Ignite Creation Date:
2025-12-24 @ 9:17 PM
Ignite Modification Date:
2026-04-04 @ 10:08 PM
Study NCT ID:
NCT06762704
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-01-08
First Post:
2024-12-31
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Evaluation of an Artificial Intelligence Model for the Prediction of Human Blastocyst Ploidy Without Invasive Procedures
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'interventionBrowseModule': {'meshes': [{'id': 'D014743', 'term': 'Videotape Recording'}], 'ancestors': [{'id': 'D013637', 'term': 'Tape Recording'}, {'id': 'D001296', 'term': 'Audiovisual Aids'}, {'id': 'D018961', 'term': 'Educational Technology'}, {'id': 'D013672', 'term': 'Technology'}, {'id': 'D013676', 'term': 'Technology, Industry, and Agriculture'}, {'id': 'D013690', 'term': 'Television'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'Five to eight trophectoderm cells will be biopsied and collected from each Day 5/6 blastocyst. Preimplantation genetic testing for aneuploidy will then be conducted on these cells, and the test reports will be recorded.'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1408}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-02', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-01', 'completionDateStruct': {'date': '2027-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-01-06', 'studyFirstSubmitDate': '2024-12-31', 'studyFirstSubmitQcDate': '2024-12-31', 'lastUpdatePostDateStruct': {'date': '2025-01-08', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-01-07', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-06', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Sensitivity', 'timeFrame': 'Through study completion, an average of 1 year', 'description': 'Sensitivity = TP/(TP+FN). TP: number of outcomes where a euploid blastocyst is correctly predicted as euploidy. FN: number of outcomes where a euploid blastocyst is incorrectly predicted as aneuploidy. High sensitivity indicates strong ability for the prediction of euploid blastocysts.'}, {'measure': 'Specificity', 'timeFrame': 'Through study completion, an average of 1 year', 'description': 'Specificity = TN/(TN+FP). TN: number of outcomes where an aneuploid blastocyst is correctly predicted as aneuploidy. FP: number of outcomes where an aneuploid blastocyst is incorrectly predicted as euploidy. High specificity indicates strong ability for the prediction of aneuploid blastocysts.'}], 'secondaryOutcomes': [{'measure': 'Accuracy', 'timeFrame': 'Through study completion, an average of 1 year', 'description': 'Accuracy evaluates the number of correct predictions of blastocyst ploidy over the total number of predictions'}, {'measure': 'AUC', 'timeFrame': 'Through study completion, an average of 1 year', 'description': 'AUC is defined as the area under the receiver operating characteristic curve, which evaluates the overall model performance for predicting blastocyst ploidy regardless of classification threshold.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Blastocyst Evaluation', 'Prediction of Human Blastocyst Ploidy', '3D Morphological Measurement of Human Blastocysts', 'Non-Invasive Evaluation of Human Blastocysts'], 'conditions': ['The Focus is to Validate the Accuracy of a Non-invasive Artificial Intelligence Model for the Prediction of the Human Blastocyst Ploidy', 'Preimplantation Genetic Testing (PGT)']}, 'referencesModule': {'references': [{'pmid': '22305103', 'type': 'BACKGROUND', 'citation': 'Scott RT Jr, Ferry K, Su J, Tao X, Scott K, Treff NR. Comprehensive chromosome screening is highly predictive of the reproductive potential of human embryos: a prospective, blinded, nonselection study. Fertil Steril. 2012 Apr;97(4):870-5. doi: 10.1016/j.fertnstert.2012.01.104. Epub 2012 Feb 2.'}, {'pmid': '34480803', 'type': 'BACKGROUND', 'citation': 'Wang L, Wang X, Liu Y, Ou X, Li M, Chen L, Shao X, Quan S, Duan J, He W, Shen H, Sun L, Yu Y, Cram DS, Leigh D, Yao Y. IVF embryo choices and pregnancy outcomes. Prenat Diagn. 2021 Dec;41(13):1709-1717. doi: 10.1002/pd.6042. Epub 2021 Oct 14.'}, {'pmid': '19224361', 'type': 'BACKGROUND', 'citation': 'Kushnir VA, Frattarelli JL. Aneuploidy in abortuses following IVF and ICSI. J Assist Reprod Genet. 2009 Mar;26(2-3):93-7. doi: 10.1007/s10815-009-9292-z. Epub 2009 Feb 18.'}, {'pmid': '21044350', 'type': 'BACKGROUND', 'citation': 'Kim JW, Lee WS, Yoon TK, Seok HH, Cho JH, Kim YS, Lyu SW, Shim SH. Chromosomal abnormalities in spontaneous abortion after assisted reproductive treatment. BMC Med Genet. 2010 Nov 3;11:153. doi: 10.1186/1471-2350-11-153.'}, {'pmid': '32141057', 'type': 'BACKGROUND', 'citation': 'Sciorio R, Dattilo M. PGT-A preimplantation genetic testing for aneuploidies and embryo selection in routine ART cycles: Time to step back? Clin Genet. 2020 Aug;98(2):107-115. doi: 10.1111/cge.13732. Epub 2020 Apr 6.'}, {'pmid': '31551155', 'type': 'BACKGROUND', 'citation': 'Munne S, Kaplan B, Frattarelli JL, Child T, Nakhuda G, Shamma FN, Silverberg K, Kalista T, Handyside AH, Katz-Jaffe M, Wells D, Gordon T, Stock-Myer S, Willman S; STAR Study Group. Preimplantation genetic testing for aneuploidy versus morphology as selection criteria for single frozen-thawed embryo transfer in good-prognosis patients: a multicenter randomized clinical trial. Fertil Steril. 2019 Dec;112(6):1071-1079.e7. doi: 10.1016/j.fertnstert.2019.07.1346. Epub 2019 Sep 21.'}, {'pmid': '30098682', 'type': 'BACKGROUND', 'citation': 'Rosenwaks Z, Handyside AH, Fiorentino F, Gleicher N, Paulson RJ, Schattman GL, Scott RT Jr, Summers MC, Treff NR, Xu K. The pros and cons of preimplantation genetic testing for aneuploidy: clinical and laboratory perspectives. Fertil Steril. 2018 Aug;110(3):353-361. doi: 10.1016/j.fertnstert.2018.06.002. No abstract available.'}, {'pmid': '30895497', 'type': 'BACKGROUND', 'citation': 'Belandres D, Shamonki M, Arrach N. Current status of spent embryo media research for preimplantation genetic testing. J Assist Reprod Genet. 2019 May;36(5):819-826. doi: 10.1007/s10815-019-01437-6. Epub 2019 Mar 21.'}, {'pmid': '37356468', 'type': 'BACKGROUND', 'citation': 'Cinnioglu C, Glessner H, Jordan A, Bunshaft S. A systematic review of noninvasive preimplantation genetic testing for aneuploidy. Fertil Steril. 2023 Aug;120(2):235-239. doi: 10.1016/j.fertnstert.2023.06.013. Epub 2023 Jun 24.'}, {'pmid': '21502182', 'type': 'BACKGROUND', 'citation': 'Alpha Scientists in Reproductive Medicine and ESHRE Special Interest Group of Embryology. The Istanbul consensus workshop on embryo assessment: proceedings of an expert meeting. Hum Reprod. 2011 Jun;26(6):1270-83. doi: 10.1093/humrep/der037. Epub 2011 Apr 18.'}, {'pmid': '30617927', 'type': 'BACKGROUND', 'citation': 'Ozgur K, Berkkanoglu M, Bulut H, Yoruk GDA, Candurmaz NN, Coetzee K. Single best euploid versus single best unknown-ploidy blastocyst frozen embryo transfers: a randomized controlled trial. J Assist Reprod Genet. 2019 Apr;36(4):629-636. doi: 10.1007/s10815-018-01399-1. Epub 2019 Jan 7.'}, {'pmid': '32863013', 'type': 'BACKGROUND', 'citation': 'Tiegs AW, Tao X, Zhan Y, Whitehead C, Kim J, Hanson B, Osman E, Kim TJ, Patounakis G, Gutmann J, Castelbaum A, Seli E, Jalas C, Scott RT Jr. A multicenter, prospective, blinded, nonselection study evaluating the predictive value of an aneuploid diagnosis using a targeted next-generation sequencing-based preimplantation genetic testing for aneuploidy assay and impact of biopsy. Fertil Steril. 2021 Mar;115(3):627-637. doi: 10.1016/j.fertnstert.2020.07.052. Epub 2020 Aug 28.'}, {'pmid': '32843306', 'type': 'BACKGROUND', 'citation': 'Chavez-Badiola A, Flores-Saiffe-Farias A, Mendizabal-Ruiz G, Drakeley AJ, Cohen J. Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation. Reprod Biomed Online. 2020 Oct;41(4):585-593. doi: 10.1016/j.rbmo.2020.07.003. Epub 2020 Jul 5.'}, {'pmid': '35674312', 'type': 'BACKGROUND', 'citation': 'Diakiw SM, Hall JMM, VerMilyea MD, Amin J, Aizpurua J, Giardini L, Briones YG, Lim AYX, Dakka MA, Nguyen TV, Perugini D, Perugini M. Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF. Hum Reprod. 2022 Jul 30;37(8):1746-1759. doi: 10.1093/humrep/deac131.'}, {'pmid': '36543475', 'type': 'BACKGROUND', 'citation': 'Barnes J, Brendel M, Gao VR, Rajendran S, Kim J, Li Q, Malmsten JE, Sierra JT, Zisimopoulos P, Sigaras A, Khosravi P, Meseguer M, Zhan Q, Rosenwaks Z, Elemento O, Zaninovic N, Hajirasouliha I. A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study. Lancet Digit Health. 2023 Jan;5(1):e28-e40. doi: 10.1016/S2589-7500(22)00213-8.'}, {'pmid': '37645653', 'type': 'BACKGROUND', 'citation': 'Danardono GB, Handayani N, Louis CM, Polim AA, Sirait B, Periastiningrum G, Afadlal S, Boediono A, Sini I. Embryo ploidy status classification through computer-assisted morphology assessment. AJOG Glob Rep. 2023 May 18;3(3):100209. doi: 10.1016/j.xagr.2023.100209. eCollection 2023 Aug.'}, {'pmid': '37394089', 'type': 'BACKGROUND', 'citation': 'Jiang VS, Bormann CL. Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction. Fertil Steril. 2023 Aug;120(2):228-234. doi: 10.1016/j.fertnstert.2023.06.025. Epub 2023 Jun 30.'}, {'pmid': '34021832', 'type': 'BACKGROUND', 'citation': 'Lee CI, Su YR, Chen CH, Chang TA, Kuo EE, Zheng WL, Hsieh WT, Huang CC, Lee MS, Liu M. End-to-end deep learning for recognition of ploidy status using time-lapse videos. J Assist Reprod Genet. 2021 Jul;38(7):1655-1663. doi: 10.1007/s10815-021-02228-8. Epub 2021 May 22.'}, {'pmid': '34903224', 'type': 'BACKGROUND', 'citation': 'Huang B, Tan W, Li Z, Jin L. An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data. Reprod Biol Endocrinol. 2021 Dec 13;19(1):185. doi: 10.1186/s12958-021-00864-4.'}, {'pmid': '36640251', 'type': 'BACKGROUND', 'citation': 'Jiang VS, Kandula H, Thirumalaraju P, Kanakasabapathy MK, Cherouveim P, Souter I, Dimitriadis I, Bormann CL, Shafiee H. The use of voting ensembles to improve the accuracy of deep neural networks as a non-invasive method to predict embryo ploidy status. J Assist Reprod Genet. 2023 Feb;40(2):301-308. doi: 10.1007/s10815-022-02707-6. Epub 2023 Jan 14.'}, {'pmid': '37394179', 'type': 'BACKGROUND', 'citation': 'Paya E, Pulgarin C, Bori L, Colomer A, Naranjo V, Meseguer M. Deep learning system for classification of ploidy status using time-lapse videos. F S Sci. 2023 Aug;4(3):211-218. doi: 10.1016/j.xfss.2023.06.002. Epub 2023 Jun 30.'}, {'pmid': '37015494', 'type': 'BACKGROUND', 'citation': 'Shan G, Dai C, Liu H, Wang X, Dou W, Zhang Z, Sun Y. 3D Morphology Measurement for Blastocyst Evaluation From "All Angles". IEEE Trans Biomed Eng. 2023 Jun;70(6):1921-1930. doi: 10.1109/TBME.2022.3232068. Epub 2023 May 19.'}, {'pmid': '39468586', 'type': 'BACKGROUND', 'citation': 'Shan G, Abdalla K, Liu H, Dai C, Tan J, Law J, Steinberg C, Li A, Kuznyetsova I, Zhang Z, Librach C, Sun Y. Non-invasively predicting euploidy in human blastocysts via quantitative 3D morphology measurement: a retrospective cohort study. Reprod Biol Endocrinol. 2024 Oct 28;22(1):132. doi: 10.1186/s12958-024-01302-x.'}]}, 'descriptionModule': {'briefSummary': 'The goal of this clinical trial is to evaluate an artificial intelligence model for the prediction of human blastocyst ploidy without invasive procedures in couples that receive preimplantation genetic testing. The main questions it aims to answer are:\n\n* Is an artificial intelligence model able to predict the ploidy status of a human blastocyst based on its 3D morphology?\n* Do quantitative 3D morphological parameters of trophectoderm cells and inner cell mass have strong correlations with human blastocyst ploidy status?\n\nVideos that include multi-view images of each blastocyst from participants will be collected on Day 5/6 of culture, and preimplantation genetic testing results of these blastocysts will be collected 4-8 weeks after trophectoderm biopsy.', 'detailedDescription': 'Background:\n\nA pilot study has been conducted at The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School from August 2023 to September 2024. Videos and preimplantation genetic testing (PGT) results from 144 blastocysts were retrospectively collected. The artificial intelligence (AI) model first reconstructed the 3D surface of the blastocysts from the videos, and then measured their 3D morphological parameters. Based on these parameters, the model predicted the ploidy status of the blastocysts, and the prediction outputs were compared with the PGT results. The prediction sensitivity, specificity, accuracy and AUC were 90.5%, 91.3%, 90.9% and 0.946, respectively.\n\nStudy design:\n\nThis is a multi-center, prospective, non-randomized, non-blinded, and single-group study. After being informed about the study and potential risks, all participants will write the informed consents. Videos and PGT results of Day 5/6 blastocysts from each participant will be collected. Blastocysts will be classified as euploid, mosaic, and aneuploid corresponding to \\<30%, 30-80%, and \\>80% aneuploidy, respectively. Embryo culture, biopsy, and transfer will follow the standard operating procedure (SOP) in the laboratory. The study is non-interventional, and results will not be used to make treatment decisions.\n\nSample size:\n\nWe plan to enroll \\~1408 Day 5/6 blastocysts in this trial based on one-sample sensitivity and specificity analysis. Meta-analysis shows that the sensitivity and specificity of the existing AI models are 73.4% (3702/5047) and 69.6% (4892/7028), and those of the non-invasive chromosomal screening methods are 80.3% (678/844) and 73.3% (908/1238) for non-invasively predicting blastocyst ploidy status. This study is presumed to achieve a sensitivity of no less than 85% and a specificity of no less than 80% with a significance level of α = 0.05. A total of 1126 blastocysts are required to achieve a statistical power of 0.9. Assuming a \\~20% dropout rate, a total of 1408 blastocysts are anticipated to be enrolled. This sample size calculation is based on the analysis of statistical power and will be regularly revisited/adjusted during the trial period to ensure a high statistical power is achieved.\n\nData management:\n\nThe electronic data capture (EDC) system will be used for data collection. A clinical research coordinator will be assigned at each hospital, and they are responsible for recording the videos and clinical data via the EDC system. A senior clinical research associate will inspect the data in the EDC system regularly among 5 hospitals. The Data Safety and Monitoring Committee (DSMC) is responsible for overseeing the entire research process and the EDC system. For incomplete or missing data in the EDC system, the DSMC will contact the investigators for clarification.\n\nStatistical analysis:\n\nStatistical analysis will be conducted using IBM SPSS Statistics 26. Categorical variables will be described by number and percentage, and numerical variables will be described by mean, standard deviation (SD) and range. The Chi-squared test will be performed to analyze trends in categorical variables, and the t-test will be performed to compare numerical variables among different groups. Pearson correlation will be used to analyze the linear relationship among numerical variables. All statistical tests are two-tailed. P-values of \\<0.05 will be considered statistically significant, and odd ratios (ORs) with 95% confidence interval (CI) will be calculated. Logistic regression will be used for multivariate analysis to calculate the adjusted odd ratios (aORs). The performance of the AI for blastocyst ploidy prediction will be evaluated by sensitivity, specificity, accuracy and AUC, with 95% confidence interval.\n\nMissing data will be removed if the proportion of samples with missing values is very small relative to the total sample size. Otherwise, the average, maximum, minimum, medium, or regression model will be used to impute the missing values. Outliers will be treated as the missing data and addressed accordingly.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT'], 'maximumAge': '55 Years', 'minimumAge': '20 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'This study is a multi-center trial which will be conducted in five reproductive medicine centers in eight provinces (Jiangsu, Shanghai, Jiangxi, Shaanxi, and Anhui) across China. The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School will serve as the primary investigate center. Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Jiangxi Maternal and Child Health Hospital, Tangdu Hospital of Air Force Military Medical University, and The First Affiliated Hospital of USTC (Anhui Provincial Hospital) will serve as the participant investigate centers.\n\nVideos that include multi-view images of human blastocysts will be collected on Day 5/6 of culture. PGT-A results of these blastocysts will be collected 4-8 weeks after trophectoderm biopsy. The study is non-interventional, and results will not be used to make treatment decisions.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Maternal age between 20 and 40; paternal age between 20 and 55.\n* Preimplantation genetic testing (PGT) cycles, including PGT for aneuploidy, PGT for monogenic disorders (PGT-M) or PGT for structural chromosome defect (PGT-SR).\n* Having at least one Day 5/6 blastocyst developed from two-pronuclear (2PN) embryo which is suitable for trophectoderm biopsy (i.e., degree of expansion: IV, and at least a grade better than C for trophectoderm and inner cell mass grading).\n* Couples with written informed consent.\n\nExclusion Criteria:\n\n* Couples with contraindications for IVF or PGT.\n* Women with all oocytes frozen after retrieval.\n* Couples who fail to follow the study protocol.\n* Couples deemed ineligible for enrollment by the investigator in consideration of study protocol and treatment safety.'}, 'identificationModule': {'nctId': 'NCT06762704', 'briefTitle': 'Evaluation of an Artificial Intelligence Model for the Prediction of Human Blastocyst Ploidy Without Invasive Procedures', 'organization': {'class': 'OTHER', 'fullName': 'The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School'}, 'officialTitle': 'A Non-Invasive Artificial Intelligence Model for the Prediction of Human Blastocyst Ploidy: a Multi-Center, Prospective, Non-Randomized Validation Study', 'orgStudyIdInfo': {'id': '2024-739-02'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Video recording', 'type': 'OTHER', 'description': 'Videos of rotating the blastocysts will be recorded during the preparation stage of trophectoderm (TE) biopsy. The focal plane starts from the middle plane of the blastocyst. and then moves downwards until individual TE cells and inner cell mass (ICM) are clearly visible. A biopsy micropipette is used to gently push the blastocyst and rotate the blastocyst each time by a small angle, for instance, smaller than 35° such that more than 10 images can be captured for the entire 360° rotation to achieve high-accuracy measurement. After the first 360° rotation, the second one will be conducted around the axis perpendicular to the previous axis to ensure the whole surface of the blastocyst is captured. The entire rotation process will be video recoded.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '230001', 'city': 'Hefei', 'state': 'Anhui', 'country': 'China', 'contacts': [{'name': 'Limin Wu', 'role': 'CONTACT', 'email': 'wuliminmail@126.com', 'phone': '18963797080'}, {'name': 'Limin Wu', 'role': 'CONTACT'}], 'facility': 'The First Affiliated Hospital of USTC (Anhui Provincial Hospital)', 'geoPoint': {'lat': 31.86389, 'lon': 117.28083}}, {'zip': '210008', 'city': 'Nanjing', 'state': 'Jiangsu', 'country': 'China', 'contacts': [{'name': 'Shanshan Wang', 'role': 'CONTACT', 'email': 'wss_19860820@sina.com', 'phone': '13814549922'}, {'name': 'Haixiang Sun', 'role': 'CONTACT'}], 'facility': 'The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School', 'geoPoint': {'lat': 32.06167, 'lon': 118.77778}}, {'zip': '330046', 'city': 'Nanchang', 'state': 'Jiangxi', 'country': 'China', 'contacts': [{'name': 'Yan Zhao', 'role': 'CONTACT', 'email': 'zhaoyan1972009@163.com', 'phone': '13807056599'}, {'name': 'Yan Zhao', 'role': 'CONTACT'}], 'facility': 'Jiangxi Maternal and Child Health Hospital', 'geoPoint': {'lat': 28.68396, 'lon': 115.85306}}, {'zip': '710024', 'city': "Xi'an", 'state': 'Shaanxi', 'country': 'China', 'contacts': [{'name': 'Xiaohong Wang', 'role': 'CONTACT', 'email': 'wangxh919@fmmu.edu.cn', 'phone': '13991262559'}, {'name': 'Xiaohong Wang', 'role': 'CONTACT'}], 'facility': 'Tangdu Hospital of Air Force Military Medical University', 'geoPoint': {'lat': 34.25833, 'lon': 108.92861}}, {'zip': '200127', 'city': 'Shanghai', 'state': 'Shanghai Municipality', 'country': 'China', 'contacts': [{'name': 'Yun Sun', 'role': 'CONTACT', 'email': 'syun163@163.com', 'phone': '13601634278'}, {'name': 'Yun Sun', 'role': 'CONTACT'}], 'facility': 'Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}], 'centralContacts': [{'name': 'Shanshan Wang', 'role': 'CONTACT', 'email': 'wss_19860820@sina.com', 'phone': '13814549922'}, {'name': 'Haixiang Sun', 'role': 'CONTACT', 'email': 'stevensunz@163.com', 'phone': '13851622008'}], 'overallOfficials': [{'name': 'Haixiang Sun', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'ICF', 'CSR', 'ANALYTIC_CODE'], 'timeFrame': 'Beginning 3 months after publication or one year after completion of the trial with no end date', 'ipdSharing': 'YES', 'description': 'All collected IPD will be shared according to the data access criteria.', 'accessCriteria': 'The study protocol, statistical analysis plan, informed consent form and clinical study report will be available in publications. IPD that underlie results in publications will be shared online with the publications. Analytic codes will be available at the open-source online platform, e.g., Github. All other IPD collected for the study, including specified dataset and a data dictionary defining each field in the set, will be shared on reasonable request to the principal investigator at The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School via emails. Principal investigators from all five participant hospitals will review the request in consideration of patient privacy, data safety, and data analyses plan.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School', 'class': 'OTHER'}, 'collaborators': [{'name': 'RenJi Hospital', 'class': 'OTHER'}, {'name': 'Jiangxi Maternal and Child Health Hospital', 'class': 'OTHER'}, {'name': 'Tangdu Hospital of Air Force Military Medical University', 'class': 'UNKNOWN'}, {'name': 'The First Affiliated Hospital of USTC (Anhui Provincial Hospital)', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Director of Department of Reproductive Medicine', 'investigatorFullName': 'Wang Shanshan', 'investigatorAffiliation': 'The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School'}}}}