Application of Time-Lapse Imaging Technology and Artificial Intelligence in ART

Yanrong HUANG; Mingying LI; Mengying GAO; Lifeng XIANG; Jiacong YAN; Yonggang LI

doi:10.12259/j.issn.2095-610X.S20240724

Volume 45 Issue 7

Jul. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Kunming Medical University > 2024 > 45(7): 160-167

Yanrong HUANG, Mingying LI, Mengying GAO, Lifeng XIANG, Jiacong YAN, Yonggang LI. Application of Time-Lapse Imaging Technology and Artificial Intelligence in ART[J]. Journal of Kunming Medical University, 2024, 45(7): 160-167. doi: 10.12259/j.issn.2095-610X.S20240724

Citation:

Yanrong HUANG, Mingying LI, Mengying GAO, Lifeng XIANG, Jiacong YAN, Yonggang LI. Application of Time-Lapse Imaging Technology and Artificial Intelligence in ART[J]. Journal of Kunming Medical University, 2024, 45(7): 160-167. doi: 10.12259/j.issn.2095-610X.S20240724

Citation:

PDF( 578 KB)

Application of Time-Lapse Imaging Technology and Artificial Intelligence in ART

doi: 10.12259/j.issn.2095-610X.S20240724

Yanrong HUANG^{1, 2},
Mingying LI^{2, 3},
Mengying GAO^{2, 3},
Lifeng XIANG^{1, 2, 3},
Jiacong YAN^{1, 2, 3},
Yonggang LI^{1, 2, 3
,
,}

1.
Medical school，Kunming University of Science and Technology，Kunming Yunnan 650500
2.
Reproductive Medical Center，The 1st People’s Hospital of Yunnan Province，Kunming Yunnan 650032
3.
NHC Key Laboratory of Periconception Health Birth in Western China，KunmingYunnan 650032，China

Received Date: 2023-12-12
Publish Date: 2024-07-25

Abstract

Abstract

Accurate assessment of embryo development is of paramount importance for the success of Assisted Reproductive Technologies （ART）. Traditional methods predominantly rely on subjective embryo morphological evaluations, lacking objectivity and real-time capabilities. Time-lapse Technology （TLT） offers a more stable incubation environment, facilitating dynamic monitoring of embryo development, analysis, and modeling of dynamic parameters during different developmental stages for predicting embryo implantation potential. However, dynamic parameters often require manual annotations, introducing subjective bias and exhibiting significant disparities in data modeling capabilities, deviating from real-world scenarios, especially in the analysis of chromosomal ploidy. With the continuous advancement of Artificial Intelligence （AI）, the integration of TLT and AI holds promise in reducing manual annotation time in TLT, enhancing embryo implantation rates, and chromosomal ploidy prediction. This review aims to explore the application of TLT combined with AI in morphological and dynamic parameters for assessing embryo implantation potential and chromosomal ploidy.
- Assisted reproduction,
- Time-lapse imaging technology,
- Artificial intelligence,
- Embryo implantation potential,
- Embryo euploidy

FullText(HTML)

References(58)

References

[1]	Fujiwara M,Takahashi K,Izuno M,et al. Effect of micro-environment maintenance on embryo culture after in-vitro fertilization: comparison of top-load mini incubator and conventional front-load incubator[J]. J Assist Reprod Genet,2007,24(1):5-9. doi: 10.1007/s10815-006-9088-3
[2]	陈志坚,汪彩珠. 时差成像技术用于胚胎选择的研究进展[J]. 国际生殖健康/计划生育杂志,2022,41(02):139-142.
[3]	中国医师协会生殖医学专业委员会. 人类卵裂期胚胎及囊胚形态学评价中国专家共识[J]. 中华生殖与避孕杂志,2022(12):1218-1225.
[4]	Gazzo E,Peña F,Valdéz F,et al. The Kidscore(TM) D5 algorithm as an additional tool to morphological assessment and PGT-A in embryo selection: a time-lapse study[J]. JBRA Assist Reprod,2020,24(1):55-60.
[5]	Kovacs P,Matyas S,Forgacs V,et al. Non-invasive embryo evaluation and selection using time-lapse monitoring: Results of a randomized controlled study[J]. Eur J Obstet Gynecol Reprod Biol,2019,233:58-63. doi: 10.1016/j.ejogrb.2018.12.011
[6]	Capalbo A,Rienzi L,Cimadomo D,et al. Correlation between standard blastocyst morphology,euploidy and implantation: an observational study in two centers involving 956 screened blastocysts[J]. Hum Reprod,2014,29(6):1173-1181. doi: 10.1093/humrep/deu033
[7]	Macklon N S,Geraedts J P,Fauser B C. Conception to ongoing pregnancy: The 'black box' of early pregnancy loss[J]. Hum Reprod Update,2002,8(4):333-343. doi: 10.1093/humupd/8.4.333
[8]	Basile N,Nogales Mdel C,Bronet F,et al. Increasing the probability of selecting chromosomally normal embryos by time-lapse morphokinetics analysis[J]. Fertil Steril,2014,101(3):699-704. doi: 10.1016/j.fertnstert.2013.12.005
[9]	Chawla M,Fakih M,Shunnar A,et al. Morphokinetic analysis of cleavage stage embryos and its relationship to aneuploidy in a retrospective time-lapse imaging study[J]. J Assist Reprod Genet,2015,32(1):69-75. doi: 10.1007/s10815-014-0372-3
[10]	Campbell A,Fishel S,Bowman N,et al. Modelling a risk classification of aneuploidy in human embryos using non-invasive morphokinetics[J]. Reprod Biomed Online,2013,26(5):477-485. doi: 10.1016/j.rbmo.2013.02.006
[11]	Huang T T,Huang D H,Ahn H J,et al. Early blastocyst expansion in euploid and aneuploid human embryos: evidence for a non-invasive and quantitative marker for embryo selection[J]. Reprod Biomed Online,2019,39(1):27-39. doi: 10.1016/j.rbmo.2019.01.010
[12]	Zaninovic N,Irani M,Meseguer M. Assessment of embryo morphology and developmental dynamics by time-lapse microscopy: Is there a relation to implantation and ploidy?[J]. Fertil Steril,2017,108(5):722-729. doi: 10.1016/j.fertnstert.2017.10.002
[13]	Liu Y,Chapple V,Roberts P,et al. Prevalence,consequence,and significance of reverse cleavage by human embryos viewed with the use of the Embryoscope time-lapse video system [J]. Fertil Steril,2014,102(5): 1295-1300. e1292.
[14]	Rubio I,Kuhlmann R,Agerholm I,et al. Limited implantation success of direct-cleaved human zygotes: A time-lapse study[J]. Fertil Steril,2012,98(6):1458-1463. doi: 10.1016/j.fertnstert.2012.07.1135
[15]	Liu Y,Qi F,Matson P,et al. Between-laboratory reproducibility of time-lapse embryo selection using qualitative and quantitative parameters: A systematic review and meta-analysis[J]. J Assist Reprod Genet,2020,37(6):1295-1302. doi: 10.1007/s10815-020-01789-4
[16]	Macedo J F,Gomes L M O,Oliveira M R,et al. Morphokinetic parameters as auxiliary criteria for selection of blastocysts cultivated in a time-lapse monitoring system[J]. JBRA Assist Reprod,2020,24(4):411-415.
[17]	Yang Q,Zhu L,Wang M,et al. Analysis of maturation dynamics and developmental competence of in vitro matured oocytes under time-lapse monitoring[J]. Reprod Biol Endocrinol,2021,19(1):183. doi: 10.1186/s12958-021-00868-0
[18]	Guo Y H,Liu Y,Qi L,et al. Can Time-Lapse Incubation and Monitoring Be Beneficial to Assisted Reproduction Technology Outcomes? A Randomized Controlled Trial Using Day 3 Double Embryo Transfer[J]. Front Physiol,2021,12:794601.
[19]	Rubio I,Galán A,Larreategui Z,et al. Clinical validation of embryo culture and selection by morphokinetic analysis: a randomized,controlled trial of the EmbryoScope [J]. Fertil Steril,2014,102(5): 1287-1294. e1285.
[20]	Chera-Aree P,Thanaboonyawat I,Thokha B,et al. Comparison of pregnancy outcomes using a time-lapse monitoring system for embryo incubation versus a conventional incubator in in vitro fertilization: An age-stratification analysis[J]. Clin Exp Reprod Med,2021,48(2):174-183. doi: 10.5653/cerm.2020.04091
[21]	Kieslinger D C,Vergouw C G,Ramos L,et al. Clinical outcomes of uninterrupted embryo culture with or without time-lapse-based embryo selection versus interrupted standard culture (SelecTIMO): A three-armed,multicentre,double-blind,randomised controlled trial[J]. Lancet (London,England),2023,401(10386):1438-1446. doi: 10.1016/S0140-6736(23)00168-X
[22]	Iwasawa T,Takahashi K,Goto M,et al. Human frozen-thawed blastocyst morphokinetics observed using time-lapse cinematography reflects the number of trophectoderm cells[J]. PLoS One,2019,14(1):e0210992. doi: 10.1371/journal.pone.0210992
[23]	Haikin Herzberger E,Ghetler Y,Tamir Yaniv R,et al. Time lapse microscopy is useful for elective single-embryo transfer[J]. Gynecol Endocrinol,2016,32(10):816-818. doi: 10.1080/09513590.2016.1188375
[24]	田可可,王娟,闫虹,等. 时差成像技术辅助胚胎选择在单胚胎移植中的应用价值[J]. 河南医学研究,2022,31(16):031.
[25]	Bamford T,Barrie A,Montgomery S,et al. Morphological and morphokinetic associations with aneuploidy: a systematic review and meta-analysis[J]. Hum Reprod Update,2022,28(5):656-686. doi: 10.1093/humupd/dmac022
[26]	Ho J R,Arrach N,Rhodes-Long K,et al. Blastulation timing is associated with differential mitochondrial content in euploid embryos[J]. J Assist Reprod Genet,2018,35(4):711-720. doi: 10.1007/s10815-018-1113-9
[27]	Boucret L,Tramon L,Saulnier P,et al. Change in the Strategy of Embryo Selection with Time-Lapse System Implementation-Impact on Clinical Pregnancy Rates [J]. J Clin Med,2021,10(18):4111.
[28]	Chavez S L,Loewke K E,Han J,et al. Dynamic blastomere behaviour reflects human embryo ploidy by the four-cell stage[J]. Nat Commun,2012,3:1251. doi: 10.1038/ncomms2249
[29]	Rienzi L,Capalbo A,Stoppa M,et al. No evidence of association between blastocyst aneuploidy and morphokinetic assessment in a selected population of poor-prognosis patients: a longitudinal cohort study[J]. Reprod Biomed Online,2015,30(1):57-66. doi: 10.1016/j.rbmo.2014.09.012
[30]	Huang B,Zheng S,Ma B,et al. Using deep learning to predict the outcome of live birth from more than 10,000 embryo data[J]. BMC Pregnancy Childbirth,2022,22(1):36. doi: 10.1186/s12884-021-04373-5
[31]	Bormann C L,Kanakasabapathy M K,Thirumalaraju P,et al. Performance of a deep learning based neural network in the selection of human blastocysts for implantation [J]. Elife,2020,9:e55301.
[32]	Yuan Z,Yuan M,Song X,et al. Development of an artificial intelligence based model for predicting the euploidy of blastocysts in PGT-A treatments[J]. Sci Rep,2023,13(1):2322. doi: 10.1038/s41598-023-29319-z
[33]	Kragh M F,Rimestad J,Lassen J T,et al. Predicting Embryo Viability Based on Self-Supervised Alignment of Time-Lapse Videos[J]. IEEE Trans Med Imaging,2022,41(2):465-475. doi: 10.1109/TMI.2021.3116986
[34]	Cimadomo D,Soscia D,Casciani V,et al. How slow is too slow? A comprehensive portrait of Day 7 blastocysts and their clinical value standardized through artificial intelligence[J]. Hum Reprod,2022,37(6):1134-1147. doi: 10.1093/humrep/deac080
[35]	Shu Y,Watt J,Gebhardt J,et al. The value of fast blastocoele re-expansion in the selection of a viable thawed blastocyst for transfer[J]. Fertil Steril,2009,91(2):401-406. doi: 10.1016/j.fertnstert.2007.11.083
[36]	Zhao J,Yan Y,Huang X,et al. Blastocoele expansion: an important parameter for predicting clinical success pregnancy after frozen-warmed blastocysts transfer[J]. Reprod Biol Endocrinol,2019,17(1):15. doi: 10.1186/s12958-019-0454-2
[37]	Lagalla C,Barberi M,Orlando G,et al. A quantitative approach to blastocyst quality evaluation: Morphometric analysis and related IVF outcomes[J]. J Assist Reprod Genet,2015,32(5):705-712. doi: 10.1007/s10815-015-0469-3
[38]	Huang T T F,Kosasa T,Walker B,et al. Deep learning neural network analysis of human blastocyst expansion from time-lapse image files[J]. Reprod Biomed Online,2021,42(6):1075-1085. doi: 10.1016/j.rbmo.2021.02.015
[39]	Chen C H,Lee C I,Huang C C,et al. Blastocyst morphology based on uniform time-point assessments is correlated with mosaic levels in embryos[J]. Front Genet,2021,12:783826. doi: 10.3389/fgene.2021.783826
[40]	Berntsen J,Rimestad J,Lassen J T,et al. Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences[J]. PLoS One,2022,17(2):e0262661. doi: 10.1371/journal.pone.0262661
[41]	Sawada Y,Sato T,Nagaya M,et al. Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth[J]. Reprod Biomed Online,2021,43(5):843-852. doi: 10.1016/j.rbmo.2021.05.002
[42]	Lee C I,Su Y R,Chen C H,et al. End-to-end deep learning for recognition of ploidy status using time-lapse videos[J]. J Assist Reprod Genet,2021,38(7):1655-1663. doi: 10.1007/s10815-021-02228-8
[43]	Huang B,Tan W,Li Z,et al. An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data[J]. Reprod Biol Endocrinol,2021,19(1):185. doi: 10.1186/s12958-021-00864-4
[44]	Paya E,Pulgarín C,Bori L,et al. Deep learning system for classification of ploidy status using time-lapse videos[J]. F S Sci,2023,4(3):211-218.
[45]	Zou Y,Pan Y,Ge N,et al. Can the combination of time-lapse parameters and clinical features predict embryonic ploidy status or implantation?[J]. Reprod Biomed Online,2022,45(4):643-651. doi: 10.1016/j.rbmo.2022.06.007
[46]	Lee C I,Huang C C,Lee T H,et al. Associations between the artificial intelligence scoring system and live birth outcomes in preimplantation genetic testing for aneuploidy cycles[J]. Reprod Biol Endocrinol,2024,22(1):12. doi: 10.1186/s12958-024-01185-y
[47]	Khosravi P,Kazemi E,Zhan Q,et al. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization[J]. NPJ Digit Med,2019,2:21. doi: 10.1038/s41746-019-0096-y
[48]	Liao Q,Zhang Q,Feng X,et al. Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring[J]. Commun Biol,2021,4(1):415. doi: 10.1038/s42003-021-01937-1
[49]	Leahy B D, Jang W D, Yang H Y, et al. Automated measurements of key morphological features of human embryos for IVF[J]. Medical Image Comput Comput Assist Interv,2020,12265:25-35.
[50]	Thirumalaraju P,Kanakasabapathy M K,Bormann C L,et al. Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality[J]. Heliyon,2021,7(2):e06298. doi: 10.1016/j.heliyon.2021.e06298
[51]	Hammer K C,Jiang V S,Kanakasabapathy M K,et al. Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study[J]. J Assist Reprod Genet,2022,39(10):2343-2348. doi: 10.1007/s10815-022-02585-y
[52]	Danardono G B,Erwin A,Purnama J,et al. A Homogeneous Ensemble of Robust Pre-defined Neural Network Enables Automated Annotation of Human Embryo Morphokinetics[J]. J Reprod Infertil,2022,23(4):250-256.
[53]	Kanakasabapathy M K,Thirumalaraju P,Bormann C L,et al. Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology[J]. Lab Chip,2019,19(24):4139-4145. doi: 10.1039/C9LC00721K
[54]	Ueno S,Berntsen J,Ito M,et al. Pregnancy prediction performance of an annotation-free embryo scoring system on the basis of deep learning after single vitrified-warmed blastocyst transfer: A single-center large cohort retrospective study[J]. Fertil Steril,2021,116(4):1172-1180. doi: 10.1016/j.fertnstert.2021.06.001
[55]	Duval A,Nogueira D,Dissler N,et al. A hybrid artificial intelligence model leverages multi-centric clinical data to improve fetal heart rate pregnancy prediction across time-lapse systems[J]. Hum Reprod,2023,38(4):596-608.
[56]	Petersen B M,Boel M,Montag M,et al. Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3[J]. Hum Reprod,2016,31(10):2231-2244. doi: 10.1093/humrep/dew188
[57]	Diakiw S M,Hall J M M,VerMilyea M D,et al. Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF[J]. Hum Reprod,2022,37(8):1746-1759. doi: 10.1093/humrep/deac131
[58]	De Gheselle S,Jacques C,Chambost J,et al. Machine learning for prediction of euploidy in human embryos: In search of the best-performing model and predictive features[J]. Fertil Steril,2022,117(4):738-746. doi: 10.1016/j.fertnstert.2021.11.029

Relative Articles(20)

Relative Articles

[1]	Limei HE, Xuemin FENG, Linjun LI, Ying CHEN, Zexing YANG. Changes of Sex Hormone Levels in Infertile Population with Polycystic Ovary Syndrome after the Assisted Reproductive Technology Treatment. Journal of Kunming Medical University, 2024, 45(1): 73-77. doi: 10.12259/j.issn.2095-610X.S20240106
[2]	Xiaoxiao ZHANG, Lulu LIU, Feng YAN, Jinlan YANG, Zhiqiang WANG, Meili HU. Analysis of Results from AI-Assisted Cervical Cancer Screening for Rural Women in Baoding City. Journal of Kunming Medical University, 2024, 45(10): 135-139. doi: 10.12259/j.issn.2095-610X.S20241021
[3]	Yiwen ZHANG, Ximing WANG, Zilong LI, Xinzhang ZHANG, Changxian CHEN, Weijun LIU, Zhenyong ZHANG. Research Progress of Artificial Intelligence in the Diagnosis and Treatment of Anorectal Diseases. Journal of Kunming Medical University, 2024, 45(2): 1-6. doi: 10.12259/j.issn.2095-610X.S20240201
[4]	Cheng PAN, Rongshuang YAN, Derui FEI, Xingfei DAI, Qitang LI, Yujian MA, Ying ZHANG. Accuracy of Sub-axial Cervical Pedicle Screw Placement：A Systematic Review with the Meta-analysis Comparing Free-hand and Assistant Techniques. Journal of Kunming Medical University, 2023, 44(9): 32-38. doi: 10.12259/j.issn.2095-610X.S20230910
[5]	Zhibiao CHEN, Qiuxiang HUANG, Lihua MAO, Guoyong CHEN, Yun LIU, Zhihong WANG. Application of Pre-implantation Genetic Testing in the Patients with Turner Syndrome. Journal of Kunming Medical University, 2023, 44(12): 100-105. doi: 10.12259/j.issn.2095-610X.S20231216
[6]	Yun BAI, Lanping ZHONG, Tingting ZHANG, Yuerong WU, Lei LI. Analysis of the Influence of Maternal Systemic Factors on Endometrial Implantation Window in Women with Repeated Implantation Failure. Journal of Kunming Medical University, 2022, 43(7): 74-79. doi: 10.12259/j.issn.2095-610X.S20220701
[7]	Chao-qun WANG, Jie NIU, Ping JIANG, Rong-xia HUANG, Jian CHEN, Li-yan MENG. Clinical Observation of Assisted Reproduction Intrauterine and Extrauterine Combined Pregnancy Operation in 32 Cases. Journal of Kunming Medical University, 2022, 43(1): 1-5. doi: 10.12259/j.issn.2095-610X.S20220125
[8]	Zhao Man Lin , Li Qing , Xu Wei Biao , Huang Qin , Wei Dao . . Journal of Kunming Medical University, 2020, 41(02): 110-114.
[9]	Ma Li Mei , Zhang Qing Wen , Li Ping , Yuan Yun , Guo Xiao Bing . . Journal of Kunming Medical University, 2019, 40(02): 128-132.
[10]	Zhang Zun Yue , Li Ping , Tang Li , Wang Kun Hua , Long Yan Xi , Wang Hua Wei . . Journal of Kunming Medical University, 2019, 40(01): 118-122.
[11]	Luo Xi , Wu Ze , Li Yong Gang , Dong Yun Hua , Gao Meng Ying , Deng Lian , Xiang Li Feng , Li Ming Ying . . Journal of Kunming Medical University, 2018, 39(10): 72-78.
[12]	Xiang Li Feng , Wang Qing Qing , Li Ming Ying , Zeng Peng , Li Yong Gang . A Study on Methylation Status of Imprinted Gene H19 and PEG1 in Different Quality of Sperms and Embryos. Journal of Kunming Medical University, 2018, 39(06): 101-104.
[13]	Zhao Yu , Yuan Jing , Liu Jian Jun , Lu Lin . Comparison of Proliferation between Fetal Forebrain-and Spinal Cord-Derived Neural Stem Cells in Mice. Journal of Kunming Medical University, 2017, 38(09): 19-23.
[14]	Song Jia Mei , Chen Hui , Xu Pei Qi , Meng Yu Shi . Effect of Female Basal Testosterone Level on the Pregnancy Outcome of in Vitro Fertilization Embryo Transfer. Journal of Kunming Medical University, 2017, 38(07): 93-98.
[15]	Li Yong Gang . Vertical Transmission of Hepatitis B Virus in Human Early Embryos. Journal of Kunming Medical University,
[16]	Guo Hong Liang . . Journal of Kunming Medical University,
[17]	Yang Xiao Ling . . Journal of Kunming Medical University,
[18]	. The Clinical Application of Electrocardiograph Modulated Milliampere in CT Coronary Artery Imaging. Journal of Kunming Medical University,
[19]	. Chromosomal Analysis in Spontaneous Abortion during the First Trimester after Assisted Reproduction Technique Treatment. Journal of Kunming Medical University,
[20]	Cao Yue Ling . . Journal of Kunming Medical University,