Utilization of standardized preimplantation genetic testing for aneuploidy (PGT-A) via artificial intelligence (AI) technology is correlated with improved pregnancy outcomes in single thawed euploid embryo transfer (STEET) cycles
Purpose To investigate the role of standardized preimplantation genetic testing for aneuploidy (PGT-A) using artificial intelligence (AI) in patients undergoing single thawed euploid embryo transfer (STEET) cycles. Methods Retrospective cohort study at a single, large university-based fertility cent...
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| Veröffentlicht in: | Journal of assisted reproduction and genetics 2023-02, Vol.40 (2), p.289-299 |
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| creator | Buldo-Licciardi, Julia Large, Michael J. McCulloh, David H. McCaffrey, Caroline Grifo, James A. |
| description | Purpose
To investigate the role of standardized preimplantation genetic testing for aneuploidy (PGT-A) using artificial intelligence (AI) in patients undergoing single thawed euploid embryo transfer (STEET) cycles.
Methods
Retrospective cohort study at a single, large university-based fertility center with patients undergoing in vitro fertilization (IVF) utilizing PGT-A from February 2015 to April 2020. Controls included embryos tested using subjective NGS. The first experimental group included embryos analyzed by NGS utilizing AI and machine learning (PGTai
SM
Technology Platform, AI 1.0). The second group included embryos analyzed by AI 1.0 and SNP analysis (PGTai2.0, AI 2.0). Primary outcomes included rates of euploidy, aneuploidy and simple mosaicism. Secondary outcomes included rates of implantation (IR), clinical pregnancy (CPR), biochemical pregnancy (BPR), spontaneous abortion (SABR) and ongoing pregnancy and/or live birth (OP/LBR).
Results
A total of 24,908 embryos were analyzed, and classification rates using AI platforms were compared to subjective NGS. Overall, those tested via AI 1.0 showed a significantly increased euploidy rate (36.6% vs. 28.9%), decreased simple mosaicism rate (11.3% vs. 14.0%) and decreased aneuploidy rate (52.1% vs. 57.0%). Overall, those tested via AI 2.0 showed a significantly increased euploidy rate (35.0% vs. 28.9%) and decreased simple mosaicism rate (10.1% vs. 14.0%). Aneuploidy rate was insignificantly decreased when comparing AI 2.0 to NGS (54.8% vs. 57.0%). A total of 1,174 euploid embryos were transferred. The OP/LBR was significantly higher in the AI 2.0 group (70.3% vs. 61.7%). The BPR was significantly lower in the AI 2.0 group (4.6% vs. 11.8%).
Conclusion
Standardized PGT-A via AI significantly increases euploidy classification rates and OP/LBR, and decreases BPR when compared to standard NGS. |
| doi_str_mv | 10.1007/s10815-022-02695-7 |
| format | Article |
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To investigate the role of standardized preimplantation genetic testing for aneuploidy (PGT-A) using artificial intelligence (AI) in patients undergoing single thawed euploid embryo transfer (STEET) cycles.
Methods
Retrospective cohort study at a single, large university-based fertility center with patients undergoing in vitro fertilization (IVF) utilizing PGT-A from February 2015 to April 2020. Controls included embryos tested using subjective NGS. The first experimental group included embryos analyzed by NGS utilizing AI and machine learning (PGTai
SM
Technology Platform, AI 1.0). The second group included embryos analyzed by AI 1.0 and SNP analysis (PGTai2.0, AI 2.0). Primary outcomes included rates of euploidy, aneuploidy and simple mosaicism. Secondary outcomes included rates of implantation (IR), clinical pregnancy (CPR), biochemical pregnancy (BPR), spontaneous abortion (SABR) and ongoing pregnancy and/or live birth (OP/LBR).
Results
A total of 24,908 embryos were analyzed, and classification rates using AI platforms were compared to subjective NGS. Overall, those tested via AI 1.0 showed a significantly increased euploidy rate (36.6% vs. 28.9%), decreased simple mosaicism rate (11.3% vs. 14.0%) and decreased aneuploidy rate (52.1% vs. 57.0%). Overall, those tested via AI 2.0 showed a significantly increased euploidy rate (35.0% vs. 28.9%) and decreased simple mosaicism rate (10.1% vs. 14.0%). Aneuploidy rate was insignificantly decreased when comparing AI 2.0 to NGS (54.8% vs. 57.0%). A total of 1,174 euploid embryos were transferred. The OP/LBR was significantly higher in the AI 2.0 group (70.3% vs. 61.7%). The BPR was significantly lower in the AI 2.0 group (4.6% vs. 11.8%).
Conclusion
Standardized PGT-A via AI significantly increases euploidy classification rates and OP/LBR, and decreases BPR when compared to standard NGS.</description><identifier>ISSN: 1058-0468</identifier><identifier>ISSN: 1573-7330</identifier><identifier>EISSN: 1573-7330</identifier><identifier>DOI: 10.1007/s10815-022-02695-7</identifier><identifier>PMID: 36609941</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Aneuploidy ; Artificial Intelligence ; Assisted Reproduction Technologies ; Blastocyst ; Classification ; Embryo transfer ; Embryos ; Female ; Fertility ; Fertilization in Vitro ; Genetic screening ; Genetic Testing ; Gynecology ; Human Genetics ; Humans ; Implantation ; In vitro fertilization ; Medicine ; Medicine & Public Health ; Mosaicism ; Pregnancy ; Pregnancy Outcome ; Preimplantation Diagnosis ; Reproductive Medicine ; Retrospective Studies ; Single Embryo Transfer ; Single-nucleotide polymorphism</subject><ispartof>Journal of assisted reproduction and genetics, 2023-02, Vol.40 (2), p.289-299</ispartof><rights>The Author(s) 2023</rights><rights>2023. The Author(s).</rights><rights>The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-88f0319655b46f055500c573466f8d2a8079e44f6d07565fe565982b80202e6f3</citedby><cites>FETCH-LOGICAL-c474t-88f0319655b46f055500c573466f8d2a8079e44f6d07565fe565982b80202e6f3</cites><orcidid>0000-0001-8222-9439</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9935782/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9935782/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,41464,42533,51294,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36609941$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Buldo-Licciardi, Julia</creatorcontrib><creatorcontrib>Large, Michael J.</creatorcontrib><creatorcontrib>McCulloh, David H.</creatorcontrib><creatorcontrib>McCaffrey, Caroline</creatorcontrib><creatorcontrib>Grifo, James A.</creatorcontrib><title>Utilization of standardized preimplantation genetic testing for aneuploidy (PGT-A) via artificial intelligence (AI) technology is correlated with improved pregnancy outcomes in single thawed euploid embryo transfer (STEET) cycles</title><title>Journal of assisted reproduction and genetics</title><addtitle>J Assist Reprod Genet</addtitle><addtitle>J Assist Reprod Genet</addtitle><description>Purpose
To investigate the role of standardized preimplantation genetic testing for aneuploidy (PGT-A) using artificial intelligence (AI) in patients undergoing single thawed euploid embryo transfer (STEET) cycles.
Methods
Retrospective cohort study at a single, large university-based fertility center with patients undergoing in vitro fertilization (IVF) utilizing PGT-A from February 2015 to April 2020. Controls included embryos tested using subjective NGS. The first experimental group included embryos analyzed by NGS utilizing AI and machine learning (PGTai
SM
Technology Platform, AI 1.0). The second group included embryos analyzed by AI 1.0 and SNP analysis (PGTai2.0, AI 2.0). Primary outcomes included rates of euploidy, aneuploidy and simple mosaicism. Secondary outcomes included rates of implantation (IR), clinical pregnancy (CPR), biochemical pregnancy (BPR), spontaneous abortion (SABR) and ongoing pregnancy and/or live birth (OP/LBR).
Results
A total of 24,908 embryos were analyzed, and classification rates using AI platforms were compared to subjective NGS. Overall, those tested via AI 1.0 showed a significantly increased euploidy rate (36.6% vs. 28.9%), decreased simple mosaicism rate (11.3% vs. 14.0%) and decreased aneuploidy rate (52.1% vs. 57.0%). Overall, those tested via AI 2.0 showed a significantly increased euploidy rate (35.0% vs. 28.9%) and decreased simple mosaicism rate (10.1% vs. 14.0%). Aneuploidy rate was insignificantly decreased when comparing AI 2.0 to NGS (54.8% vs. 57.0%). A total of 1,174 euploid embryos were transferred. The OP/LBR was significantly higher in the AI 2.0 group (70.3% vs. 61.7%). The BPR was significantly lower in the AI 2.0 group (4.6% vs. 11.8%).
Conclusion
Standardized PGT-A via AI significantly increases euploidy classification rates and OP/LBR, and decreases BPR when compared to standard NGS.</description><subject>Aneuploidy</subject><subject>Artificial Intelligence</subject><subject>Assisted Reproduction Technologies</subject><subject>Blastocyst</subject><subject>Classification</subject><subject>Embryo transfer</subject><subject>Embryos</subject><subject>Female</subject><subject>Fertility</subject><subject>Fertilization in Vitro</subject><subject>Genetic screening</subject><subject>Genetic Testing</subject><subject>Gynecology</subject><subject>Human Genetics</subject><subject>Humans</subject><subject>Implantation</subject><subject>In vitro fertilization</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Mosaicism</subject><subject>Pregnancy</subject><subject>Pregnancy Outcome</subject><subject>Preimplantation Diagnosis</subject><subject>Reproductive Medicine</subject><subject>Retrospective Studies</subject><subject>Single Embryo Transfer</subject><subject>Single-nucleotide polymorphism</subject><issn>1058-0468</issn><issn>1573-7330</issn><issn>1573-7330</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9UstuEzEUHSEQLYEfYIEssUkWA56H7ZkNUlSlpVIlkEjXluOxJ64cO9ieVNOv6bf0Z_gNLkwojwULP6R77jnXxyfLXhf4XYExex8L3BQkx2UJi7YkZ0-y04KwKmdVhZ_CHZMmxzVtTrIXMd5gjNumrJ5nJxWluG3r4jT7dp2MNXciGe-Q1ygm4ToROnOnOrQPyuz2Vrg01XvlVDISJRWTcf3Dvfbh4V44NeytN92I5p8v1vlygQ5GIBGS0UYaYZFxSVlroF0qNF9eLoBBbp23vh-RiUj6EJQVCSRvTdoiEA3-MA3QO-HkiPyQpN-pCFwogrZVKG3FLWCO4kjtNmH0KAXholYBzb-sV6v1AslRWhVfZs-0sFG9Op6z7Pp8tT77mF99urg8W17lsmZ1yptG46poKSGbmmpMCMFYgqM1pbrpStFg1qq61rTDjFCiFWzg6abBJS4V1dUs-zDx7ofNTnVSORjI8n0wOxFG7oXhf1ec2fLeH3jbVoTB78yy-ZEg-K8DGM13JkqwD2z2Q-Qlo0XbkKYuAPr2H-iNH4KD5wGKsYKWtMKAKieUDD7GoPTjMAXmP3LEpxxxyBH_mSPOoOnNn894bPkVHABUEyBCyfUq_Nb-D-13dz3aLg</recordid><startdate>20230201</startdate><enddate>20230201</enddate><creator>Buldo-Licciardi, Julia</creator><creator>Large, Michael J.</creator><creator>McCulloh, David H.</creator><creator>McCaffrey, Caroline</creator><creator>Grifo, James A.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-8222-9439</orcidid></search><sort><creationdate>20230201</creationdate><title>Utilization of standardized preimplantation genetic testing for aneuploidy (PGT-A) via artificial intelligence (AI) technology is correlated with improved pregnancy outcomes in single thawed euploid embryo transfer (STEET) cycles</title><author>Buldo-Licciardi, Julia ; Large, Michael J. ; McCulloh, David H. ; McCaffrey, Caroline ; Grifo, James A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-88f0319655b46f055500c573466f8d2a8079e44f6d07565fe565982b80202e6f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Aneuploidy</topic><topic>Artificial Intelligence</topic><topic>Assisted Reproduction Technologies</topic><topic>Blastocyst</topic><topic>Classification</topic><topic>Embryo transfer</topic><topic>Embryos</topic><topic>Female</topic><topic>Fertility</topic><topic>Fertilization in Vitro</topic><topic>Genetic screening</topic><topic>Genetic Testing</topic><topic>Gynecology</topic><topic>Human Genetics</topic><topic>Humans</topic><topic>Implantation</topic><topic>In vitro fertilization</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Mosaicism</topic><topic>Pregnancy</topic><topic>Pregnancy Outcome</topic><topic>Preimplantation Diagnosis</topic><topic>Reproductive Medicine</topic><topic>Retrospective Studies</topic><topic>Single Embryo Transfer</topic><topic>Single-nucleotide polymorphism</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Buldo-Licciardi, Julia</creatorcontrib><creatorcontrib>Large, Michael J.</creatorcontrib><creatorcontrib>McCulloh, David H.</creatorcontrib><creatorcontrib>McCaffrey, Caroline</creatorcontrib><creatorcontrib>Grifo, James A.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of assisted reproduction and genetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Buldo-Licciardi, Julia</au><au>Large, Michael J.</au><au>McCulloh, David H.</au><au>McCaffrey, Caroline</au><au>Grifo, James A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Utilization of standardized preimplantation genetic testing for aneuploidy (PGT-A) via artificial intelligence (AI) technology is correlated with improved pregnancy outcomes in single thawed euploid embryo transfer (STEET) cycles</atitle><jtitle>Journal of assisted reproduction and genetics</jtitle><stitle>J Assist Reprod Genet</stitle><addtitle>J Assist Reprod Genet</addtitle><date>2023-02-01</date><risdate>2023</risdate><volume>40</volume><issue>2</issue><spage>289</spage><epage>299</epage><pages>289-299</pages><issn>1058-0468</issn><issn>1573-7330</issn><eissn>1573-7330</eissn><abstract>Purpose
To investigate the role of standardized preimplantation genetic testing for aneuploidy (PGT-A) using artificial intelligence (AI) in patients undergoing single thawed euploid embryo transfer (STEET) cycles.
Methods
Retrospective cohort study at a single, large university-based fertility center with patients undergoing in vitro fertilization (IVF) utilizing PGT-A from February 2015 to April 2020. Controls included embryos tested using subjective NGS. The first experimental group included embryos analyzed by NGS utilizing AI and machine learning (PGTai
SM
Technology Platform, AI 1.0). The second group included embryos analyzed by AI 1.0 and SNP analysis (PGTai2.0, AI 2.0). Primary outcomes included rates of euploidy, aneuploidy and simple mosaicism. Secondary outcomes included rates of implantation (IR), clinical pregnancy (CPR), biochemical pregnancy (BPR), spontaneous abortion (SABR) and ongoing pregnancy and/or live birth (OP/LBR).
Results
A total of 24,908 embryos were analyzed, and classification rates using AI platforms were compared to subjective NGS. Overall, those tested via AI 1.0 showed a significantly increased euploidy rate (36.6% vs. 28.9%), decreased simple mosaicism rate (11.3% vs. 14.0%) and decreased aneuploidy rate (52.1% vs. 57.0%). Overall, those tested via AI 2.0 showed a significantly increased euploidy rate (35.0% vs. 28.9%) and decreased simple mosaicism rate (10.1% vs. 14.0%). Aneuploidy rate was insignificantly decreased when comparing AI 2.0 to NGS (54.8% vs. 57.0%). A total of 1,174 euploid embryos were transferred. The OP/LBR was significantly higher in the AI 2.0 group (70.3% vs. 61.7%). The BPR was significantly lower in the AI 2.0 group (4.6% vs. 11.8%).
Conclusion
Standardized PGT-A via AI significantly increases euploidy classification rates and OP/LBR, and decreases BPR when compared to standard NGS.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>36609941</pmid><doi>10.1007/s10815-022-02695-7</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-8222-9439</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Springer Nature - Complete Springer Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Aneuploidy Artificial Intelligence Assisted Reproduction Technologies Blastocyst Classification Embryo transfer Embryos Female Fertility Fertilization in Vitro Genetic screening Genetic Testing Gynecology Human Genetics Humans Implantation In vitro fertilization Medicine Medicine & Public Health Mosaicism Pregnancy Pregnancy Outcome Preimplantation Diagnosis Reproductive Medicine Retrospective Studies Single Embryo Transfer Single-nucleotide polymorphism |
| title | Utilization of standardized preimplantation genetic testing for aneuploidy (PGT-A) via artificial intelligence (AI) technology is correlated with improved pregnancy outcomes in single thawed euploid embryo transfer (STEET) cycles |
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