potential non-invasive method to aid in karyotype screening and selection of embryos.
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Affiliation(s)
- Victoria S Jiang
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, VincentBoston, MA, 02114, USA
| | - Hemanth Kandula
- Division of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Prudhvi Thirumalaraju
- Division of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Manoj Kumar Kanakasabapathy
- Division of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Panagiotis Cherouveim
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, VincentBoston, MA, 02114, USA
| | - Irene Souter
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, VincentBoston, MA, 02114, USA
| | - Irene Dimitriadis
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, VincentBoston, MA, 02114, USA
| | - Charles L Bormann
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, VincentBoston, MA, 02114, USA.
| | - Hadi Shafiee
- Division of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA, 02139, USA.
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24
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Automated identification of blastocyst regions at different development stages. Sci Rep 2023; 13:15. [PMID: 36593239 PMCID: PMC9807603 DOI: 10.1038/s41598-022-26386-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 12/14/2022] [Indexed: 01/04/2023] Open
Abstract
The selection of the best single blastocyst for transfer is typically based on the assessment of the morphological characteristics of the zona pellucida (ZP), trophectoderm (TE), blastocoel (BC), and inner cell-mass (ICM), using subjective and observer-dependent grading protocols. We propose the first automatic method for segmenting all morphological structures during the different developmental stages of the blastocyst (i.e., expansion, hatching, and hatched). Our database contains 592 original raw images that were augmented to 2132 for training and 55 for validation. The mean Dice similarity coefficient (DSC) was 0.87 for all pixels, and for the BC, BG (background), ICM, TE, and ZP was 0.85, 0.96, 0.54, 0.63, and 0.71, respectively. Additionally, we tested our method against a public repository of 249 images resulting in accuracies of 0.96 and 0.93 and DSC of 0.67 and 0.67 for ICM and TE, respectively. A sensitivity analysis demonstrated that our method is robust, especially for the BC, BG, TE, and ZP. It is concluded that our approach can automatically segment blastocysts from different laboratory settings and developmental phases of the blastocysts, all within a single pipeline. This approach could increase the knowledge base for embryo selection.
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25
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Hammer KC, Jiang VS, Kanakasabapathy MK, Thirumalaraju P, Kandula H, Dimitriadis I, Souter I, Bormann CL, Shafiee H. Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study. J Assist Reprod Genet 2022; 39:2343-2348. [PMID: 35962845 PMCID: PMC9596636 DOI: 10.1007/s10815-022-02585-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 07/19/2022] [Indexed: 10/15/2022] Open
Abstract
PURPOSE To determine whether convolutional neural networks (CNN) can be used to accurately ascertain the patient identity (ID) of cleavage and blastocyst stage embryos based on image data alone. METHODS A CNN model was trained and validated over three replicates on a retrospective cohort of 4889 time-lapse embryo images. The algorithm processed embryo images for each patient and produced a unique identification key that was associated with the patient ID at a timepoint on day 3 (~ 65 hours post-insemination (hpi)) and day 5 (~ 105 hpi) forming our data library. When the algorithm evaluated embryos at a later timepoint on day 3 (~ 70 hpi) and day 5 (~ 110 hpi), it generates another key that was matched with the patient's unique key available in the library. This approach was tested using 400 patient embryo cohorts on day 3 and day 5 and number of correct embryo identifications with the CNN algorithm was measured. RESULTS CNN technology matched the patient identification within random pools of 8 patient embryo cohorts on day 3 with 100% accuracy (n = 400 patients; 3 replicates). For day 5 embryo cohorts, the accuracy within random pools of 8 patients was 100% (n = 400 patients; 3 replicates). CONCLUSIONS This study describes an artificial intelligence-based approach for embryo identification. This technology offers a robust witnessing step based on unique morphological features of each embryo. This technology can be integrated with existing imaging systems and laboratory protocols to improve specimen tracking.
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Affiliation(s)
- Karissa C. Hammer
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA 02114 USA
| | - Victoria S. Jiang
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA 02114 USA
| | - Manoj Kumar Kanakasabapathy
- Division of Engineering in Medicine, Brigham and Women’s Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA 02139 USA
| | - Prudhvi Thirumalaraju
- Division of Engineering in Medicine, Brigham and Women’s Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA 02139 USA
| | - Hemanth Kandula
- Division of Engineering in Medicine, Brigham and Women’s Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA 02139 USA
| | - Irene Dimitriadis
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA 02114 USA
| | - Irene Souter
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA 02114 USA
| | - Charles L. Bormann
- Division of Reproductive Endocrinology and Infertility, Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA 02114 USA
| | - Hadi Shafiee
- Division of Engineering in Medicine, Brigham and Women’s Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA 02139 USA
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26
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Handayani N, Louis CM, Erwin A, Aprilliana T, Polim AA, Sirait B, Boediono A, Sini I. Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program. FERTILITY & REPRODUCTION 2022. [DOI: 10.1142/s2661318222500098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Objective: Hidden knowledge could be discovered within a large practical data of in vitro fertilization (IVF) practice. In this study, Machine learning-based data mining techniques were utilized to construct a reliable prediction model for clinical pregnancy in IVF. Study Design: A retrospective cohort multicenter study involving 4.570 IVF cycles. All patients underwent fresh embryo transfer at either the cleavage or blastocyst stage between January 2015 and December 2019. The experiment focused on utilizing tree-based classifiers to generate and compare the most effective prediction model that could predict a clinical pregnancy through clinical data. Additionally, each classifier is optimized via a genetic algorithm technique, along with the selection of variables. Results: Both the decision tree and random forest showed similar performance that was much better than the gradient boost. The two superior classifiers achieved a balanced accuracy of roughly 0.62. Additionally, each prediction model was shown to work optimally with different combinations of variables, with some variables being consistently included, such as female age, and some consistently excluded, which provides an insight into the relationship between the variables and each prediction model. Conclusion: Machine learning algorithm remains effective for the purpose of data mining and knowledge extraction in IVF clinical datasets through which a relatively reliable prediction system for clinical pregnancy could be constructed, provided the available data is sufficient.
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Affiliation(s)
- Nining Handayani
- Morula IVF Jakarta Clinic, Jakarta, Indonesia
- IRSI Research and Training Centre, Jakarta, Indonesia
| | | | - Alva Erwin
- IRSI Research and Training Centre, Jakarta, Indonesia
- Faculty of Engineering and Information Technology, Swiss German University, Tangerang, Indonesia
| | | | - Arie A Polim
- Morula IVF Jakarta Clinic, Jakarta, Indonesia
- IRSI Research and Training Centre, Jakarta, Indonesia
- Department of Obstetrics and Gynecology, School of Medicine and Health Sciences, Atmajaya Catholic University of Indonesia, Jakarta, Indonesia
| | - Batara Sirait
- Morula IVF Jakarta Clinic, Jakarta, Indonesia
- Department of Obstetrics and Gynaecology, Faculty of Medicine Universitas Kristen Indonesia, Jakarta, Indonesia
| | - Arief Boediono
- Morula IVF Jakarta Clinic, Jakarta, Indonesia
- IRSI Research and Training Centre, Jakarta, Indonesia
- Department of Anatomy, Physiology and Pharmacology, IPB University, Bogor, Indonesia
| | - Ivan Sini
- Morula IVF Jakarta Clinic, Jakarta, Indonesia
- IRSI Research and Training Centre, Jakarta, Indonesia
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27
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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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28
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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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29
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Rabbi F, Dabbagh SR, Angin P, Yetisen AK, Tasoglu S. Deep Learning-Enabled Technologies for Bioimage Analysis. MICROMACHINES 2022; 13:mi13020260. [PMID: 35208385 PMCID: PMC8880650 DOI: 10.3390/mi13020260] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 01/31/2022] [Accepted: 02/03/2022] [Indexed: 02/05/2023]
Abstract
Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.
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Affiliation(s)
- Fazle Rabbi
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
| | - Sajjad Rahmani Dabbagh
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
- Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer, Istanbul 34450, Turkey
- Koc University Is Bank Artificial Intelligence Lab (KUIS AILab), Koç University, Sariyer, Istanbul 34450, Turkey
| | - Pelin Angin
- Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey;
| | - Ali Kemal Yetisen
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK;
| | - Savas Tasoglu
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
- Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer, Istanbul 34450, Turkey
- Koc University Is Bank Artificial Intelligence Lab (KUIS AILab), Koç University, Sariyer, Istanbul 34450, Turkey
- Institute of Biomedical Engineering, Boğaziçi University, Çengelköy, Istanbul 34684, Turkey
- Physical Intelligence Department, Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany
- Correspondence:
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30
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Berntsen J, Rimestad J, Lassen JT, Tran D, Kragh MF. Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences. PLoS One 2022; 17:e0262661. [PMID: 35108306 PMCID: PMC8809568 DOI: 10.1371/journal.pone.0262661] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 01/03/2022] [Indexed: 01/31/2023] Open
Abstract
Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep learning. Based on images of embryos with known implantation data (KID), AI models have been trained to automatically score embryos related to their chance of achieving a successful implantation. However, as of now, only limited research has been conducted to evaluate how embryo selection models generalize to new clinics and how they perform in subgroup analyses across various conditions. In this paper, we investigate how a deep learning-based embryo selection model using only time-lapse image sequences performs across different patient ages and clinical conditions, and how it correlates with traditional morphokinetic parameters. The model was trained and evaluated based on a large dataset from 18 IVF centers consisting of 115,832 embryos, of which 14,644 embryos were transferred KID embryos. In an independent test set, the AI model sorted KID embryos with an area under the curve (AUC) of a receiver operating characteristic curve of 0.67 and all embryos with an AUC of 0.95. A clinic hold-out test showed that the model generalized to new clinics with an AUC range of 0.60-0.75 for KID embryos. Across different subgroups of age, insemination method, incubation time, and transfer protocol, the AUC ranged between 0.63 and 0.69. Furthermore, model predictions correlated positively with blastocyst grading and negatively with direct cleavages. The fully automated iDAScore v1.0 model was shown to perform at least as good as a state-of-the-art manual embryo selection model. Moreover, full automatization of embryo scoring implies fewer manual evaluations and eliminates biases due to inter- and intraobserver variation.
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Affiliation(s)
| | | | | | - Dang Tran
- Harrison AI, Sydney, New South Wales, Australia
| | - Mikkel Fly Kragh
- Vitrolife A/S, Aarhus, Denmark
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
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31
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Nagaya M, Ukita N. Embryo Grading With Unreliable Labels Due to Chromosome Abnormalities by Regularized PU Learning With Ranking. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:320-331. [PMID: 34748484 DOI: 10.1109/tmi.2021.3126169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We propose a method for human embryo grading with its images. This grading has been achieved by positive-negative classification (i.e., live birth or non-live birth). However, negative (non-live birth) labels collected in clinical practice are unreliable because the visual features of negative images are equal to those of positive (live birth) images if these non-live birth embryos have chromosome abnormalities. For alleviating an adverse effect of these unreliable labels, our method employs Positive-Unlabeled (PU) learning so that live birth and non-live birth are labeled as positive and unlabeled, respectively, where unlabeled samples contain both positive and negative samples. In our method, this PU learning on a deep CNN is improved by a learning-to-rank scheme. While the original learning-to-rank scheme is designed for positive-negative learning, it is extended to PU learning. Furthermore, overfitting in this PU learning is alleviated by regularization with mutual information. Experimental results with 643 time-lapse image sequences demonstrate the effectiveness of our framework in terms of the recognition accuracy and the interpretability. In quantitative comparison, the full version of our proposed method outperforms positive-negative classification in recall and F-measure by a wide margin (0.22 vs. 0.69 in recall and 0.27 vs. 0.42 in F-measure). In qualitative evaluation, visual attentions estimated by our method are interpretable in comparison with morphological assessments in clinical practice.
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32
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Huang B, Zheng S, Ma B, Yang Y, Zhang S, Jin L. Using deep learning to predict the outcome of live birth from more than 10,000 embryo data. BMC Pregnancy Childbirth 2022; 22:36. [PMID: 35034623 PMCID: PMC8761300 DOI: 10.1186/s12884-021-04373-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 12/29/2021] [Indexed: 12/04/2022] Open
Abstract
Background Recently, the combination of deep learning and time-lapse imaging provides an objective, standard and scientific solution for embryo selection. However, the reported studies were based on blastocyst formation or clinical pregnancy as the end point. To the best of our knowledge, there is no predictive model that uses the outcome of live birth as the predictive end point. Can a deep learning model predict the probability of live birth from time-lapse system? Methods This study retrospectively analyzed the time-lapse data and live birth outcomes of embryos samples from January 2018 to November 2019. We used the SGD optimizer with an initial learning rate of 0.025 and cosine learning rate reduction strategy. The network is randomly initialized and trained for 200 epochs from scratch. The model is quantitively evaluated over a hold-out test and a 5-fold cross-validation by the average area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results The deep learning model was able to predict live birth outcomes from time-lapse images with an AUC of 0.968 in 5-fold stratified cross-validation. Conclusions This research reported a deep learning model that predicts the live birth outcome of a single blastocyst transfer. This efficient model for predicting the outcome of live births can automatically analyze the time-lapse images of the patient’s embryos without the need for manual embryo annotation and evaluation, and then give a live birth prediction score for each embryo, and sort the embryos by the predicted value. Supplementary Information The online version contains supplementary material available at 10.1186/s12884-021-04373-5.
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Affiliation(s)
- Bo Huang
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Shunyuan Zheng
- School of Computer Science and Technology, Harbin Institute of Technology, Weihai, 264209, China
| | - Bingxin Ma
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Yongle Yang
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Shengping Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Weihai, 264209, China.
| | - Lei Jin
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
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Fadon P, Gallegos E, Jalota S, Muriel L, Diaz-Garcia C. Time-Lapse Systems: A Comprehensive Analysis on Effectiveness. Semin Reprod Med 2022; 39:e12-e18. [PMID: 35008119 DOI: 10.1055/s-0041-1742149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Time-lapse systems have quickly become a common feature of in vitro fertilization laboratories all over the world. Since being introduced over a decade ago, the alleged benefits of time-lapse technology have continued to grow, from undisturbed culture conditions and round the clock, noninvasive observations to more recent computer-assisted selection of embryos through the development of algorithms. Despite the global uptake of time-lapse technology, its real impact on clinical outcomes is still controversial. This review aims to explore the different features offered by time-lapse technology, discussing incubation, algorithms, artificial intelligence and the regulation of nonessential treatment interventions, while assessing evidence on whether any benefit is offered over conventional technology.
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Affiliation(s)
| | | | | | | | - Cesar Diaz-Garcia
- IVI London, IVIRMA Global, London, United Kingdom.,EGA Institute for Women's Health, University College London, London, United Kingdom
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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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Nguyen DP, Pham QT, Tran TL, Vuong LN, Ho TM. Blastocyst Prediction of Day-3 Cleavage-Stage Embryos Using Machine Learning. FERTILITY & REPRODUCTION 2021. [DOI: 10.1142/s266131822150016x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Background: Embryo selection plays an important role in the success of in vitro fertilization (IVF). However, morphological embryo assessment has a number of limitations, including the time required, lack of accuracy, and inconsistency. This study determined whether a machine learning-based model could predict blastocyst formation using day-3 embryo images. Methods: Day-3 embryo images from IVF/intracytoplasmic sperm injection (ICSI) cycles performed at My Duc Phu Nhuan Hospital between August 2018 and June 2019 were retrospectively analyzed to inform model development. Day-3 embryo images derived from two-pronuclear (2PN) zygotes with known blastocyst formation data were extracted from the CCM-iBIS time-lapse incubator (Astec, Japan) at 67 hours post ICSI, and labeled as blastocyst/non-blastocyst based on results at 116 hours post ICSI. Images were used as the input dataset to train (85%) and validate (15%) the convolutional neural network (CNN) model, then model accuracy was determined using the training and validation dataset. The performance of 13 experienced embryologists for predicting blastocyst formation based on 100 day-3 embryo images was also evaluated. Results: A total of 1,135 images were allocated into training ([Formula: see text] 967) and validation ([Formula: see text] 168) sets, with an even distribution for blastocyst formation outcome. The accuracy of the final model for blastocyst formation was 97.72% in the training dataset and 76.19% in the validation dataset. The final model predicted blastocyst formation from day-3 embryo images in the validation dataset with an area under the curve of 0.75 (95% confidence interval [CI] 0.69–0.81). Embryologists predicted blastocyst formation with the accuracy of 70.07% (95% CI 68.12%–72.03%), sensitivity of 87.04% (95% CI 82.56%–91.52%), and specificity of 30.93% (95% CI 29.35%–32.51%). Conclusions: The CNN-based machine learning model using day-3 embryo images predicted blastocyst formation more accurately than experienced embryologists. The CNN-based model is a potential tool to predict additional IVF outcomes.
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Affiliation(s)
- Dung P. Nguyen
- IVFMD PN, My Duc Phu Nhuan Hospital, Ho Chi Minh City, Vietnam
- HOPE Research Center, My Duc Hospital, Ho Chi Minh City, Vietnam
| | - Quan T. Pham
- HOPE Research Center, My Duc Hospital, Ho Chi Minh City, Vietnam
| | - Thanh L. Tran
- IVFMD PN, My Duc Phu Nhuan Hospital, Ho Chi Minh City, Vietnam
- HOPE Research Center, My Duc Hospital, Ho Chi Minh City, Vietnam
| | - Lan N. Vuong
- HOPE Research Center, My Duc Hospital, Ho Chi Minh City, Vietnam
- IVFMD, My Duc Hospital, Ho Chi Minh City, Vietnam
- Department of Obstetrics and Gynecology, University of Medicine and Pharmacy at Ho Chi Minh City, Vietnam
| | - Tuong M. Ho
- HOPE Research Center, My Duc Hospital, Ho Chi Minh City, Vietnam
- IVFMD, My Duc Hospital, Ho Chi Minh City, Vietnam
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Should there be an "AI" in TEAM? Embryologists selection of high implantation potential embryos improves with the aid of an artificial intelligence algorithm. J Assist Reprod Genet 2021; 38:2663-2670. [PMID: 34535847 DOI: 10.1007/s10815-021-02318-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 09/09/2021] [Indexed: 10/20/2022] Open
Abstract
PURPOSE A deep learning artificial intelligence (AI) algorithm has been demonstrated to outperform embryologists in identifying euploid embryos destined to implant with an accuracy of 75.3% (1). Our aim was to evaluate the performance of highly trained embryologists in selecting top quality day 5 euploid blastocysts with and without the aid of a deep learning algorithm. MATERIALS AND METHODS A non-overlapping series of 200 sets of day 5 euploid embryo images with known implantation outcomes was distributed to 17 highly trained embryologists. One embryo in each set was known to have implanted and one failed implantation. They were asked to select which embryo to transfer from each set. The same 200 sets of embryos, with indication of which embryo in each set had been identified by the algorithm as more likely to implant was then distributed. Chi-squared, t-test, and receiver operating curves were performed to compare the embryologist performeance with and without AI. RESULTS Fourteen embryologists completed both assessments. Embryologists provided with AI results selected successfully implanted embryos in 73.6% of cases compared to 65.5% for those selected using visual assessments alone (p < 0.001). All embryologists improved in their ability to select embryos with the aid of the AI algorithm with a mean percent improvement of 11.1% (range 1.4% to 15.5%). There were no differences in degree of improvement by embryologist level of experience (junior, intermediate, senior). CONCLUSIONS The incorporation of an AI framework for blastocyst selection enhanced the performance of trained embryologists in identifying PGT-A euploid embryos destined to implant.
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Riegler MA, Stensen MH, Witczak O, Andersen JM, Hicks SA, Hammer HL, Delbarre E, Halvorsen P, Yazidi A, Holst N, Haugen TB. Artificial intelligence in the fertility clinic: status, pitfalls and possibilities. Hum Reprod 2021; 36:2429-2442. [PMID: 34324672 DOI: 10.1093/humrep/deab168] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/21/2021] [Indexed: 12/15/2022] Open
Abstract
In recent years, the amount of data produced in the field of ART has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, artificial intelligence (AI) is progressively used in medical practice and may become a promising tool to improve success rates with ART. AI models may compensate for the lack of objectivity in several critical procedures in fertility clinics, especially embryo and sperm assessments. Various models have been developed, and even though several of them show promising performance, there are still many challenges to overcome. In this review, we present recent research on AI in the context of ART. We discuss the strengths and weaknesses of the presented methods, especially regarding clinical relevance. We also address the pitfalls hampering successful use of AI in the clinic and discuss future possibilities and important aspects to make AI truly useful for ART.
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Affiliation(s)
- M A Riegler
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | | | - O Witczak
- Department of Life Sciences and Health, Faculty of Health Sciences, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - J M Andersen
- Department of Life Sciences and Health, Faculty of Health Sciences, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - S A Hicks
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.,Department of Computer Science, Faculty of Technology, Art and Design, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - H L Hammer
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.,Department of Computer Science, Faculty of Technology, Art and Design, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - E Delbarre
- Department of Life Sciences and Health, Faculty of Health Sciences, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - P Halvorsen
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.,Department of Computer Science, Faculty of Technology, Art and Design, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - A Yazidi
- Department of Computer Science, Faculty of Technology, Art and Design, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - N Holst
- Fertilitetssenteret, Oslo, Norway
| | - T B Haugen
- Department of Life Sciences and Health, Faculty of Health Sciences, OsloMet-Oslo Metropolitan University, Oslo, Norway
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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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40
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Geller J, Collazo I, Pai R, Hendon N, Lokeshwar SD, Arora H, Molina M, Ramasamy R. An Artificial Intelligence-Based Algorithm for Predicting Pregnancy Success Using Static Images Captured by Optical Light Microscopy during Intracytoplasmic Sperm Injection. J Hum Reprod Sci 2021; 14:288-292. [PMID: 34759619 PMCID: PMC8527076 DOI: 10.4103/jhrs.jhrs_53_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/11/2021] [Accepted: 07/16/2021] [Indexed: 12/02/2022] Open
Abstract
CONTEXT BACKGROUND Analysis of embryos for in vitro fertilization (IVF) involves manual grading of human embryos through light microscopy. Recent research shows that artificial intelligence techniques applied to time lapse embryo images can successfully ascertain embryo quality. However, laboratories often capture static images and cannot apply this research in a real-world setting. Further, current models do not predict the outcome of pregnancy. AIMS To create and assess a convolutional neural network to predict embryo quality using static images from a limited dataset. We considered two classification problems: predicting whether an embryo will lead to a pregnancy or not and predicting the outcome of that pregnancy. SETTINGS AND DESIGN We utilized transfer learning techniques using a pretrained Inception V1 network. All models were built using the Tensorflow software package. METHODS We utilized a total of 361 randomly sampled static images collected from four South Florida IVF clinics. Data were collected between 2016 and 2019. STATISTICAL ANALYSIS USED We utilized deep-learning techniques, including data augmentation to reduce model variance and transfer learning to bolster our limited dataset. We used a standard train/validation/ test dataset split to avoid model overfitting. RESULTS Our algorithm achieved 0.657 area under the curve for predicting pregnancy versus nonpregnancy. However, our model was unable to meaningfully predict whether a pregnancy led a to live birth. CONCLUSIONS Despite the limited dataset that achieved somewhat of a lower accuracy than conventional embryo selection, this is the first study that has successfully made IVF predictions from static images alone. Future availability of more data may allow for prospective validation and further generalisability of results.
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Affiliation(s)
- Jared Geller
- Department of Urology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Ineabelle Collazo
- Department of Urology, University of Miami – Miller School of Medicine, Miami, FL, USA
| | - Raghav Pai
- Department of Urology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Nicholas Hendon
- Department of Urology, University of Miami – Miller School of Medicine, Miami, FL, USA
| | - Soum D. Lokeshwar
- Department of Urology, Yale University School of Medicine, New Haven, CT, USA
| | - Himanshu Arora
- Department of Urology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Manuel Molina
- Department of Urology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Ranjith Ramasamy
- Department of Urology, Miller School of Medicine, University of Miami, Miami, FL, USA
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Kanakasabapathy MK, Thirumalaraju P, Kandula H, Doshi F, Sivakumar AD, Kartik D, Gupta R, Pooniwala R, Branda JA, Tsibris AM, Kuritzkes DR, Petrozza JC, Bormann CL, Shafiee H. Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images. Nat Biomed Eng 2021; 5:571-585. [PMID: 34112997 PMCID: PMC8943917 DOI: 10.1038/s41551-021-00733-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 04/15/2021] [Indexed: 01/30/2023]
Abstract
In machine learning for image-based medical diagnostics, supervised convolutional neural networks are typically trained with large and expertly annotated datasets obtained using high-resolution imaging systems. Moreover, the network's performance can degrade substantially when applied to a dataset with a different distribution. Here, we show that adversarial learning can be used to develop high-performing networks trained on unannotated medical images of varying image quality. Specifically, we used low-quality images acquired using inexpensive portable optical systems to train networks for the evaluation of human embryos, the quantification of human sperm morphology and the diagnosis of malarial infections in the blood, and show that the networks performed well across different data distributions. We also show that adversarial learning can be used with unlabelled data from unseen domain-shifted datasets to adapt pretrained supervised networks to new distributions, even when data from the original distribution are not available. Adaptive adversarial networks may expand the use of validated neural-network models for the evaluation of data collected from multiple imaging systems of varying quality without compromising the knowledge stored in the network.
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Affiliation(s)
- Manoj Kumar Kanakasabapathy
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Prudhvi Thirumalaraju
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hemanth Kandula
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fenil Doshi
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anjali Devi Sivakumar
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Deeksha Kartik
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Raghav Gupta
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rohan Pooniwala
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - John A Branda
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Athe M Tsibris
- Division of Infectious Diseases, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel R Kuritzkes
- Division of Infectious Diseases, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - John C Petrozza
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Charles L Bormann
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hadi Shafiee
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Zaninovic N, Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil Steril 2021; 114:914-920. [PMID: 33160513 DOI: 10.1016/j.fertnstert.2020.09.157] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 09/23/2020] [Indexed: 02/06/2023]
Abstract
Embryo evaluation and selection embody the aggregate manifestation of the entire in vitro fertilization (IVF) process. It aims to choose the "best" embryos from the larger cohort of fertilized oocytes, the majority of which will be determined to be not viable either as a result of abnormal development or due to chromosomal imbalances. Indeed, it is generally acknowledged that even after embryo selection based on morphology, time-lapse microscopic photography, or embryo biopsy with preimplantation genetic testing, implantation rates in the human are difficult to predict. Our pursuit of enhancing embryo evaluation and selection, as well as increasing live birth rates, will require the adoption of novel technologies. Recently, several artificial intelligence (AI)-based methods have emerged as objective, standardized, and efficient tools for evaluating human embryos. Moreover, AI-based methods can be implemented for other clinical aspects of IVF, such as assessing patient reproductive potential and individualizing gonadotropin stimulation protocols. As AI has the capability to analyze "big" data, the ultimate goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data in an effort to provide patient-tailored treatments. In this chapter, we present an overview of existing AI technologies in reproductive medicine and envision their potential future applications in the field.
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Affiliation(s)
- Nikica Zaninovic
- The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York.
| | - Zev Rosenwaks
- The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York
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Targosz A, Przystałka P, Wiaderkiewicz R, Mrugacz G. Semantic segmentation of human oocyte images using deep neural networks. Biomed Eng Online 2021; 20:40. [PMID: 33892725 PMCID: PMC8066497 DOI: 10.1186/s12938-021-00864-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 03/04/2021] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Infertility is a significant problem of humanity. In vitro fertilisation is one of the most effective and frequently applied ART methods. The effectiveness IVF depends on the assessment and selection of gametes and embryo with the highest developmental potential. The subjective nature of morphological assessment of oocytes and embryos is still one of the main reasons for seeking effective and objective methods for assessing quality in automatic manner. The most promising methods to automatic classification of oocytes and embryos are based on image analysis aided by machine learning techniques. The special attention is paid on deep neural networks that can be used as classifiers solving the problem of automatic assessment of the oocytes/embryos. METHODS This paper deals with semantic segmentation of human oocyte images using deep neural networks in order to develop new version of the predefined neural networks. Deep semantic oocyte segmentation networks can be seen as medically oriented predefined networks understanding the content of the image. The research presented in the paper is focused on the performance comparison of different types of convolutional neural networks for semantic oocyte segmentation. In the case study, the merits and limitations of the selected deep neural networks are analysed. RESULTS 71 deep neural models were analysed. The best score was obtained for one of the variants of DeepLab-v3-ResNet-18 model, when the training accuracy (Acc) reached about 85% for training patterns and 79% for validation ones. The weighted intersection over union (wIoU) and global accuracy (gAcc) for test patterns were calculated, as well. The obtained values of these quality measures were 0,897 and 0.93, respectively. CONCLUSION The obtained results prove that the proposed approach can be applied to create deep neural models for semantic oocyte segmentation with the high accuracy guaranteeing their usage as the predefined networks in other tasks.
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Affiliation(s)
- Anna Targosz
- Department of Histology and Embryology, Medical University of Silesia, Faculty of Medical Sciences, 18 Medyków St., 40-752 Katowice, Poland
- Center for Reproductive Medicine Bocian, 26 Akademicka St., 15-267 Białystok, Poland
| | - Piotr Przystałka
- Department of Fundamentals of Machinery Design, Silesian University of Technology, Faculty of Mechanical Engineering, 18a Konarskiego St., 44-100 Gliwice, Poland
| | - Ryszard Wiaderkiewicz
- Department of Histology and Embryology, Medical University of Silesia, Faculty of Medical Sciences, 18 Medyków St., 40-752 Katowice, Poland
| | - Grzegorz Mrugacz
- Center for Reproductive Medicine Bocian, 26 Akademicka St., 15-267 Białystok, Poland
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44
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Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF. J Assist Reprod Genet 2021; 38:1627-1639. [PMID: 33811587 DOI: 10.1007/s10815-021-02123-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 02/21/2021] [Indexed: 12/30/2022] Open
Abstract
In vitro fertilization has been regarded as a forefront solution in treating infertility for over four decades, yet its effectiveness has remained relatively low. This could be attributed to the lack of advancements for the method of observing and selecting the most viable embryos for implantation. The conventional morphological assessment of embryos exhibits inevitable drawbacks which include time- and effort-consuming, and imminent risks of bias associated with subjective assessments performed by individual embryologists. A combination of these disadvantages, undeterred by the introduction of the time-lapse incubator technology, has been considered as a prominent contributor to the less preferable success rate of IVF cycles. Nonetheless, a recent surge of AI-based solutions for tasks automation in IVF has been observed. An AI-powered assistant could improve the efficiency of performing certain tasks in addition to offering accurate algorithms that behave as baselines to minimize the subjectivity of the decision-making process. Through a comprehensive review, we have discovered multiple approaches of implementing deep learning technology, each with varying degrees of success, for constructing the automated systems in IVF which could evaluate and even annotate the developmental stages of an embryo.
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Curchoe CL. All Models Are Wrong, but Some Are Useful. J Assist Reprod Genet 2020; 37:2389-2391. [PMID: 33026558 DOI: 10.1007/s10815-020-01895-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 07/14/2020] [Indexed: 11/26/2022] Open
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Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation. Reprod Biomed Online 2020; 41:585-593. [DOI: 10.1016/j.rbmo.2020.07.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 06/26/2020] [Accepted: 07/02/2020] [Indexed: 12/23/2022]
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Bormann CL, Kanakasabapathy MK, Thirumalaraju P, Gupta R, Pooniwala R, Kandula H, Hariton E, Souter I, Dimitriadis I, Ramirez LB, Curchoe CL, Swain J, Boehnlein LM, Shafiee H. Performance of a deep learning based neural network in the selection of human blastocysts for implantation. eLife 2020; 9:e55301. [PMID: 32930094 PMCID: PMC7527234 DOI: 10.7554/elife.55301] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 09/01/2020] [Indexed: 11/13/2022] Open
Abstract
Deep learning in in vitro fertilization is currently being evaluated in the development of assistive tools for the determination of transfer order and implantation potential using time-lapse data collected through expensive imaging hardware. Assistive tools and algorithms that can work with static images, however, can help in improving the access to care by enabling their use with images acquired from traditional microscopes that are available to virtually all fertility centers. Here, we evaluated the use of a deep convolutional neural network (CNN), trained using single timepoint images of embryos collected at 113 hr post-insemination, in embryo selection amongst 97 clinical patient cohorts (742 embryos) and observed an accuracy of 90% in choosing the highest quality embryo available. Furthermore, a CNN trained to assess an embryo's implantation potential directly using a set of 97 euploid embryos capable of implantation outperformed 15 trained embryologists (75.26% vs. 67.35%, p<0.0001) from five different fertility centers.
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Affiliation(s)
- Charles L Bormann
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
- Harvard Medical SchoolBostonUnited States
| | - Manoj Kumar Kanakasabapathy
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolBostonUnited States
| | - Prudhvi Thirumalaraju
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolBostonUnited States
| | - Raghav Gupta
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolBostonUnited States
| | - Rohan Pooniwala
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolBostonUnited States
| | - Hemanth Kandula
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolBostonUnited States
| | - Eduardo Hariton
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
| | - Irene Souter
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
- Harvard Medical SchoolBostonUnited States
| | - Irene Dimitriadis
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
- Harvard Medical SchoolBostonUnited States
| | | | - Carol L Curchoe
- San Diego Fertility CenterSan DiegoUnited States
- Colorado Center for Reproductive Medicine IVF Laboratory NetworkEnglewoodUnited States
| | - Jason Swain
- Colorado Center for Reproductive Medicine IVF Laboratory NetworkEnglewoodUnited States
| | - Lynn M Boehnlein
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, University of WisconsinMadisonUnited States
| | - Hadi Shafiee
- Harvard Medical SchoolBostonUnited States
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolBostonUnited States
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Fernandez EI, Ferreira AS, Cecílio MHM, Chéles DS, de Souza RCM, Nogueira MFG, Rocha JC. Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. J Assist Reprod Genet 2020; 37:2359-2376. [PMID: 32654105 DOI: 10.1007/s10815-020-01881-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 07/03/2020] [Indexed: 12/18/2022] Open
Abstract
Over the past years, the assisted reproductive technologies (ARTs) have been accompanied by constant innovations. For instance, intracytoplasmic sperm injection (ICSI), time-lapse monitoring of the embryonic morphokinetics, and PGS are innovative techniques that increased the success of the ART. In the same trend, the use of artificial intelligence (AI) techniques is being intensively researched whether in the embryo or spermatozoa selection. Despite several studies already published, the use of AI within assisted reproduction clinics is not yet a reality. This is largely due to the different AI techniques that are being proposed to be used in the daily routine of the clinics, which causes some uncertainty in their use. To shed light on this complex scenario, this review briefly describes some of the most frequently used AI algorithms, their functionalities, and their potential use. Several databases were analyzed in search of articles where applied artificial intelligence algorithms were used on reproductive data. Our focus was on the classification of embryonic cells and semen samples. Of a total of 124 articles analyzed, 32 were selected for this review. From the proposed algorithms, most have achieved a satisfactory precision, demonstrating the potential of a wide range of AI techniques. However, the evaluation of these studies suggests the need for more standardized research to validate the proposed models and their algorithms. Routine use of AI in assisted reproduction clinics is just a matter of time. However, the choice of AI technique to be used is supported by a better understanding of the principles subjacent to each technique, that is, its robustness, pros, and cons. We provide some current (although incipient) and potential uses of AI on the clinic routine, discussing how accurate and friendly it could be. Finally, we propose some standards for AI research on the selection of the embryo to be transferred and other future hints. For us, the imminence of its use is evident, providing a revolutionary milestone that will impact the ART.
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Affiliation(s)
- Eleonora Inácio Fernandez
- Laboratory of Applied Mathematics, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil
| | - André Satoshi Ferreira
- Laboratory of Applied Mathematics, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil
| | - Matheus Henrique Miquelão Cecílio
- Laboratory of Applied Mathematics, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil
| | - Dóris Spinosa Chéles
- Laboratory of Applied Mathematics, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil.,Laboratory of Embryonic Micromanipulation, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil
| | - Rebeca Colauto Milanezi de Souza
- Laboratory of Applied Mathematics, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil
| | - Marcelo Fábio Gouveia Nogueira
- Laboratory of Embryonic Micromanipulation, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil
| | - José Celso Rocha
- Laboratory of Applied Mathematics, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil. .,Universidade Estadual Paulista Julio de Mesquita Filho, Assis, São Paulo, Brazil.
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Go KJ. Beauty (quality) is in the eye of the convoluted neural network. Fertil Steril 2020; 113:756-757. [DOI: 10.1016/j.fertnstert.2020.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 01/03/2020] [Indexed: 11/26/2022]
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