More than 50 million couples worldwide experience infertility. The most common treatment is in vitro fertilization (IVF). Fertility specialists collect eggs and sperm from the prospective parents. They combine the egg and sperm in a laboratory and allow the fertilized eggs to develop for five days into a multi-celled blastocyst. Then, the specialists select the healthiest blastocysts and return them to the patient's uterus.

Since 1978, more than 8 million children have been conceived through IVF. Yet, only about 30% of IVF attempts result in a successful birth. As a result, fertility patients often undergo multiple rounds of IVF, which can be expensive and emotionally draining. Several factors determine IVF success, one of which is the health of the blastocysts selected for transfer to the uterus. Specialists select the blastocysts using several criteria. But these human assessments are subjective and inconsistent in predicting which ones are most likely to result in a successful birth. Recent studies suggest artificial intelligence technology may help select blastocysts.

Liu et al. show that using artificial intelligence to assess blastocysts and fertility patient characteristics leads to more accurate predictions about which blastocysts are likely to result in a successful birth. In the experiments, the researchers trained an artificial intelligence computer program using pictures of 17,580 blastocysts with known birth outcomes and the parents' clinical characteristics. The model identified 16 parental factors associated with birth outcomes. The top 5 most predictive parental factors were maternal age, the day of blastocyst transfer to the uterus, how many eggs were present in the ovaries, the number of eggs retrieved and the thickness of the uterus lining. The program achieved the highest prediction of healthy births so far, compared to success rates listed in other studies.

Artificial intelligence-aided blastocyte selection using patient and blastocyst characteristics may improve IVF success rates and reduce the number of treatment cycles patient couples undergo. Before specialists can use artificial intelligence in their clinics, they must conduct confirmatory clinical studies that enroll patient couples to compare conventional methods and artificial intelligence.

Infertility is a global health issue, affecting more than 50 million couples worldwide (Mascarenhas et al., 2012). Since the birth of the first in vitro fertilization (IVF) child in 1978, over 8 million children were born with IVF treatment (Adamson et al., 2018). Among the various factors contributing to IVF outcomes, the quality of the blastocyst (day 5 embryo) selected for transfer is critical for the success of IVF treatment. Manual grading of blastocyst development stage, inner cell mass (ICM), and trophectoderm (TE) remains the most common method for blastocyst evaluation. While the blastocyst morphological grading is widely used in clinical practice, morphological grades of the development stage, ICM, and TE have shown limited predictive power on clinical outcomes (Seli et al., 2011; Reignier et al., 2019; Bartolacci et al., 2021; Ueno et al., 2021; Xiong et al., 2022). It is desired to identify features for accurate prediction of clinical outcomes of blastocysts.

To achieve this goal, the convolutional neural network (CNN) is expected to play a critical role. CNN is able to automatically detect discriminative features from images and has been the state-of-the-art method in various fields in medical imaging, such as lung cancer prediction (Ardila et al., 2019), breast cancer prediction (McKinney et al., 2020), and diabetic retinopathy screening (Bora et al., 2021). To apply CNN to predict the clinical outcome of a blastocyst, images of blastocysts with a known clinical outcome (e.g., pregnancy and live birth) are collected for the CNN model development. The area under the receiver operating characteristic (ROC) curve (AUC) is the most commonly used metric to evaluate and compare machine learning models on predicting clinical outcomes of blastocysts (Kragh and Karstoft, 2021a). The AUCs reported in the literature using CNN to predict clinical outcomes from blastocyst images range from 0.64 to 0.71 for pregnancy prediction (VerMilyea et al., 2020; Kragh et al., 2021b; Berntsen et al., 2022; Enatsu et al., 2022; Loewke et al., 2022), and are around 0.65 for live birth prediction (Miyagi et al., 2019; Nagaya and Ukita, 2021).

Besides using blastocyst images, attempts have also been made to use time-lapse videos for live birth prediction. These videos contain the entire development process from days 0 to 5–7. However, results in the literature show that CNN using static images achieved similar or slightly better accuracies in predicting clinical outcomes than using time-lapse videos, for instance, AUC=0.68–0.71 (VerMilyea et al., 2020; Enatsu et al., 2022; Loewke et al., 2022) versus 0.64–0.67 (Kragh et al., 2021b; Berntsen et al., 2022) for pregnancy prediction, and 0.66 (Miyagi et al., 2019) versus 0.65 (Nagaya and Ukita, 2021) for live birth prediction. A potential reason is that the redundant frames of images in time-lapse videos may work as noise causing the model to overfit and thus leading to a lower prediction accuracy (Zhu et al., 2018; Wu et al., 2021; Tao et al., 2022). Therefore, we opted to use static blastocyst images in this study.

Different from using blastocyst images alone to predict clinical outcomes, Miyagi et al., 2020 proposed to use blastocyst images together with maternal clinical features including maternal age, AMH, and BMI and reported an AUC of 0.74, the highest accuracy in literature (Miyagi et al., 2020). However, two questions remain elusive. First, the contribution of blastocyst images and the additional contribution of maternal clinical features to live birth prediction are unknown. Second, endometrium status-related features, such as endometrium thickness and pattern, are also critical factors impacting live birth outcomes (Ng et al., 2007; Bu et al., 2016; Mahutte et al., 2022), but were not considered.

In this study, we quantified the effect of blastocyst images and the combined effect of both blastocyst images and patient couple’s clinical features on live birth prediction. The live birth prediction model using only blastocyst images achieved an AUC of 0.67, which was significantly outperformed by the AUC of 0.77 achieved by the model using both blastocyst images and patient couple’s clinical features (p value<0.0001). Additionally, when endometrium status-related features (e.g., endometrium thickness and pattern) were excluded, the AUC of the model using both blastocyst images and patient couple’s clinical features significantly decreased to 0.74 (p value<0.0001), indicating that the inclusion of endometrium status-related clinical features helps improve live birth prediction accuracy. Sixteen patient couple’s clinical features were identified to be most related to live birth outcomes of blastocysts, among which maternal age, the day of blastocyst transfer, antral follicle count (AFC), retrieved oocyte number, and endometrium thickness measured before transfer are the top five features contributing to live birth prediction. Additionally, the CNN heatmaps showed that the CNN mainly focused on ICM and TE for live birth prediction, and the contribution of TE-related features was greater in the CNN trained with the inclusion of patient couple’s clinical features compared with the CNN trained with blastocyst images alone.

In this study, the individual effect of blastocyst images and the combined effect of patient couple’s clinical features for live birth prediction were quantified by comparing the AUC values of the model using only blastocyst images and the model using both blastocyst images and patient couple’s clinical features. An AUC of 0.67 was achieved with blastocyst images only while using both blastocyst images and patient couple’s clinical features led to a significantly higher AUC of 0.77 in live birth prediction. When endometrium status-related features were excluded from patient couple’s clinical features, the AUC of the live birth prediction model significantly decreased (p value=0.007) from 0.77 to 0.74, indicating the strong relevance of endometrium status-related features in live birth prediction. Sixteen patient couple’s clinical features were identified to be most related to live birth outcomes of blastocysts, among which maternal age, the day of blastocyst transfer, AFC, retrieved oocyte number, and endometrium thickness before transfer are the top five features contributing to live birth prediction.

This study was based on a comprehensive multimodal data set collected for blastocyst evaluation. The data set includes 17,580 blastocysts with known live birth outcomes, blastocyst images, and 16 patient couple’s clinical features. As shown in Figure 3, 16 patient couple’s clinical features comprehensively include maternal basal characteristics (age and BMI); hormone profiles measured after period (LH and FT4), on HCG day (PE2, P, and LH), and before transfer (E2); endometrium status-related features (endometrium thickness on HCG day and before transfer, endometrium pattern on HCG day); features related to oocytes (AFC, retrieved oocyte number); the day of blastocyst transfer; number of ovarian stimulation cycles; and paternal features (the ratio of grade A sperm after semen processing). For comparison, the data set studied by Miyagi et al., 2020 did not contain endometrium status-related features and key hormone profiles (e.g., P, E2, and LH). There are numerous IVF data sets containing over 100,000 records of clinical features and live birth outcomes (Nelson and Lawlor, 2011; McLernon et al., 2016; La Marca et al., 2021); however, there are no blastocyst images in these data sets, and thus, these data sets cannot be used for building models to evaluate blastocysts from their images.

To handle the multimodal data set, our proposed model was designed to integrate two modules including a CNN and an MLP to enable the model to simultaneously consider images and numerical clinical features for blastocyst evaluation. The large and comprehensive multimodal data set and the proposed CNN+MLP model resulted in the highest AUC value of 0.77 ever reported thus far for predicting live birth outcomes of blastocysts. They also enabled us to quantify the predictive power of each feature in predicting the live birth outcomes of blastocysts.

The blastocyst grading system introduced in 1999 (Gardner and Schoolcraft, 1999; Gardner, 1999) remains the most common method used by embryologists to evaluate blastocyst quality although the morphological grades of blastocyst development stage, ICM and TE have limited predictive power on live birth outcomes (e.g., AUC=0.58–0.61 for live birth prediction reported by Reignier et al., 2019; Bartolacci et al., 2021; Xiong et al., 2022). Since CNN became a state-of-the-art method for image-based classification, many attempts have been made to apply the CNN to blastocyst evaluation for predicting clinical outcomes (e.g., VerMilyea et al., 2020; Kragh et al., 2021b; Berntsen et al., 2022; Enatsu et al., 2022; Loewke et al., 2022; Miyagi et al., 2019; Nagaya and Ukita, 2021). Among these, the AUC values reported in the literature using blastocyst images only were around 0.65 for live birth prediction (Miyagi et al., 2019; Nagaya and Ukita, 2021). Similarly, we achieved an AUC of 0.67 (see Figure 2). Compared with the AUC of 0.58–0.61 reported in the literature using Gardner grades for live birth prediction, these results confirmed that CNN can achieve a higher prediction accuracy. As shown in Figure 4, the CNN mainly focuses on ICM and TE. Different from the Gardner-defined TE grade on the number of TE cells and the cohesiveness of TE cells as a whole, the CNN tends to focus on specific TE clusters. Understanding the heatmaps further requires more investigations.

Miyagi et al., 2020 used both blastocyst images and maternal clinical features (age, AMH, and BMI) to predict live birth outcomes and achieved an AUC of 0.74 (Miyagi et al., 2020). The additional contribution from the three maternal clinical features was not clear since no AUC was reported by using blastocyst images alone. Furthermore, despite their importance in pregnancy and live birth, endometrium status-related features were not considered in their work. Therefore, our study used a comprehensive data set and quantitatively compared the AUC values of live birth prediction using only blastocyst images versus using both blastocyst images and patient couple’s clinical features. We also quantified the usefulness of endometrium status-related features in working with blastocyst images to improve live birth prediction. Furthermore, we revealed that hormone profiles such as E2, LH, P, and FT4, features related to oocyte retrieval such as AFC and number of oocytes retrieved, and the ratio of grade A sperm after processing representing semen quality are able to work with blastocyst images to further improve the live birth prediction accuracy. Note that in this study, only the total testosterone (T) was analyzed, and free T or bioavailable T was not available for clinical feature analysis (see Supplementary file 1). This may cause potential bias in determining the significance of testosterone as a predictor of live birth.

Another finding of this study, by comparing the heatmaps of the CNN trained without and with the inclusion of patient couple’s clinical features, is that the weights of TE-related features increased (see Figure 4). A potential reason may be that TE and the endometrium status-related features (e.g., endometrium thickness and pattern) play critical roles when a blastocyst initiates implantation, and a positive live birth outcome is not possible without the success of this implantation process (Ahlström et al., 2011; Hill et al., 2013; Chen et al., 2014; Bakkensen et al., 2019).

In conclusion, in this retrospective study involving 17,580 blastocysts with known live birth outcomes, blastocyst images and 16 patient couple’s clinical features, we built a live birth prediction model based on multimodal blastocyst evaluation using both blastocyst images and patient couple’s clinical features. We quantified the individual effect of blastocyst images and the combined effect of patient couple’s clinical features on live birth prediction. Results demonstrated that using both blastocyst images and patient couple’s clinical features can significantly improve live birth prediction than using blastocyst images alone.

The proposed live birth prediction model improves the evaluation of a blastocyst in terms of its live birth potential for best blastocyst selection from multiple blastocysts of a patient. The next step is to validate the model’s prediction accuracy using prospectively collected data and verify its effectiveness in blastocyst selection via a randomized controlled trial (RCT). Patients enrolled in the RCT will be split into the study group and the control group (1:1 ratio). In the study group, the model selects a top blastocyst having the highest probability of live birth for transfer, and in the control group, embryologists select a top blastocyst based on their routine morphological grading for transfer. Live birth outcomes of both groups will be tracked and compared.

All processed data and code needed to reproduce the findings of the study are made openly available in deidentified form. This can be found in https://github.com/robotVisionHang/LiveBirthPrediction_Data_Code (copy archived at Liu et al., 2023), and attached to this manuscript. All codes and software used to analyze the data can also be accessed through the link. Due to data privacy regulations of patient data, raw data cannot be publicly shared. Interested researchers are welcome to contact the corresponding author with a concise project proposal indicating aims of using the data and how they will use the data. The project proposal will be firstly assessed by Prof. Yu Sun, Prof. Ge Lin, and then by the Ethics Committee of the Reproductive and Genetic Hospital of CITIC-Xiangya. There are no restrictions on who can access the data.

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Author details

1. Hang Liu

   Department of Mechanical Engineering, University of Toronto, Toronto, Canada

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Zhuoran Zhang and Yifan Gu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7948-4236

2. Zhuoran Zhang

   School of Science and Engineering, The Chinese University of Hong Kong-Shenzhen, Shenzhen, China

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Hang Liu and Yifan Gu

   Competing interests

   No competing interests declared

3. Yifan Gu

   1. Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, China
   2. Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, China

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Hang Liu and Zhuoran Zhang

   Competing interests

   No competing interests declared

4. Changsheng Dai

   Department of Mechanical Engineering, University of Toronto, Toronto, Canada

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Guanqiao Shan

   Department of Mechanical Engineering, University of Toronto, Toronto, Canada

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2570-769X

6. Haocong Song

   Department of Mechanical Engineering, University of Toronto, Toronto, Canada

   Contribution

   Data curation, Software, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Daniel Li

   Department of Electrical and Computer Engineering, Toronto, Canada

   Contribution

   Data curation, Software, Validation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Wenyuan Chen

   Department of Mechanical Engineering, University of Toronto, Toronto, Canada

   Contribution

   Conceptualization, Resources, Data curation, Supervision, Validation, Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

9. Ge Lin

   1. Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, China
   2. Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, China
   3. Key Laboratory of Reproductive and Stem Cell Engineering, National Health and Family Planning Commission, Changsha, China
   4. National Engineering Research Center of Human Stem Cells, Changsha, China

   Contribution

   Conceptualization, Resources, Supervision, Project administration, Writing – review and editing

   For correspondence

   linggf@hotmail.com

   Competing interests

   No competing interests declared

10. Yu Sun

    1. Department of Mechanical Engineering, University of Toronto, Toronto, Canada
    2. Department of Electrical and Computer Engineering, Toronto, Canada
    3. Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
    4. Department of Computer Science, University of Toronto, Toronto, Canada

    Contribution

    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    sun@mie.utoronto.ca

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7895-0741

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