scholarly journals Embryo selection with artificial intelligence: how to evaluate and compare methods?

2021 ◽  
Author(s):  
Mikkel Fly Kragh ◽  
Henrik Karstoft
Keyword(s):  
Artificial Intelligence ◽  
Performance Metrics ◽  
Selection Procedure ◽  
Relevant Information ◽  
Embryo Selection ◽  
Vitro Fertilization ◽  
Ranking Performance ◽  
Retrospective Cohort Studies ◽  
Microscopy Images

AbstractEmbryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent years, artificial intelligence (AI) has been used extensively to improve and automate the embryo ranking and selection procedure by extracting relevant information from embryo microscopy images. The AI models are evaluated based on their ability to identify the embryo(s) with the highest chance(s) of achieving a successful pregnancy. Whether such evaluations should be based on ranking performance or pregnancy prediction, however, seems to divide studies. As such, a variety of performance metrics are reported, and comparisons between studies are often made on different outcomes and data foundations. Moreover, superiority of AI methods over manual human evaluation is often claimed based on retrospective data, without any mentions of potential bias. In this paper, we provide a technical view on some of the major topics that divide how current AI models are trained, evaluated and compared. We explain and discuss the most common evaluation metrics and relate them to the two separate evaluation objectives, ranking and prediction. We also discuss when and how to compare AI models across studies and explain in detail how a selection bias is inevitable when comparing AI models against current embryo selection practice in retrospective cohort studies.

scholarly journals Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Qingsong Xi ◽  
Qiyu Yang ◽  
Meng Wang ◽  
Bo Huang ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Endometrial Thickness ◽  
Learning System ◽  
Embryo Selection ◽  
Selection Strategy ◽  
Application Study ◽  
Vitro Fertilization ◽  
The Impact

Abstract Background To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing. Methods This was an application study including 9211 patients with 10,076 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes. Results For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1 + P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1 × P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree. Conclusion Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

scholarly journals Detection of human embryo aneuploidy in the procedure of in vitro fertilization after preimplantation genetic screening using array comparative genomic hybridization

2017 ◽  
Vol 70 (9-10) ◽  
pp. 325-331
Author(s):  
Jelena Vukosavljevic ◽  
Aleksandra Trninic-Pjevic ◽  
Artur Bjelica ◽  
Ivana Jagodic ◽  
Vesna Kopitovic ◽  
...  
Keyword(s):  
In Vitro Fertilization ◽  
Comparative Genomic Hybridization ◽  
Genetic Screening ◽  
Array Comparative Genomic Hybridization ◽  
Preimplantation Genetic Screening ◽  
Embryo Selection ◽  
Comparative Genomic ◽  
Genomic Hybridization ◽  
Vitro Fertilization

Introduction. Numerical aberrations (whole chromosomal aneuploidy) have been considered one of the most important factors leading to implantation failure and early miscarriages in patients undergoing assisted reproductive procedures. Embryo selection is mainly based on morphological assessment; however, embryos produced from aneuploid gametes cannot be distinguished from euploid based on morphological characteristics. Detection of aneuploidy in human embryos. Thanks to the introduction of molecular-genetic screening of embryos, it is possible to identify aneuploid embryos via preimplantation genetic screening/diagnosis and thus select the best embryos based on their ploidy. Array comparative genomic hybridization is a molecular technique which allows ploidy analysis of the entire genome amplification from a single cell, within 24 hours after polar body, blastomere or trophectoderm cell biopsy. Trophectoderm cell biopsy is considered the most reliable screening approach given the lower mosaicism appearance at the blastocyst stage. Conclusion. This paper points to the importance and necessity of molecular analysis in embryo selection. Further investigations and improvements are required, because this technology has only recently become available in clinical practice in the in vitro fertilization procedure.

scholarly journals Reproductive aspects of the male jaguar (Panthera onca): A review

2021 ◽  
Author(s):  
E. López-Pérez ◽  
F. Cortés-Villavicencio ◽  
C. Muñoz-García ◽  
J. Gallegos-Sánchez ◽  
Alejandro Ávalos-Rodríguez
Keyword(s):  
In Vitro Fertilization ◽  
Assisted Reproduction ◽  
Reproductive System ◽  
Artificial Insemination ◽  
Relevant Information ◽  
Panthera Onca ◽  
Leopardus Pardalis ◽  
Assisted Reproduction Techniques ◽  
Vitro Fertilization

Objective: To describe the anatomy, morphology and physiology of the reproductive system of male jaguars, as well as assisted reproduction techniques. Methodology: A literature review on the anatomy and morphology of the jaguar´s reproductive system, its physiological characteristics and assisted reproduction techniques were carried out to document relevant information on the topic. Results: With this review, basic aspects of the morphology of the reproductive system of the jaguars are disclosed, although scarce knowledge is available on their reproduction. The advances in the collection, evaluation and cryopreservation of semen of this feline are shown, in addition to assisted reproduction techniques such as artificial insemination and in vitro fertilization, which have a great potential to safeguard the species. Study limitations: The jaguar, an emblematic species of Latinamerica, is an endangered species, like other wild felids species as ocelot (Leopardus pardalis) and margay (Leopardus wiedii), which makes it necessary to have a national assisted reproduction program. However, for this to be possible, information about their reproductive physiology is necessary, which is complicated in wild animals and even more so because the reproductive mechanisms greatly differ between felids species. There is scarce information in this regard from its free-living or Mexican zoos, it is for this reason necessary to generate such information. Conclusions: It is necessary to continue working on designing protocols for artificial insemination and other assisted reproduction techniques such as in-vitro fertilization specifically for male Panthera onca.

Lighting-Aware Segmentation of Microscopy Images for In Vitro Fertilization

2009 ◽  
pp. 576-585 ◽  
Author(s):  
Alessandro Giusti ◽  
Giorgio Corani ◽  
Luca Maria Gambardella ◽  
Cristina Magli ◽  
Luca Gianaroli
Keyword(s):  
In Vitro Fertilization ◽  
Vitro Fertilization ◽  
Microscopy Images

Application of Machine Learning and Artificial Intelligence Techniques for IVF Analysis and Prediction

2019 ◽  
Vol 4 (2) ◽  
pp. 21-33
Author(s):  
Satya Kiranmai Tadepalli ◽  
P.V. Lakshmi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Future Generation ◽  
Machine Learning Techniques ◽  
Successful Pregnancy ◽  
Vitro Fertilization ◽  
Digital Revolution ◽  
Learning Techniques ◽  
Assistive Reproductive Technology

Infertility is the combination of factors that prevent pregnancy. It involves a lot of care and expertise while selecting the best embryo to lead to a successful pregnancy. Assistive reproductive technology (ART) helps to solve this issue. In vitro fertilization (IVF) is one of the methods of ART which is very popular. Artificial intelligence will have digital revolution and manifold advances in the field of reproductive medicine and will eventually provide immense benefits to infertile patients. The main aim of this article is to focus on the methods that can predict the accuracy of pregnancy without human intervention. It provides successful studies conducted by using machine learning techniques. This easily enables doctors to understand the behavior of the attributes which are suitable for the treatment. Blastocyst images can be deployed for the detection and prediction of the best embryo which has the maximum chance of a successful pregnancy. This pioneering work gives one a view into how this field could benefit the future generation.

scholarly journals Individualized Embryo Selection Strategy Developed by Stacking Machine Learning Model for Better In Vitro Fertilization Outcomes: An Application Study.

2021 ◽  
Author(s):  
Qingsong XI ◽  
Qiyu YANG ◽  
Meng WANG ◽  
Bo HUANG ◽  
Bo ZHANG ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Endometrial Thickness ◽  
Learning System ◽  
Embryo Selection ◽  
Selection Strategy ◽  
Application Study ◽  
Vitro Fertilization ◽  
The Impact

Abstract Background: To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing. Methods: This was an application study including 7887 patients with 8585 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes.Results: For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1+P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1×P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree. Conclusion: Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

scholarly journals Machine Learning-Guided Noninvasive Embryo Selection for Clinical in Vitro Fertilization Treatment to Avoid Wasting Potentially Qualified Embryos

2021 ◽  
Author(s):  
Li Chen ◽  
Wen Li ◽  
Yuxiu Liu ◽  
Zhihang Peng ◽  
Liyi Cai ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Learning Algorithm ◽  
Embryo Selection ◽  
Human Embryos ◽  
Utilization Rate ◽  
Grading System ◽  
Vitro Fertilization ◽  
Embryo Grading

Abstract BackgroundThe success rates of in vitro fertilization (IVF) treatment are limited by the aneuploidy of human embryos. Pre-implantation genetic testing for aneuploidy(PGT-A) is often used to select embryos with normal ploidy but requires invasive embryo biopsy. MethodsWe performed chromosome sequencing of 345 paired blastocyst culture medium and whole blastocyst samples and developed a noninvasive embryo grading system based on the random forest machine-learning algorithm to predict blastocyst ploidy. The system was validated in 266 patients, and a blinded prospective observational study was performed to investigate clinical outcomes between machine learning-guided and traditional niPGT-A analyses. We graded embryos as A, B, or C using machine learning-guided niPGT-A analysis according to their euploidy probability levels predicted by noninvasive chromosomal screening. ResultsWe observed higher live birth rate in A- versus C-grade embryos (50.4% versus 27.1%, p=0.006) and B- versus C-grade embryos (45.3% versus 27.1%, p=0.022) and lower miscarriage rate in A- versus C-grade embryos (15.9% versus 33.3%, p=0.026) and B- versus C-grade embryos (14.3% versus 33.3%, p=0.021). The embryo utilization rate was significantly higher through machine learning strategy compared to the conventional dichotomic judgment of euploidy or aneuploidy in the niPGT-A analysis (78.8% versus 57.9%, p<0.001). We observed better outcomes in A- and B-grade embryos versus C-grade embryos and higher embryo utilization rates through machine learning strategies than traditional niPGT-A analysis. ConclusionThese results demonstrate that the machine learning-guided embryo grading system can optimize embryo selection and avoid wasting potential embryos.Trial registrationChinese Clinical Trial Registry,ChiCTR-RRC-17010396.Registered 11 January 2017, http://www.chictr.org.cn/ChiCTR-RRC-17010396

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