Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis
Artificial intelligence (AI) has been gaining support in the field of in vitro fertilization (IVF). Despite the promising existing data, AI cannot yet claim gold-standard status, which serves as the rationale for this study. This systematic review and data synthesis aims to evaluate and report on th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/10/3/697 |
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| author | Konstantinos Sfakianoudis Evangelos Maziotis Sokratis Grigoriadis Agni Pantou Georgia Kokkini Anna Trypidi Polina Giannelou Athanasios Zikopoulos Irene Angeli Terpsithea Vaxevanoglou Konstantinos Pantos Mara Simopoulou |
| author_facet | Konstantinos Sfakianoudis Evangelos Maziotis Sokratis Grigoriadis Agni Pantou Georgia Kokkini Anna Trypidi Polina Giannelou Athanasios Zikopoulos Irene Angeli Terpsithea Vaxevanoglou Konstantinos Pantos Mara Simopoulou |
| author_sort | Konstantinos Sfakianoudis |
| collection | DOAJ |
| description | Artificial intelligence (AI) has been gaining support in the field of in vitro fertilization (IVF). Despite the promising existing data, AI cannot yet claim gold-standard status, which serves as the rationale for this study. This systematic review and data synthesis aims to evaluate and report on the predictive capabilities of AI-based prediction models regarding IVF outcome. The study has been registered in PROSPERO (CRD42021242097). Following a systematic search of the literature in Pubmed/Medline, Embase, and Cochrane Central Library, 18 studies were identified as eligible for inclusion. Regarding live-birth, the Area Under the Curve (AUC) of the Summary Receiver Operating Characteristics (SROC) was 0.905, while the partial AUC (pAUC) was 0.755. The Observed: Expected ratio was 1.12 (95%CI: 0.26–2.37; 95%PI: 0.02–6.54). Regarding clinical pregnancy with fetal heartbeat, the AUC of the SROC was 0.722, while the pAUC was 0.774. The O:E ratio was 0.77 (95%CI: 0.54–1.05; 95%PI: 0.21–1.62). According to this data synthesis, the majority of the AI-based prediction models are successful in accurately predicting the IVF outcome regarding live birth, clinical pregnancy, clinical pregnancy with fetal heartbeat, and ploidy status. This review attempted to compare between AI and human prediction capabilities, and although studies do not allow for a meta-analysis, this systematic review indicates that the AI-based prediction models perform rather similarly to the embryologists’ evaluations. While AI models appear marginally more effective, they still have some way to go before they can claim to significantly surpass the clinical embryologists’ predictive competence. |
| first_indexed | 2024-03-09T20:05:10Z |
| format | Article |
| id | doaj.art-cd4d332b8c214785b994e80050c7da8a |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-09T20:05:10Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-cd4d332b8c214785b994e80050c7da8a2023-11-24T00:33:49ZengMDPI AGBiomedicines2227-90592022-03-0110369710.3390/biomedicines10030697Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data SynthesisKonstantinos Sfakianoudis0Evangelos Maziotis1Sokratis Grigoriadis2Agni Pantou3Georgia Kokkini4Anna Trypidi5Polina Giannelou6Athanasios Zikopoulos7Irene Angeli8Terpsithea Vaxevanoglou9Konstantinos Pantos10Mara Simopoulou11Centre for Human Reproduction, Genesis Athens Clinic, 14-16 Papanikoli, 15232 Athens, GreeceDepartment of Physiology, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias, 11527 Athens, GreeceDepartment of Physiology, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias, 11527 Athens, GreeceCentre for Human Reproduction, Genesis Athens Clinic, 14-16 Papanikoli, 15232 Athens, GreeceDepartment of Physiology, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias, 11527 Athens, GreeceDepartment of Physiology, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias, 11527 Athens, GreeceCentre for Human Reproduction, Genesis Athens Clinic, 14-16 Papanikoli, 15232 Athens, GreeceObstetrics and Gynaecology Royal Cornwall Hospital, Treliske, Truro TR1 3LQ, UKCentre for Human Reproduction, Genesis Athens Clinic, 14-16 Papanikoli, 15232 Athens, GreeceCentre for Human Reproduction, Genesis Athens Clinic, 14-16 Papanikoli, 15232 Athens, GreeceCentre for Human Reproduction, Genesis Athens Clinic, 14-16 Papanikoli, 15232 Athens, GreeceDepartment of Physiology, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias, 11527 Athens, GreeceArtificial intelligence (AI) has been gaining support in the field of in vitro fertilization (IVF). Despite the promising existing data, AI cannot yet claim gold-standard status, which serves as the rationale for this study. This systematic review and data synthesis aims to evaluate and report on the predictive capabilities of AI-based prediction models regarding IVF outcome. The study has been registered in PROSPERO (CRD42021242097). Following a systematic search of the literature in Pubmed/Medline, Embase, and Cochrane Central Library, 18 studies were identified as eligible for inclusion. Regarding live-birth, the Area Under the Curve (AUC) of the Summary Receiver Operating Characteristics (SROC) was 0.905, while the partial AUC (pAUC) was 0.755. The Observed: Expected ratio was 1.12 (95%CI: 0.26–2.37; 95%PI: 0.02–6.54). Regarding clinical pregnancy with fetal heartbeat, the AUC of the SROC was 0.722, while the pAUC was 0.774. The O:E ratio was 0.77 (95%CI: 0.54–1.05; 95%PI: 0.21–1.62). According to this data synthesis, the majority of the AI-based prediction models are successful in accurately predicting the IVF outcome regarding live birth, clinical pregnancy, clinical pregnancy with fetal heartbeat, and ploidy status. This review attempted to compare between AI and human prediction capabilities, and although studies do not allow for a meta-analysis, this systematic review indicates that the AI-based prediction models perform rather similarly to the embryologists’ evaluations. While AI models appear marginally more effective, they still have some way to go before they can claim to significantly surpass the clinical embryologists’ predictive competence.https://www.mdpi.com/2227-9059/10/3/697artificial intelligenceIVFdata-synthesis |
| spellingShingle | Konstantinos Sfakianoudis Evangelos Maziotis Sokratis Grigoriadis Agni Pantou Georgia Kokkini Anna Trypidi Polina Giannelou Athanasios Zikopoulos Irene Angeli Terpsithea Vaxevanoglou Konstantinos Pantos Mara Simopoulou Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis Biomedicines artificial intelligence IVF data-synthesis |
| title | Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis |
| title_full | Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis |
| title_fullStr | Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis |
| title_full_unstemmed | Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis |
| title_short | Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis |
| title_sort | reporting on the value of artificial intelligence in predicting the optimal embryo for transfer a systematic review including data synthesis |
| topic | artificial intelligence IVF data-synthesis |
| url | https://www.mdpi.com/2227-9059/10/3/697 |
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