Around one in seven couples have trouble conceiving, which means there is a high demand for solutions such as in vitro fertilization, also known as IVF. This process involves fertilizing and developing embryos in the laboratory and then selecting a few to implant into the womb of the patient. IVF, however, only has a 30% success rate, is expensive and can be both mentally and physically taxing for patients. Selecting the right embryos to implant is therefore extremely important, as this increases the chance of success, minimizes complications and ensures the baby will be healthy.

Currently the tools available for making this decision are limited, highly subjective, time-consuming, and often extremely expensive. As a result, embryologists often rely on their experience and observational skills when choosing which embryos to implant, which can lead to a lot of variability. An automated system based on artificial intelligence (AI) could therefore improve IVF success rates by assisting embryologists with this decision and ensuring more consistent results. The AI system could learn how embryos develop over time and then uses this information to select the best embryos to implant from just a single image. This would offer a cheaper alternative to current analysis tools that are only available at the most expensive IVF clinics.

Now, Bormann, Kanakasabapathy, Thirumalaraj et al. have developed an AI system for IVF based on thousands of images of embryos. Using individual images, the system selected embryos of a comparable quality to those selected by a human specialist. It also showed a greater ability to identify embryos that will lead to successful implantation. Indeed, the software outperformed 15 embryologists from five different centers across the United States in detecting which embryos were most likely to implant out of a group of high-quality embryos with few visible differences.

Artificial intelligence has many potential applications to support expert clinical decision-making. Systems like these could improve success, reduce errors and lead to faster, cheaper and more accessible results. Beyond immediate IVF applications, this system could also be used in research and industry to help understand differences in embryo quality.

Assisted reproductive technologies (ART) such as in vitro fertilization (IVF), while a solution to many infertile couples have been inefficient with an average success rate of approximately 30% reported in 2015 in the US (CDC, 2015). IVF is also an expensive solution costing patients well over $10,000 out-of-pocket for each ART cycle in the US with many patients requiring multiple cycles to achieve successful pregnancy (CDC, 2015; Birenbaum-Carmeli, 2004; Toner, 2002). Although multiple factors such as maternal age, medical diagnosis, gamete and embryo quality, and endometrium receptivity determine the success of ART cycles, the challenge of non-invasive selection of the highest available quality from a patient’s cohort of embryos (top-quality embryo) for transfer remains as one of the most important factors in achieving successful ART outcomes (Vaegter et al., 2017; Barash et al., 2017; Conaghan et al., 2013; Wong et al., 2013; Racowsky et al., 2015; Filho et al., 2010; Machtinger and Racowsky, 2013; Demko et al., 2016; Einarsson et al., 2017; Hill et al., 1989; Erenus et al., 1991; Paulson et al., 1990; Osman et al., 2015).

Traditional methods of embryo selection rely on visual embryo morphological assessment and are highly practice-dependent and subjective (Storr et al., 2017; Baxter Bendus et al., 2006; Paternot et al., 2009). Fully automated assessments of embryos are challenging owing to the complexity of embryo morphologies. Emulating the skill of highly trained embryologists in efficient embryo assessment in a fully automated system is a major challenge in all of the previous work done in computer-aided assessments of embryos due to focus on measuring specific expert-defined parameters such as zona pellucida thickness variation, number of blastomeres, degree of cell symmetry and cytoplasmic fragmentation, etc. (Rocha et al., 2017a; Rocha et al., 2017b).

Machine learning is loosely defined as a computer program that learns a given task over time through experience and improves itself to achieve the best possible task performance. In the past decade, advances in hardware compute performance and machine learning techniques have significantly improved their applicability in real-world medical and non-medical problems. Recently, machine learning has been proposed as a solution for automated analysis of embryo morphologies (Rocha et al., 2017b; Bormann et al., 2020; Dimitriadis et al., 2019; Thirumalaraju et al., 2019; Khosravi et al., 2019; Kanakasabapathy et al., 2019a). This work makes use of a deep convolutional neural network (CNN), a representation learning technique, that has been proven to be effective in image classification tasks. Unlike most prior computer-aided algorithms, including some techniques of machine learning used for embryo assessment, the reported CNN architecture allows automated embryo feature selection and analysis at the pixel level without any interference by an embryologist (Rocha et al., 2017a; Rocha et al., 2017b). Such networks do not depend on human-specified features and can develop an ability to evaluate embryos categorically through iterative learning from thousands of examples. The use of deep-learning in IVF has also been explored; however, these recent neural network-based approaches have focused on either classifying embryos based on morphological quality and were not evaluated for transfer outcomes, or were developed with the use of time-lapse series of images toward the evaluation of implantation (Khosravi et al., 2019; Tran et al., 2019). It is important to emphasize here that most fertility centers do not possess time-lapse imaging hardware even in the United States of America (Dolinko et al., 2017). The lack of availability of such hardware limits an otherwise promising technology mostly to resource-rich settings and fail to improve quality of and access to care in resource-constrained settings where such advances are sorely needed (Wahl et al., 2018; Hosny and Aerts, 2019). Furthermore, in current clinical practice, embryos with the highest morphological grades (top-quality) are the first to be transferred, however, clinically these decisions are performed manually, even with time-lapse imaging systems. The development of networks that can measure an embryo’s potential for implantation and help in rank ordering embryos in a patient embryo cohort for transfer have utility in virtually all fertility centers.

Conventionally, embryo transfers are performed at the cleavage or the blastocyst stage of development. Embryos are at the cleavage stage 2–3 days after fertilization and develop further in suitable culture conditions to reach the blastocyst stage 5–7 days after fertilization. Blastocyst embryo transfers, in particular, have been associated with better implantation rates and have helped lower the number of embryos transferred at a time (De Croo et al., 2019). Therefore, in this study, we have investigated the use of a CNN pre-trained with 1.4 million ImageNet images and transfer-learned using 2440 static human embryo images recorded at a single time-point of 113 hr post insemination (hpi) for the development of neural networks that can help identify embryos capable of implantation and for identifying the top quality embryos (Figure 1). The top-quality embryos were identified by combining a previously developed network (Xception architecture) trained to classify embryos based on its blastocyst quality with a genetic algorithm scheme (Figure 1; Thirumalaraju et al., 2020). The original neural network was trained on a hierarchical system of categorization, derived from a clinical Gardener-based grading system, to minimize data sparsity and improve overall network learning (Kanakasabapathy et al., 2019a; Thirumalaraju et al., 2020; Kanakasabapathy et al., 2019b; Esteva et al., 2017). The two major categories of non-blastocysts and blastocysts made up the inference classes, which included the training classes 1, 2, and 3, 4, 5, respectively (Figure 1). Pre-training with a large dataset of images from ImageNet honed the ability of the developed CNN to identify the shape, structure, and texture variations between morphologically complex embryos with minimal data requirements while the genetic algorithm helped in rank ordering embryos by generating unified scores (Figure 1). The developed network was evaluated using an independent test set comprising of 97 patient-embryo cohorts. Embryos of the highest quality that were selected from the patient cohorts were evaluated using known implantation outcomes.

Figure 1

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Additionally, we also investigated if the neural network can be trained to directly differentiate between embryos based on their potential for implantation (Figure 1). Our tests with patient cohorts using the algorithm does not account for the ploidy status of the embryos. Since pre-implantation genetic screened (PGS) euploid embryos are associated with higher implantation chance, we also designed a neural network to evaluate the network performance in refining the screened embryos based on their implantation potential. The evaluations using the patient cohorts tend to yield embryo selections with unknown outcomes or ploidy status, therefore, for this section of the study, we utilized a test set of 97 euploid embryos with known implantation outcomes. The CNN was trained and evaluated in identifying euploid embryos capable of implantation and the performance was compared against those of 15 embryologists from five different fertility centers across the United States of America.

Deep neural networks hold value in aiding clinical decision making and have received significant attention from the IVF community. The deep-neural network-based approach showcased here is an objective approach to one of the more subjective but important parts of a clinical IVF process-embryo selections for transfer (Bormann et al., 2020). Since over 80% of fertility clinics rely on non-time lapse imaging systems as part of their clinical processes, such neural network-based algorithms that rely purely on static single timepoint images can effectively assist in decision making (Dolinko et al., 2017). In our study, we have evaluated two neural network-based approaches for improving embryo selection.

Firstly, we have demonstrated that a deep-neural network in combination with a genetic algorithm (CNNg) can yield a continuous score that represents the quality of the embryo and that objective orders of transfer can be determined for a given set of embryos using such scores. The ranking algorithm studied here was able to consistently select embryos of the highest available morphological quality. Although the network was trained to classify embryos based on their quality, it performed well even in differentiating between embryos of the same class when combined with a genetic algorithm. The benefit of such systems is particularly evident in cases where selections made by the clinic/embryologist, although of similar grade, resulted in lower overall transfer success rates. Our networks only focused on the morphological features for embryo quality assessments due to data scarcity. The network’s learning can be compounded with data from additional timepoints, morphokinetics, and patient and cycle-specific information for more personalized IVF predictions and outcomes. Recently, Tran et al. studied the use of a deep-learning model (IVY) that can analyze whole time-lapse videos instead of specific time points for fetal heartbeat prediction (Tran et al., 2019). However, the study was flawed since embryos with unknown outcomes (non-transferred embryos) were considered as negative outcome cases, which made up most of their dataset (~90%). The heavy class bias in their dataset and improper study design severely limits any conclusions that can be drawn from the work. A major hurdle for the development of networks capable of analyzing multi-timepoint images and with additional patient-specific information is the limited availability of diversified data with known clinical outcomes. During training, the lack of availability of such data prevents the networks from effectively learning relevant outcome-associated patterns in data. The need for data scales with the complexity of the task and the number of variables introduced. While this work focuses primarily on the utility of deep-learning algorithm for embryo evaluations at 113 hpi, it is also possible to develop similar networks for embryo evaluations at different timepoints, provided that sufficient data with matched outcomes/annotations are available. We have evaluated a similar network for use with cleavage-stage embryos (70 hpi) and showed that deep-learning approaches can outperform trained embryologists in certain tasks such as embryo selection (Thirumalaraju et al., 2019; Kanakasabapathy et al., 2020).

A major concern in any clinical practice, however, is the loss of viable embryos due to system errors. Therefore, the AI-based embryo selection algorithm reported here does not make any suggestion on discarding embryos. All embryos assessed by the CNNg in the selection process may be cryopreserved as per clinical practice. Thereby, our approach will not negatively affect the cumulative pregnancy rate since viable embryos will not be lost. However, it may improve the pregnancy rate as the system may be able to improve the chance of achieving a pregnancy faster with fewer embryos transferred. Furthermore, it is important to note that in its current stage this system is intended to act only as an assistive tool for embryologists. The embryologists can include the system’s prediction to make better judgments during embryo selection. The scores provided by the algorithm are continuous, but it can also be easily modified to present its scoring results in both binary and a more categorical format.

Clinically, besides morphological features, various other important metrics and parameters are considered by embryologists at the time of decision making such as taking into account the ploidy status of the transferable embryos. PGS verified euploid embryos have been shown to possess a higher probability of successful outcome but cost a hefty premium on top of the cycle costs at most fertility centers in the United States (Drazba et al., 2014). Furthermore, for patients with two or more euploid embryos, additional assessments of embryo morphology are required to select the best embryo based on their morphology for transfer, since euploids do not inherently guarantee implantation. Thus far, to the best of our knowledge, no system, deep-learning-based or otherwise, has been shown to be capable of differentiating between euploid blastocysts based on their capacity for implantation. Euploid embryos are usually of the highest available quality and differentiating between them objectively and reliably through manual analysis can be extremely challenging. The CNN-based approach, through direct estimations of implantation potential from 113 hpi embryo morphology, outperformed trained embryologists in identifying implanting embryos from a set of PGS euploid embryos. This accomplishment exhibits the potential of artificial intelligence-based approaches to improve success rates in the IVF lab. Our observations indicated that the system performed with a significantly better agreement with the actual implantation outcome for embryos with implantation scores closer to 1 or 0 (Higher confidence). Furthermore, the comparison between the decisions made by 15 embryologists from different fertility centers in the US and the deep-neural network showcased that neural networks can outperform embryologists in identifying embryos capable of implantation. Hence, by applying the suggestions of a CNN, a trained embryologist can improve their selection of the embryo with the highest implantation potential.

Advances in artificial intelligence have fostered numerous applications that have the potential to improve standard-of-care in the different fields of medicine. While other groups have also evaluated different use cases for machine learning in assisted reproductive medicine, this approach is novel in how it used a CNN trained on a large dataset to make predictions based on static images. The approach has shown the potential of CNNs to be used in aiding embryologists to select the embryo with the highest implantation potential, especially amongst high-quality euploid embryos. Although the current retrospective study shows that these systems can perform better than highly-trained embryologists, randomized control trials are required before routine use in clinical practice is adopted.

Patients did not explicitly consent to their data being made public and access is therefore restricted. Requests for the anonymized data should be made to Charles Bormann (cbormann@partners.org) and Hadi Shafiee (hshafiee@bwh.harvard.edu). Requests will be reviewed by a data access committee, taking into account the research proposal and intended use of the data. Requestors are required to sign a data-sharing agreement to ensure patients' confidentiality is maintained prior to the release of any data.

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Author details

1. Charles L Bormann

   1. Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, United States
   2. Harvard Medical School, Boston, United States

   Contribution

   Conceptualization, Investigation, Data curation, Funding Acquisition, Project administration, Supervision, Validation, Formal analysis, Supervision, Methodology, Writing - original draft

   Contributed equally with

   Manoj Kumar Kanakasabapathy and Prudhvi Thirumalaraju

   Competing interests

   Charles L Bormann, Ph.D has a patent WO2019068073A1 pending.

2. Manoj Kumar Kanakasabapathy

   Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States

   Contribution

   Conceptualization, Investigation, Data curation, Funding Acquisition, Project administration, Supervision, Validation, Formal analysis, Supervision, Methodology, Writing - original draft

   Contributed equally with

   Charles L Bormann and Prudhvi Thirumalaraju

   Competing interests

   Manoj Kumar Kanakasabapathy has a patent WO2019068073A1 pending.

   "This ORCID iD identifies the author of this article:" 0000-0002-0756-8585

3. Prudhvi Thirumalaraju

   Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States

   Contribution

   Conceptualization, Investigation, Validation, Software, Formal analysis, Methodology

   Contributed equally with

   Charles L Bormann and Manoj Kumar Kanakasabapathy

   Competing interests

   Prudhvi Thirumalaraju has a patent WO2019068073A1 pending.

   "This ORCID iD identifies the author of this article:" 0000-0002-0573-0243

4. Raghav Gupta

   Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States

   Contribution

   Data curation, Software, Methodology, Formal analysis

   Competing interests

   No competing interests declared

5. Rohan Pooniwala

   Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States

   Contribution

   Data curation, Software, Methodology, Formal analysis

   Competing interests

   No competing interests declared

6. Hemanth Kandula

   Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States

   Contribution

   Data curation, Software, Methodology, Validation, Formal analysis

   Competing interests

   No competing interests declared

7. Eduardo Hariton

   Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, United States

   Contribution

   Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

8. Irene Souter

   1. Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, United States
   2. Harvard Medical School, Boston, United States

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

9. Irene Dimitriadis

   1. Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, United States
   2. Harvard Medical School, Boston, United States

   Contribution

   Resources, Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

10. Leslie B Ramirez

    Extend Fertility, New York, United States

    Contribution

    Resources, Supervision, Writing - review and editing

    Competing interests

    No competing interests declared

11. Carol L Curchoe

    1. San Diego Fertility Center, San Diego, United States
    2. Colorado Center for Reproductive Medicine IVF Laboratory Network, Englewood, United States

    Contribution

    Resources, Supervision, Writing - review and editing

    Competing interests

    No competing interests declared

12. Jason Swain

    Colorado Center for Reproductive Medicine IVF Laboratory Network, Englewood, United States

    Contribution

    Resources, Supervision, Writing - review and editing

    Competing interests

    No competing interests declared

13. Lynn M Boehnlein

    Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, University of Wisconsin, Madison, United States

    Contribution

    Resources, Supervision, Writing - review and editing

    Competing interests

    No competing interests declared

14. Hadi Shafiee

    1. Harvard Medical School, Boston, United States
    2. Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States

    Contribution

    Conceptualization, Resources, Supervision, Project Administration, Funding acquisition, Methodology, Writing - review and editing

    For correspondence

    HSHAFIEE@BWH.HARVARD.EDU

    Competing interests

    Dr. Shafiee has a patent WO2019068073A1 pending.

    "This ORCID iD identifies the author of this article:" 0000-0003-2240-7648

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