Bayesian classification for the selection of in vitro human embryos using morphological and clinical data

A brief history of artificial intelligence embryo selection: from black-box to glass-box

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.

Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical models

Embryo selection is a critical step in assisted reproduction: good selection criteria are expected to increase the probability of inducing a pregnancy. Machine learning techniques have been applied for implantation prediction or embryo quality assessment, which embryologists can use to make a decision about embryo selection. However, this is a highly uncertain real-world problem, and current proposals do not model always all the sources of uncertainty. We present a novel probabilistic graphical model that accounts for three different sources of uncertainty, the standard embryo and cycle viability, and a third one that represents any unknown factor that can drive a treatment to a failure in otherwise perfect conditions. We derive a parametric learning method based on the Expectation-Maximization strategy, which accounts for uncertainty issues. We empirically analyze the model within a real database consisting of 604 cycles (3125 embryos) carried out at Hospital Donostia (Spain). Embryologists followed the protocol of the Spanish Association for Reproduction Biology Studies (ASEBIR), based on morphological features, for embryo selection. Our model predictions are correlated with the ASEBIR protocol, which validates our model. The benefits of accounting for the different sources of uncertainty and the importance of the cycle characteristics are shown. Considering only transferred embryos, our model does not further discriminate them as implanted or failed, suggesting that the ASEBIR protocol could be understood as a thorough summary of the available morphological features.

Interpretable, not black-box, artificial intelligence should be used for embryo selection

Artificial intelligence (AI) techniques are starting to be used in IVF, in particular for selecting which embryos to transfer to the woman. AI has the potential to process complex data sets, to be better at identifying subtle but important patterns, and to be more objective than humans when evaluating embryos. However, a current review of the literature shows much work is still needed before AI can be ethically implemented for this purpose. No randomized controlled trials (RCTs) have been published, and the efficacy studies which exist demonstrate that algorithms can broadly differentiate well between 'good-' and 'poor-' quality embryos but not necessarily between embryos of similar quality, which is the actual clinical need. Almost universally, the AI models were opaque ('black-box') in that at least some part of the process was uninterpretable. This gives rise to a number of epistemic and ethical concerns, including problems with trust, the possibility of using algorithms that generalize poorly to different populations, adverse economic implications for IVF clinics, potential misrepresentation of patient values, broader societal implications, a responsibility gap in the case of poor selection choices and introduction of a more paternalistic decision-making process. Use of interpretable models, which are constrained so that a human can easily understand and explain them, could overcome these concerns. The contribution of AI to IVF is potentially significant, but we recommend that AI models used in this field should be interpretable, and rigorously evaluated with RCTs before implementation. We also recommend long-term follow-up of children born after AI for embryo selection, regulatory oversight for implementation, and public availability of data and code to enable research teams to independently reproduce and validate existing models.

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Objective

Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes.

Materials and methods

We searched English articles on EMBASE, PubMed and SCOPUS. Search terms included ML, AI, pregnancy and informatics. We included research articles and book chapters, excluding conference papers, editorials and notes.

Results

We identified 127 distinct studies from our queries that were relevant to our topic and included in the review. We found that supervised learning methods were more popular (n = 69) than unsupervised methods (n = 9). Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). Methods such as DL are beginning to gain traction (n = 13). Common areas within the pregnancy domain where AI and ML methods were used the most include prenatal care (e.g. fetal anomalies, placental functioning) (n = 73); perinatal care, birth and delivery (n = 20); and preterm birth (n = 13). Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9).

Conclusions

Overall, we found that ML and AI methods are being employed to optimize pregnancy outcomes, including modern DL methods (n = 13). Future research should focus on less-studied pregnancy domain areas, including postnatal and postpartum care (n = 2). Also, more work on clinical adoption of AI methods and the ethical implications of such adoption is needed.

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

BACKGROUND

Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method.

OBJECTIVE

This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians' decision making.

METHODS

Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians' decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2.

RESULTS

Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07).

CONCLUSIONS

Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines.

TRIAL REGISTRATION

PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106.

Application of Artificial Intelligence Algorithms to Estimate the Success Rate in Medically Assisted Procreation

The aim of this study was to build an Artificial Neural Network (ANN) complemented by a decision tree to predict the chance of live birth after an In Vitro Fertilization (IVF)/Intracytoplasmic Sperm Injection (ICSI) treatment, before the first embryo transfer, using demographic and clinical data. Overall, 26 demographic and clinical data from 1193 cycles who underwent an IVF/ICSI treatment at Centro de Infertilidade e Reprodução Medicamente Assistida, between 2012 and 2019, were analyzed. An ANN was constructed by selecting experimentally the input variables which most correlated to the target through Pearson correlation. The final used variables were: woman’s age, total dose of gonadotropin, number of eggs, number of embryos and Antral Follicle Count (AFC). A decision tree was developed considering as an initial set the input variables integrated in the previous model. The ANN model was validated by the holdout method and the decision tree model by the 10-fold cross method. The ANN accuracy was 75.0% and the Area Under the Receiver Operating Characteristic (AUROC) curve was 75.2% (95% Confidence Interval (CI): 72.5–77.5%), whereas the decision tree model reached 75.0% and 74.9% (95% CI: 72.3–77.5%). These results demonstrated that both ANN and decision tree methods are fair for prediction the chance of conceive after an IVF/ICSI cycle.

Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data

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.

Artificial intelligence in reproductive medicine

Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine learning techniques have been used to improve the performance of assisted reproductive technology (ART). Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. In this review, we discuss the basic aspects of AI and machine learning, and we address the applications, potential limitations and challenges of AI. We also highlight the prospects and future directions in the context of reproductive medicine.

A Review of Machine Learning Approaches in Assisted Reproductive Technologies

INTRODUCTION

Assisted reproductive technologies (ART) are recent improvements in infertility treatment. However, there is no significant increase in pregnancy rates with the aid of ART. Costly and complex process of ART's makes them as challenging issues. Computational prediction models could predict treatment outcome, before the start of an ART cycle.

AIM

This review provides an overview on machine learning-based prediction models in ART.

METHODS

This article was executed based on a literature review through scientific databases search such as PubMed, Scopus, Web of Science and Google Scholar.

RESULTS

We identified 20 papers reporting on machine learning-based prediction models in IVF or ICSI settings. All of the models were validated only by internal validation. Therefore, external validation of the models and the impact analysis of them were the missing parts of the all studies.

CONCLUSION

Machine learning-based prediction models provide a clinical decision support tool for both clinicians and patients and lead to improvement in ART success rates.

Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence

Mathematics rules the world of science. Innovative technologies based on mathematics have paved the way for implementation of novel strategies in assisted reproduction. Ascertaining efficient embryo selection in order to secure optimal pregnancy rates remains the focus of the in vitro fertilization scientific community and the strongest driver behind innovative approaches. This scoping review aims to describe and analyze complex models based on mathematics for embryo selection, devices, and software most widely employed in the IVF laboratory and algorithms in the service of the cutting-edge technology of artificial intelligence. Despite their promising nature, the practicing embryologist is the one ultimately responsible for the success of the IVF laboratory and thus the one to approve embracing pioneering technologies in routine practice. Applied mathematics and computational biology have already provided significant insight into the selection of the most competent preimplantation embryo. This review describes the leap of evolution from basic mathematics to bioinformatics and investigates the possibility that computational applications may be the means to foretell a promising future for the IVF clinical practice.

An automated blastomere identification method for the evaluation of day 2 embryos during IVF/ICSI treatments

PURPOSE

Evaluation of human embryos is one of the most important challenges in vitro fertilization (IVF) programs. The morphology and the morphokinetic parameters of the early cleaving embryo are of critical clinical importance. This stage spans the first 48 h post-fertilization, in which the embryo is dividing in smaller blastomeres at specific time-points. The morphology, in combination with the symmetry of the blastomeres seems to be powerful features with strong prognostic value for embryo evaluation. To date, the identification of these features is based on human inspection in timed intervals, at best using camera systems that simply work as surveillance systems without any precise alerting and decision support mechanisms. The purpose of the study presented in this paper was to develop a computer vision technique to automatically detect and identify the most suitable cleaving embryos (preferably at day 2 post-fertilization) for embryo transfer (ET) during IVF/ICSI treatments.

METHODS AND RESULTS

To this end, texture and geometrical features were used to localize and analyze the whole cleaving embryo in 2D grayscale images captured during in vitro embryo formation. Because of the ellipsoidal nature of blastomeres, the contour of each blastomere was modeled with an optimal fitting ellipse while the mean eccentricity of all ellipses is computed. The mean eccentricity in combination with the number of blastomeres forms the feature space on which the final criterion for the embryo evaluation was based.

CONCLUSIONS

Experimental results with low quality 2D grayscale images demonstrated the effectiveness of the proposed technique and provided evidence of a novel automated approach for predicting embryo quality.

Predicting pregnancy rate following multiple embryo transfers using algorithms developed through static image analysis

Single-embryo image assessment involves a high degree of inaccuracy because of the imprecise labelling of the transferred embryo images. In this study, we considered the entire transfer cycle to predict the implantation potential of embryos, and propose a novel algorithm based on a combination of local binary pattern texture feature and Adaboost classifiers to predict pregnancy rate. The first step of the proposed method was to extract the features of the embryo images using the local binary pattern operator. After this, multiple embryo images in a transfer cycle were considered as one entity, and the pregnancy rate was predicted using three classifiers: the Real Adaboost, Gentle Adaboost, and Modest Adaboost. Finally, the pregnancy rate was determined via the majority vote rule based on classification results of the three Adaboost classifiers. The proposed algorithm was verified to have a good predictive performance and may assist the embryologist and clinician to select embryos to transfer and in turn improve pregnancy rate.

Detecting Ellipses in Embryo Images Using Arc Detection Method with Particle Swarm for Blastomere-Quality Measurement System

The objective of this paper is to present a novel method, based on a swarm intelligence algorithm, for ellipse detection in digital images of embryo. The process is carried out in several stages. First, edge detection is performed on the image. Then, line segments in the image are detected, and potential elliptical arc segments are extracted from the line segments. Afterward, the detection process is carried out using the Particle Swarm Optimization (PSO) method, which utilize the calculation of the fitness function from the arc segment previously detected. The PSO technique, which is the idea behind the proposed algorithm, is used to find the actual ellipses by combining potential elliptical arcs. The best combination of potential arcs is determined by means a voting technique that utilizes three important points on the arc, namely the starting point, midpoint, and endpoint, so the voting is more efficient than doing the voting for every single pixel in the image. Furthermore, this method is used an embryo image that has following the characteristics: multiple ellipses, a lot of noise, an incomplete ellipse, low image contrast, and overlapping cells. Experiment show that the proposed method detects the ellipses better than do several voting-based ellipse detection methods such as RHT, IRHT, and PSORHT. On the other hand, the experiments show that the proposed method has a higher average hit rate than do other methods. This research is used to increase the success rate of In-Vitro Fertilization (IVF).

Applying data mining techniques for increasing implantation rate by selecting best sperms for intra-cytoplasmic sperm injection treatment

BACKGROUND AND OBJECTIVE

Aspiration of a good-quality sperm during intracytoplasmic sperm injection (ICSI) is one of the main concerns. Understanding the influence of individual sperm morphology on fertilization, embryo quality, and pregnancy probability is one of the most important subjects in male factor infertility. Embryologists need to decide the best sperm for injection in real time during ICSI cycle. Our objective is to predict the quality of zygote, embryo, and implantation outcome before injection of each sperm in an ICSI cycle for male factor infertility with the aim of providing a decision support system on the sperm selection.

METHODS

The information was collected from 219 patients with male factor infertility at the infertility therapy center of Alzahra hospital in Rasht from 2012 through 2014. The prepared dataset included the quality of zygote, embryo, and implantation outcome of 1544 injected sperms into the related oocytes. In our study, embryo transfer was performed at day 3. Each sperm was represented with thirteen clinical features. Data preprocessing was the first step in the proposed data mining algorithm. After applying more than 30 classifiers, 9 successful classifiers were selected and evaluated by 10-fold cross validation technique using precision, recall, F1, and AUC measures. Another important experiment was measuring the effect of each feature in prediction process.

RESULTS

In zygote and embryo quality prediction, IBK and RandomCommittee models provided 79.2% and 83.8% F1, respectively. In implantation outcome prediction, KStar model achieved 95.9% F1, which is even better than prediction of human experts. All these predictions can be done in real time.

CONCLUSIONS

A machine learning-based decision support system would be helpful in sperm selection phase of ICSI cycle to improve the success rate of ICSI treatment.

Automatic segmentation of zona pellucida and its application in cleavage-stage embryo biopsy position selection

A very important step of Pre-implantation genetic diagnosis (PGD) is embryo biopsy, in which process the zona pellucida (ZP) is cut open partially and a part of cellular material is extracted from the embryo. Recognition of the ZP is necessary not only for embryo biopsy, but also for other applications such as zona pellucida thickness variation (ZPTV), embryo dissection, etc. The ZP opening position is closely related to the cell survival rate after the biopsy. Selection of an unsuitable position may cause blastomere lysis after the ZP opening. Normal procedures of ZP recognition and biopsy position selection involve a skilled human embryologist. In order to make the process automatic, we introduce an automatic segmentation method for ZP recognition by using edge detection and ellipse fitting with a value adjustment algorithm in this paper. An application of ZP recognition in embryo biopsy position selection is also introduced. Our ZP recognition algorithm was able to correctly segment 43 out of 45 sample embryo images, achieving a success rate of 96%. Its application in embryo biopsy position selection achieved a success rate of 93%.

Estimating the chance of success in IVF treatment using a ranking algorithm

In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment.

Automatic blastomere recognition from a single embryo image

The number of blastomeres of human day 3 embryos is one of the most important criteria for evaluating embryo viability. However, due to the transparency and overlap of blastomeres, it is a challenge to recognize blastomeres automatically using a single embryo image. This study proposes an approach based on least square curve fitting (LSCF) for automatic blastomere recognition from a single image. First, combining edge detection, deletion of multiple connected points, and dilation and erosion, an effective preprocessing method was designed to obtain part of blastomere edges that were singly connected. Next, an automatic recognition method for blastomeres was proposed using least square circle fitting. This algorithm was tested on 381 embryo microscopic images obtained from the eight-cell period, and the results were compared with those provided by experts. Embryos were recognized with a 0 error rate occupancy of 21.59%, and the ratio of embryos in which the false recognition number was less than or equal to 2 was 83.16%. This experiment demonstrated that our method could efficiently and rapidly recognize the number of blastomeres from a single embryo image without the need to reconstruct the three-dimensional model of the blastomeres first; this method is simple and efficient.

Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting

Background. Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, and medical implications. Clinicians need to decide the number of embryos to be transferred considering the tradeoff between successful outcomes and multiple pregnancies. Objective. To predict implantation outcome of individual embryos in an IVF cycle with the aim of providing decision support on the number of embryos transferred. Design. Retrospective cohort study. Data Source. Electronic health records of one of the largest IVF clinics in Turkey. The study data set included 2453 embryos transferred at day 2 or day 3 after intracytoplasmic sperm injection (ICSI). Each embryo was represented with 18 clinical features and a class label, +1 or -1, indicating positive and negative implantation outcomes, respectively. Methods. For each classifier tested, a model was developed using two-thirds of the data set, and prediction performance was evaluated on the remaining one-third of the samples using receiver operating characteristic (ROC) analysis. The training-testing procedure was repeated 10 times on randomly split (two-thirds to one-third) data. The relative predictive values of clinical input characteristics were assessed using information gain feature weighting and forward feature selection methods. Results. The naïve Bayes model provided 80.4% accuracy, 63.7% sensitivity, and 17.6% false alarm rate in embryo-based implantation prediction. Multiple embryo implantations were predicted at a 63.8% sensitivity level. Predictions using the proposed model resulted in higher accuracy compared with expert judgment alone (on average, 75.7% and 60.1%, respectively). Conclusions. A machine learning–based decision support system would be useful in improving the success rates of IVF treatment.

A Bayesian network model for predicting pregnancy after in vitro fertilization

We present a Bayesian network model for predicting the outcome of in vitro fertilization (IVF). The problem is characterized by a particular missingness process; we propose a simple but effective averaging approach which improves parameter estimates compared to the traditional MAP estimation. We present results with generated data and the analysis of a real data set. Moreover, we assess by means of a simulation study the effectiveness of the model in supporting the selection of the embryos to be transferred.

Modeling to optimize terminal stem cell differentiation

Embryonic stem cell (ESC), iPCs, and adult stem cells (ASCs) all are among the most promising potential treatments for heart failure, spinal cord injury, neurodegenerative diseases, and diabetes. However, considerable uncertainty in the production of ESC-derived terminally differentiated cell types has limited the efficiency of their development. To address this uncertainty, we and other investigators have begun to employ a comprehensive statistical model of ESC differentiation for determining the role of intracellular pathways (e.g., STAT3) in ESC differentiation and determination of germ layer fate. The approach discussed here applies the Baysian statistical model to cell/developmental biology combining traditional flow cytometry methodology and specific morphological observations with advanced statistical and probabilistic modeling and experimental design. The final result of this study is a unique tool and model that enhances the understanding of how and when specific cell fates are determined during differentiation. This model provides a guideline for increasing the production efficiency of therapeutically viable ESCs/iPSCs/ASC derived neurons or any other cell type and will eventually lead to advances in stem cell therapy.

A review on automatic analysis of human embryo microscope images

Over the last 30 years the process of in vitro fertilisation (IVF) has evolved considerably, yet the efficiency of this treatment remains relatively poor. The principal challenge faced by doctors and embryologists is the identification of the embryo with the greatest potential for producing a child. Current methods of embryo viability assessment provide only a rough guide to potential. In order to improve the odds of a successful pregnancy it is typical to transfer more than one embryo to the uterus. However, this often results in multiple pregnancies (twins, triplets, etc), which are associated with significantly elevated risks of serious complications. If embryo viability could be assessed more accurately, it would be possible to transfer fewer embryos without negatively impacting IVF pregnancy rates. In order to assist with the identification of viable embryos, several scoring systems based on morphological criteria have been developed. However, these mostly rely on a subjective visual analysis. Automated assessment of morphological features offers the possibility of more accurate quantification of key embryo characteristics and elimination of inter- and intra-observer variation. In this paper, we describe the main embryo scoring systems currently in use and review related works on embryo image analysis that could lead to an automatic and precise grading of embryo quality. We summarise achievements, discuss challenges ahead, and point to some possible future directions in this research field.

A frequency based encoding technique for transformation of categorical variables in mixed IVF dataset

Implantation prediction of in-vitro fertilization (IVF) embryos is critical for the success of the treatment. In this study, Support Vector Machine (SVM) method has been used on an original IVF dataset for classification of embryos according to implantation potentials. The dataset we analyzed includes both categorical and continuous feature values. Transformation of categorical variables into numeric attributes is an important pre-processing stage for SVM affecting the performance of the classification. We have proposed a frequency based encoding technique for transformation of categorical variables. Experimental results revealed that, the proposed technique significantly improved the performance of IVF implantation prediction in terms of Area Under ROC curve (0.712+/-0.032) compared to common binary encoding and expert judgement based transformation methods (0.676+/-0.033 and 0.696 +/- 0.024, respectively).