Application of Deep Learning Automation Based on Time-lapse Imaging to Jointly Assess Embryo Development to Improve Pregnancy Outcome of Single Blastocyst Transfer
Conditions: Reproductive Medicine
Sponsor: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

Clinical Research Protocol: Application of deep learning automation based on 
Time-lapse imaging to jointly assess embryo development to improve 
pregnancy outcome of single blastocyst transfer 
  
 
Subject source: the Affliated Drum Tower Hospital of Nanjing University Medical School 
Responsible person: Wang Shanshan 
Department: Reproductive Medicine Department 
Contact person: Wang Shanshan 
Contact number: 13814549922 
The research start time: December 30, 2022-June 15, 2024 
Version number: AI-2021-01 
 
The date of the document: July 01, 2021 
 
 
 

Summary of the case 
Project Name 
A clinical study based on Time-lapse imaging application of deep learning automated 
joint evaluation of embryo development to improve the outcome of single blastocyst 
transplantation pregnancy 
Purpose of 
the study 
Based on Time-lapse imaging application of deep learning, this project explores the 
automatic evaluation model of different developmental stages of embryos, and 
establishes an automated joint evaluation system for the whole process of embryonic 
development. In addition, the patients with single blastocyst transplantation are 
prospectively collected, and the pregnancy outcome of the patients is followed up by 
TLI+AI evaluation and morphological evaluation, and the pregnancy outcome of the 
two groups is compared.The project aims to explore key features that play a role in the 
evaluation of AI embryos to provide a theoretical basis for further screening of high-
quality embryos, thereby increasing the implantation rate of single embryo transfer 
patients, reducing multiple pregnancies and ensuring the safety of mothers and children. 
Study Design 
This study is an observational prospective study after a retrospective analysis. It is 
a single-center study without randomization or blindness.In the early stage, 1000 
patients are collected from three periods of embryo culture through Time-lapse to 
establish an automated joint evaluation system for the whole process of embryo 
development. At the later stage, the patients are divided into two groups: TLI+AI 
assessment group and morphological assessment group. 100 patients with 5D single 
blastocyst transplantation are carried out to follow up the pregnancy outcome. 

Total number 
of cases 
1000 (retrospective study)+100 (prospective study) 
Case 
selection 
Criteria for inclusion 
1) < 40 years of age; 
2) Routine IVF cycle; 
3) Period number ≤ 2; 
4) The number of eggs harvested was 5-15. 
5) follicle stimulating hormone(FSH) ≤ 12 IU/L on the third day of menstruation; 
6) Patients with more than 3 high-quality embryos per Day 3 and feasible single 
blastocyst transplantation per Day 5; 
7) Patient factors affecting embryo implantation such as endometrial factors. 

 
 
Exclusion criteria 
1) Preimplantation Genetic Testing (PGT) is needed for male infertility, ovulation cycle 
and chromosome abnormality. 
2) Pictures of unformed or non-available blastocysts; 
3) Incomplete or unclear images were collected during prokaryotic, mitotic and 
blastocyst phases, which affected the AI evaluation. 
Treatment 
plan 
Through conventional ovulation induction and time-lapse culture, the embryo 
development was observed dynamically, pictures were collected to establish an 
automated joint evaluation system for the whole process of embryo development, and 
the embryos were selected for daily 5 single blastocyst transplantation. 
Evaluation of 
curative 
effect 
Efficacy evaluation indicators (primary efficacy indicators and secondary 
efficacy indicators) 
Precision rate, embryo planting rate 
Safety evaluation index 
The harmonic mean of accuracy rate and recall rate, abortion rate 
Method of 
statistics 
(1) Statistical analysis of the evaluation system with accuracy and recall rate; 
(2) χ2 test was used to compare the rates among control groups, and p < 0.05 was 
considered statistically significant. 
Duration of 
study 
December 30, 2022-June 15, 2024 
 

 
Directory 
 
1. Research background....................................................................................................................... 1 
2. Research objectives ......................................................................................................................... 5 
3. Research design types, principles and test procedures .................................................................... 6 
4. Case selection .................................................................................................................................. 8 
5. Research methods and technical routes ........................................................................................... 9 
6. Observation items and testing time ................................................................................................. 9 
7. Evaluation criteria for curative effect ............................................................................................ 10 
8. Observation of adverse events ....................................................................................................... 11 
9. Quality control and quality assurance of the research  ................................................................. 11 
10. Data security inspection .............................................................................................................. 11 
11.Statistical processing .................................................................................................................... 11 
12. Ethics of clinical research ............................................................................................................ 12 
13. Collection and storage of samples/specimens ............................................................................. 12 
14. References ................................................................................................................................... 12 
 
 
 
 
 
 
 

 
 
 
List of acronyms 
English abbreviation 
Full English name 
Full Chinese name 
Assisted Reproductive Technology 
ART 
Assisted reproductive 
technology 
Single embryo transfer 
SET 
Single embryo transfer 
Time-Lapse imaging 
TLI 
Time difference imaging 
technique 
Artificial Intelligence 
AI 
Artificial Intelligence 
Deep learning 
- 
Deep learning 

 
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1. Background 
    Embryo selection is the core of single embryo transfer and an important factor for the success 
of assisted reproductive therapy 
Multiple pregnancies are one of the most common and serious complications of Assisted 
Reproductive Technology (ART).Foreign evidence-based medical evidence shows that the risk of a twin 
pregnancy is 20 times that of a natural pregnancy, and the risk of a pregnancy with three or more children 
rises to 400 times. Multiple pregnancies may be related to the woman's age, previous reproductive history, 
ovulation induction drugs, fertilization methods and transplant days, but the most direct influencing factors 
are the rate of embryo implantation and the number of transplanted embryos. With the extensive 
application of ART technology, usually two or more embryos are transferred to ensure the pregnancy 
outcome, resulting in the multiple pregnancy rate in recent 30 years as high as 30-40%. With the increase 
of the number of transplanted embryos, the maternal cardiac output, oxygen consumption, basal metabolic 
rate, incidence of "physiological anemia" and postpartum hemorrhage significantly increased. For fetuses, 
the excessive uterine tension of twins increases the risk of premature, low birth weight infants and 
perinatal death, and the family economic burden also increases correspondingly. 
Numerous studies have shown that the Single embryo transfer (SET) strategy has become one of the 
most effective ways to reduce multiple pregnancies in the field of assisted reproduction.  Although both 
doctors and patients are aware of the superiority of SET in eugenics and procreation, they are concerned 
about reducing the number of embryos transferred and reducing the pregnancy rate, which is the main 
reason that makes it difficult to popularize SET in China. 
In order to reduce multiple pregnancies, it is imperative to implement SET, and embryo optimization 
is the guarantee of single embryo transfer pregnancy. 
It is challenging to optimize embryo selection based on morphological parameters to improve 
pregnancy outcome of single embryo transfer 
Static morphological evaluation is the most commonly used method for embryo evaluation. It follows 
the ESHRE consensus to observe the embryo morphology at several fixed time points, which has the 
advantages of simple operation, low cost and no trauma to the embryo. Since the embryo itself is a 
complex three-dimensional structure, the focal plane needs to be adjusted repeatedly to obtain more 

 
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comprehensive information during observation.For embryologists, it takes years of training to accurately 
and quickly judge these quantitative indicators under the microscope.On the other hand, errors brought by 
subjective factors or errors caused by fatigue cannot be avoided.In addition, a great deal of useful 
information about the development of the embryo is lost because of the timing of the assessment.The 
above defects of artificial observation not only restrict the scale development of embryo laboratory in 
terms of human resources, but also affect the further improvement of implantation rate, which is 
recognized as a technical difficulty to be improved in the field of assisted reproduction. 
Embryonic development is a dynamic process.Time-Lapse imaging (TLI) is an advanced method for 
dynamic evaluation of embryo morphology, overcomes the disadvantages of static morphological 
evaluation, which not only ensures the stability of embryo culture microenvironment, but also 
continuously obtains a series of data of biological morphological changes during embryo development, 
providing more comprehensive and effective dynamic information for embryo evaluation.However, TLI 
technique is still limited to morphological evaluation, which can not accurately reflect the quality and 
developmental potential of embryos.Recent studies have shown that the TLI system currently in use and 
the accompanying embryo quality assessment software do not significantly improve the success rate.  The 
reason may be that the current morphodynamic algorithm does not consider the influence of many external 
variables on the algorithm. 
Artificial intelligence (AI) combined visualization technology opens up new ideas for selecting 
embryos 
In order to optimize the embryo assessment process and make clinical work efficient and fast, 
embryologists have long been collaborating with computer scientists to use computer technology to assist 
embryo quality assessment.Artificial Intelligence (AI). The branch of computer science that attempts to 
understand the nature of intelligence and produce new kinds of intelligent machines that react in a similar 
way to human intelligence.With the development of AI technology and computer processing power, more 
and more researchers are combining artificial intelligence with deep learning (deep learning) technology 
applied to the medical field.Deep learning is a new field in the research of machine learning. Its motivation 
is to build and simulate the neural network of human brain for analysis and learning. It mimics the 
mechanism of human brain and forms more abstract high-level representation attribute categories or 
features by combining low-level features to discover distributed feature representation of data, mainly for 

 
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image, sound, text and other types of information.Convolutional neural network is a kind of depth 
feedforward artificial neural network. In recent years, it has been successfully applied to image 
recognition.Convolutional neural networks include one dimensional, two dimensional and three 
dimensional convolutional neural networks.Two-dimensional convolutional neural networks are mainly 
used in medical image data recognition, such as detection of diabetic retinopathy 18 kinds of skin diseases. 
And diagnosing diseases. In the field of reproduction, the measurement, evaluation and analysis of 
embryos have also been preliminarily explored. 
Deep learning-based computer vision technology has the following significant advantages: 1) 
automatic end-to-end processing.For example, for the scoring of embryos, you can directly input an 
original picture to get the scoring result, the intermediate process does not need manual assistance;2) The 
model has strong generalization ability.When the number of learning samples reaches a certain level and 
the diversity is enough to reflect the actual scene, the trained artificial intelligence model can effectively 
adapt to different image formats (including photos taken by different brands of microscope equipment, or 
photos with different focal segments) and different image quality.3) It does not depend on artificially 
specified characteristics.Deep learning technology carries out autonomous learning and feature extraction 
on the basis of labeled samples, without the need to specify features of medical or visual significance in 
advance.So.Some unknown or hidden features can be found to improve accuracy.Many scientists at home 
and abroad have applied deep learning to each stage of embryonic development. 
In the prokaryotic phase, Beuchat A uses traditional image algorithms to semi-automatically analyze 
prokaryotic images and provide accurate morphological measurement parameters.Zhao et al. use the 
segmentation system based on convolutional neural network to segment the images in the prokaryotic 
stage 
and 
achieve 
consistency 
(98 
± 
1.02%). 
.Vikrant 
Reddy 
used 
CNN 
network 
(VGG16,AlexNet,ResNet-18) to classify the biprokaryotes (2PN), 3PN and germinal vesicle (GV) with 
an accuracy of 88% .In 2015, Aisha Khan used CNN +CRF t1, t4, t4+ (265 embryos) to predict the number 
of cells with an accuracy of 94% .In 2019, Tingfung Lau cut out the original cell region on 1309 videos, 
and used ResNet 50 + LSTM tsatrt tPnf t2 t3 t4 for division stage classification, with an average accuracy 
of 88.28% .However, the accuracy of automatic labeling after the 4-cell stage was limited.In China, 
Zhong Junjie et al. used the score of prokaryotic stage combined with cleavage stage to predict the 
developmental potential of embryos, and found that the comprehensive score could improve IVF 

 
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implantation rate and pregnancy rate . 
Single blastocyst transplantation has become the mainstream of single embryo transplantation 
strategy due to the advantages of high implantation rate. Blastocyst quality is mainly judged based on 
quantitative indicators such as the degree of sac expansion, trophoblast and inner cell mass quality .Pegah 
Khosravi et al. from the University of Conor took the lead in introducing deep learning into the field of 
embryo assessment. Based on the GoogLeNet Inception-V1 model, they trained 12,000 pictures of 1600 
blastocysts. The classification accuracy was up to 96.94%, much higher than previous results. And far 
more accurate than embryologists.In the comparative analysis experiment, when five embryologists 
independently rated 394 blastocyst images (based on the same Gardner scoring method), only 89 embryos 
were identical .Jose Celso Rocha et al. used computer vision (CV) algorithm to extract relevant parameters 
from blastocyst pictures, and then modeled and classified them based on ANN neural network, achieving 
76.4% accuracy .In China, some scholars have applied deep learning technology in the field of assisted 
reproduction.B. Huang et al. used multi-task deep learning with dynamic programming to classify the 
developmental stages of embryos in time-lapse photography videos, providing valuable information for 
embryologists .Taiwan embryologist Lan Huang et al. classified blastocyst development grade, inner cell 
mass and trophoblast quality, and combined the three classification results, the final embryo classification 
accuracy was 75.36% , the accuracy needs to be improved.In the past two years, it has been demonstrated 
that AI assessment can ensure the consistency of embryo quality assessment, and is better than 
embryologists . 
In existing clinical studies, deep learning technology is mainly applied to automatically evaluate a 
single period of embryonic development, while comprehensive and continuous AI evaluation based on the 
dynamic process of embryonic development is rarely mentioned.This study is the first to propose the 
application of deep learning to the whole process of embryonic development, and to evaluate the embryo 
comprehensively at various developmental stages.In addition, although deep learning technology has 
made some progress in the evaluation of embryonic images, the black box mechanism is still a major 
obstacle to clinical application due to the lack of research on its classification mechanism.In 2017, 
Selvaraju recently proposed the Grad-CAM(Class Activation Map) method, that is, the weighted 
coefficients of the features were obtained by back propagation, the weighted weights of each feature map 
were obtained by the gradient return method, and then superimposed for visualization.Its application scope 

 
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is wider, and it can be directly used for all kinds of convolutional neural network structures without 
retraining the model, and the visualization results are more accurate .This research team has made 
preliminary progress in the multi-focal length blastocyst quality evaluation model and visualization, in 
which the evaluation index AUC reached 0.936 And conducted in-depth communication and discussion 
with global experts in the field of reproduction at the ESHRE meeting in Europe 2020.Recently, based 
on multi-focal length blastomere tracking technology, our research team has made a breakthrough 
in the identification of 8 cells and above in the division stage, and the identification accuracy of 
blastomere has reached more than 85%.The evaluation index of blastocyst region segmentation 
based on semantic segmentation method was also close to 90%, which reached the highest level of 
similar recognition results in the world. 
In conclusion, we believe that the establishment of AI-time lapse automatic system for embryo 
development evaluation not only overcomes the defects of traditional computer graphics algorithms, but 
also ensures the consistency of embryo evaluation.The quality assessment of the whole process based on 
the dynamic development of embryos can overcome the defects of single period quality assessment and 
improve the accuracy of assessment.The wide implementation and popularization of this technique can 
improve the evaluation accuracy, and at the same time, it can effectively promote the single blastocyst 
transplantation technology, reduce the rate of multiple pregnancies, protect the safety of mothers and 
children, reduce the personal and social economic burden, and create a good atmosphere for the 
establishment of a harmonious society. 
 
2. Research objectives 
2.1 Main objective: To continuously and automatically evaluate and track the development 
process of embryos using the latest deep learning technology.In this way, the human input of the 
laboratory can be reduced, and the objectivity, stability and accuracy of embryo evaluation can be 
improved, so as to improve the implantation rate of single embryo transfer patients, reduce multiple 
pregnancies, and ensure the safety of mother and child. 
2.2 Secondary purpose: Based on the images taken by TLI system, explore new indexes and 
methods of embryo quality assessment by using the latest deep learning algorithm and combining 

 
6 
embryo morphology method and embryo dynamics method. 
 
3. Research design types, principles and test procedures 
3.1 Research design 
This study is an observational prospective study. It is a single-center study without 
randomization or blind grouping.In order to establish an automated joint evaluation system for the 
whole process of embryo development, it is necessary to collect images of 1000 patients in the 
prokaryotic, mitotic and blastocyst stages of single blastocyst transplantation in IVF cycles. 
According to the TLI+AI assessment and morphological assessment, at least 100 cases are required 
for fresh cycle 5 a Day single blastocyst transplantation.At present, the pregnancy rate of single 
blastocyst transplantation is basically maintained at 60%. Through this study, the pregnancy rate is 
expected to be increased to 80%. The test standard α is 0.05. 
    3.2 Research steps 
(1) Establish an AI-time lapse automated combined embryo evaluation system to improve the 
accuracy of embryo evaluation 
1) According to the entry and expulsion criteria, 1000 patients were enrolled and Time-lapse 
closed-loop culture embryo images of prokaryotic phase, mitotic phase and blastocyst phase were 
collected. 
2) To establish the inclusion criteria for training samples, screen and sort out embryo picture 
samples; 
3) Five embryologists annotated the image samples to generate training sample sets and test 
sample sets; 
4) Convolutional neural network based on deep learning was used to build the prokaryotic 
detection network, the splintering period counting network, the splintering period segmentation 
network and the blastocyst region segmentation network, and train the detection models in each 
period respectively; 

 
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5) The convolutional neural network classification model is built to classify the embryo quality, 
and the AI-time lapse automatic joint embryo evaluation system is established by using the five-fold 
crossover method to train and verify the model. 
(2) Debug the generalization ability of the model and improve the evaluation function of 
embryonic development 
1) Test the generalization ability of the AI evaluation model by using embryo picture data, and 
visualize the features dependent on the model classification. 
2) Comparative analysis of AI evaluation and manual evaluation, optimization of model with 
transfer learning method, and improvement of model accuracy. 
(3) To verify the automated combined embryo assessment system to improve the pregnancy 
outcome of single blastocyst transplantation 
1) The enrolled subjects undergoing monoblastocyst transplantation were screened, and 
informed consent was signed with the patients; 
2) Routine ovulation, fertilization and embryo culture in time lapse system for dynamic 
observation; 
3) The optimal blastocysts were selected by AI-time lapse system for combined evaluation and 
manual evaluation, and 100 cases with fresh cycle of 5 blastocysts per Day were prospective 
performed. 
4) The pregnancy outcomes of the two groups, including the implantation rate, abortion rate and 
the rate of carrying babies home, were followed up for statistical analysis. 
3.3 Indications 
The patient meets the indications for IVF/embryo transfer therapy, as shown in the admission 
criteria 
3.4 Study endpoint 
Data collection and analysis were completed in 100 prospective 5 Day single blastocyst 

 
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transplantation patients 
 
4. Case selection 
4.1 Number of cases 
A total of 1000 patients were collected in Time-lapse closed-culture embryos for three 
periods to establish an AI-time lapse combined evaluation model, and 100 patients with 
monoblastocyst transplantation were prospectively carried out.All samples were approved by the 
Medical Ethics Committee and signed by the patient with informed consent. 
4.2 Inclusion criteria 
1) < 40 years old; 
2) Routine IVF cycle; 
3) Period number ≤ 2; 
4) The number of eggs collected was 5-15; 
5) FSH ≤ 12 IU/L on the third day of menstruation; 
6) Patients with more than 3 high-quality embryos per Day and feasible 5 single blastocyst 
transplantation per Day; 
7) Patient factors affecting embryo implantation such as endometrial factors. 
4.3 Exclusion criteria 
1) PGT should be performed due to male infertility, ovulation cycle and chromosome 
abnormalities; 
2) Pictures of blastocysts not formed or not available; 
3) Incomplete or unclear images were collected during prokaryotic, mitotic and blastocyst 
phases, which affected the AI evaluation. 
4.4 Eliminate criteria 

 
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Patients who refused to undergo 5 Day single blastocyst transplantation 
 
4.5 End research criteria 
Patients may terminate the study due to the following reasons: 1) imaging system failure before 
the end of culture;2) No embryos could be transplanted after 5 days of culture;3) There is no valid 
embryo picture for AI analysis;4) The patient cancels the transplant;5) Subjects withdraw from the 
study at their own request;6) Subjects did not follow the study protocol. 
 
    5. Research methods and technical routes 
    5.1 Randomization into groups 
This study was not a randomized controlled study 
5.2 Intervention/study drug name and specification 
None 
5.3 Study intervention options 
None 
    5.4 Combine drugs 
None 
5.5 Research related laboratory tests 
None 
5.6 Study relevant imaging tests 
None 
 
6. Observation items and testing time 

 
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1) The morphological parameters required by the embryos were observed from Day 1-5 during 
embryo culture; 
2) Images of prokaryotic stage, cleavage stage and blastocyst stage were collected for the 
establishment of automated embryo evaluation models at different developmental stages; 
3) Routine luteal support after embryo transfer, positive blood HCG detected 14 days after 
transplantation, and pulsation of gestational sac and original cardiac tube detected by B-ultrasound at 
4-6 weeks were considered as clinical pregnancy until fetal delivery. 
 
7. Evaluation criteria for curative effect 
In this study, deep learning method was used to evaluate the blastocyst quality based on neural 
network model and manual evaluation of blastocyst quality 
In order to improve the accuracy and effectiveness of prediction, the optimal parameter range is 
adjusted timely according to the results. 
The validity evaluation index in the study is the accuracy rate, which represents how many of 
the predicted positive samples are really positive samples.There are two possible predictions, one is 
to predict the positive class as positive class (TP), the other is to predict the negative class as positive 
class (FP), the accuracy rate is P=TP/(TP+FP), the value is not less than 0.9.The safety evaluation 
index is the harmonic average of accuracy rate and recall rate F=2*P*R/(R+P), that is, positive 
samples are not expected to be identified as negative samples, or negative samples are identified as 
positive samples, and the value is not less than 0.9. 
In the pregnancy outcome, the clinical results of single embryo transfer were counted, and the 
data were statistically analyzed by the rate of embryo implantation, the rate of carrying babies home 
and the abortion rate of safety indicators.The pregnancy rate of single embryo transfer cycle was 
compared with that of conventional morphology.According to the results, adjust the optimal 
parameter range in time to improve the accuracy and effectiveness of the prediction. 
 

 
11 
8. Observation of adverse events 
None 
 
9. Quality control and quality assurance of the research (Determine whether this part is needed 
according to the specific situation of the project) 
Embryo culture and transfer within the laboratory are performed in accordance with the standard 
SOP procedure, and the patient is given full informed consent to improve the subject's compliance. 
10. Data security inspection 
None 
 
11.Statistical processing 
11.1 Statistical methods include 
Through deep learning method, a 16-layer convolutional neural network structure was built, 
which was used to extract the features of embryo images in different periods, and then the neural 
network model was used for statistical analysis.SPSS16.0 software was used for statistical analysis. 
Through the clinical results of single embryo transfer, the data were statistically analyzed with the embryo 
implantation rate, abortion rate and baby carrying home rate as indicators.The pregnancy rate of single 
embryo transfer cycle was compared with that of conventional morphology.According to the results, 
adjust the optimal parameter range in time to improve the accuracy and effectiveness of the 
prediction.To obey the normal distribution of measurement data using mean + / - standard deviation (x ̅+ 
s) was described, using samples t test, inspection rate comparing with x ^ 2.P < 0.05 was considered 
statistically significant. 
11.2 Sample size calculation process 
At present, the pregnancy rate of single blastocyst transplantation is basically maintained at 60%. 
Through this study, the pregnancy rate is expected to be increased to 80%. The test standard α is 0.05. 

 
12 
12. Ethics of clinical research 
Clinical research will follow the relevant regulations such as the Declaration of Helsinki of the 
World Medical Congress.Clinical studies are conducted only after the protocol is approved by the 
Ethics Committee prior to the start of the study.Before each subject is enrolled in this study, the 
investigator is responsible to give a complete and comprehensive introduction of the purpose, 
procedure and possible risks of this study to the subject or his/her agent, and sign a written informed 
consent. Subjects should be informed that they have the right to withdraw from this study at any time, 
and informed consent should be kept as a clinical study document for future reference.Subjects' 
privacy and data confidentiality will be protected during the study. 
 
13. Collection and storage of samples/specimens 
In this study, images of embryos were collected every 15 minutes by time-lapse system, and all 
the images were stored in the computer hard drive.It is stored in the hard disk of the computer of the 
center. It is backed up regularly every year and kept completely indefinitely. 
 
14. References 
[1] Milingos D S, Bhattacharya S. Single embryo transfer. Obstetrics, Gynaecology & 
Reproductive Medicine, 2009, 19(8): 229-231. 
[2] Sazonova A, Kallen K, Thurin-Kjellberg A, et al. Neonatal and maternal outcomes 
comparing women undergoing two in vitro fertilization (IVF) singleton pregnancies and women 
undergoing one IVF twin pregnancy. Fertility and sterility, 2013, 99(3): 731-737. 
[3] Kim D H, Jeon J, Park C G, et al. Neonatal and infant mortality in Korea, Japan, and the US: 
Effect of birth weight distribution and birth weight-specific mortality rates. Journal of Korean 
medical science, 2016, 31 (9) : 1450-1454. 
[4] Morin S, Melzer-Ross K, McCulloh D, et al. A greater number of euploid blastocysts in a 
given cohort predicts excellent outcomes in single embryo transfer cycles. Journal of assisted 
reproduction and genetics, 2014, 31(6): 667-673. 
[5] ALPHA Scientists in Reproductive Medicine and ESHRE Special Interest Group in 
Embryology "The istanbul consensus workshop on embryo assessment:proceedings of an expert 
meeting," Human reproduction, vol. 26, no. 6, pp. 1270 -- 1283, 2011. 

 
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[6] Beuchat A, Thevenaz P, Unser M, et al. Quantitative morphometrical characterization of 
human pronuclear zygotes[J]. Human Reproduction, 2008, 23(9): 1983-1992. 
[7] Zhao M, Li H, Li R, et al. Automated and precise recognition of human zygote cytoplasm: 
A robust image-segmentation system based on a convolutional neural network[J]. Biomedical Signal 
Processing and Control, 2021, 67:102551. 
[8] Reddy V, Badamjav O, Meyer D, et al. Pilot utilization of convolutional neural networks to 
improve the efficiency of fertilization checks[J]. Fertility and Sterility, 2019, 111(4): e5. 
[9] Automated monitoring of human embryonic cells up to the 5-cell stage in time-lapse 
microscopy images[C]//2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). 
IEEE, 2015: 389-393. 
[10] Lau T, Ng N, Gingold J, et al. Embryo staging with weakly-supervised region selection and 
dynamically-decoded predictions[C]//Machine Learning for Healthcare Conference. PMLR, 2019: 
663-679. 
[11] Zhong Junjie, Luo Yudi, Luo Bowen, et al. Prediction value of cleavage stage combined 
with prokaryotic stage score for embryo development potential in vitro fertilization [J]. Journal of 
Guangxi Medical University, 2019, 7. 
[12] The Istanbul consensus workshop on embryo assessment: proceedings of an expert meeting. 
Human reproduction, 2011, 26(6): 1270-1283. 
[13] Khosravi P, Kazemi E, Zhan Q, et al. Deep learning enables robust assessment and selection 
of human blastocysts after in vitro fertilization. NPJ digital medicine, 2019, 2(1): 1-9. 
[14] Armstrong S, Vail A, Mastenbroek S, et al. Time-lapse in the IVF-lab: how should we assess 
potential benefit?. Human Reproduction, 2015, 30(1): 3-8. 
[15] Paulson R J, Reichman D E, Zaninovic N, et al. Time-lapse imaging: clearly useful to both 
laboratory personnel and patient outcomes versus just because we can doesn't mean we should. 
Fertility and Sterility, 2018, 109(4): 584-591. 
[16] Russell S J, Norvig P. Artificial intelligence: a modern approach. Malaysia. 2016. 
[17] LeCun Y, Bengio Y, Hinton G. Deep learning. nature, 2015, 521(7553): 436-444. 
[18] Gu J, Wang Z, Kuen J, et al. Recent advances in convolutional neural networks. Pattern 
Recognition, 2018, 77: 354-377. 
[19] Bengio Y, Goodfellow I, Courville A. Deep learning[M]. Massachusetts, USA:: MIT press, 
2017. 
[20] Abramoff M D, Lou Y, Erginay A, et al. Improved automated detection of diabetic 
retinopathy on a publicly available dataset through integration of deep learning. Investigative 
ophthalmology & visual science, 2016, 57(13): 5200-5206. 
[21] Esteva A, Kuprel B, Novoa R A, et al. Dermatologist-level classification of skin cancer with 
deep neural networks. nature, 2017, 542(7639): 115-118. 

 
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[22] Khosravi P, Kazemi E, Imielinski M, et al. Deep convolutional neural networks enable 
discrimination of heterogeneous digital pathology images. EBioMedicine, 2018, 27: 317-328. 
[23] Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis. 
Medical image analysis, 2017, 42: 60-88. 
[24] Rocha J C, Passalia F J, Matos F D, et al. A method based on artificial intelligence to fully 
automatize the evaluation of bovine blastocyst images. Scientific reports, 2017, 7(1): 1-10. 
[25] Liu Z, Huang B, Cui Y, et al. Multi-task deep learning with dynamic programming for 
embryo early development stage classification from time-lapse videos. IEEE Access, 2019, 7: 
122153-122163. 
[26] Chen T J, Zheng W L, Liu C H, et al. Using Deep Learning with Large Dataset of 
Microscope Images to Develop an Automated Embryo Grading System. Fertility & Reproduction, 
2019, 1(01): 51-56. 
[27] Selvaraju R R, Cogswell M, Das A, et al. Grad-cam: Visual explanations from deep 
networks via gradient-based localization[C]//Proceedings of the IEEE international conference on 
computer vision. 2017: 618-626. 
[28] Wang S, Zhou C, Zhang D, et al. A Deep Learning Framework Design for Automatic 
Blastocyst Evaluation With Multifocal Images. IEEE Access, 2021, 9:18927-18934. 

 
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Investigator Statement and Protocol Signature page 
  
  
The researchers stated: 
  
I agree to comply with the review opinions of the Ethics Committee and start the clinical 
trial after approval, report to the Ethics Committee in a timely manner any changes in the 
clinical trial activities, as well as any unexpected problems involving risks to subjects or 
other personnel, and carry out the trial after re-obtaining the approval of the ethical 
review.Follow up review and conclusion review as required by the Ethics Committee. 
 
I agree to conduct clinical trials in strict accordance with the design and specific provisions of 
this protocol. 
  
I understand that I can interrupt or terminate this clinical trial at any time if it is in the best 
interest of the subjects.  
  
I agree that I will personally conduct or supervise the clinical trial and that all investigators 
in my organization who assist me in conducting the clinical trial understand their role in the 
clinical trial. 
   
In carrying out this clinical trial, I will strictly abide by the current GCP and the Declaration of 
Helsinki.And promised that the whole process will be ethical, ethical and scientific requirements. 
  
During the implementation of clinical trials, I will strictly abide by all laws and regulations 
related to clinical trials to protect the rights and interests of patients. 
 
I agree to maintain adequate and accurate medical records and ensure that these medical 
records are readily available for inspection and inspection in accordance with relevant laws and 
regulations. 
  
   
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Name (block letter)         Signature                         Date of signature