Study Protocol: Evaluation of AI-Driven Grading Compared to Manual Grading in Predicting Embryo Viability and Successful Implantation and Clinical Pregnancy Outcomes in IVF Using Static Microscopic Images
Background: Infertility affects 8–12% of couples globally, with 5–10% seeking Assisted Reproductive Technology (ART) annually. In-vitro fertilization (IVF) is the most common infertility treatment, involving the retrieval of oocytes and their fertilization in a laboratory setting. Embryo selection,...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2025-05-01
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| Series: | Journal of Pharmacy and Bioallied Sciences |
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| Online Access: | https://journals.lww.com/10.4103/jpbs.jpbs_386_25 |
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| author | Puja Dhamija Akash More Namrata Choudhary Tejaswini Wadhe Devanshi R. Shah |
| author_facet | Puja Dhamija Akash More Namrata Choudhary Tejaswini Wadhe Devanshi R. Shah |
| author_sort | Puja Dhamija |
| collection | DOAJ |
| description | Background:
Infertility affects 8–12% of couples globally, with 5–10% seeking Assisted Reproductive Technology (ART) annually. In-vitro fertilization (IVF) is the most common infertility treatment, involving the retrieval of oocytes and their fertilization in a laboratory setting. Embryo selection, crucial for IVF success, is traditionally performed manually by embryologists using the Gardner Scale. However, this process is subject to variability. Time-lapse microscopy and artificial intelligence (AI)-based methods are being explored for improved embryo selection, though AI’s full potential has not been realized across diverse clinical settings.
Objectives:
The primary objective of this study is to compare AI-based embryo grading with conventional manual grading by embryologists in predicting clinical pregnancy outcomes.
Methodology:
This prospective study will be conducted at an IVF clinic in Sawangi, Wardha, Maharashtra, involving 222 participants aged 23–40 years undergoing Intra-Cytoplasmic Sperm Injection (ICSI). Embryos on Day 5 (blastocyst stage) will be imaged and graded using Life Whisperer Genetics (LWG), an AI-based tool, and by skilled embryologists using the ASEBIR criteria. The success rate of clinical pregnancy, confirmed by the presence of a gestational sac, will be the primary outcome.
Expected Results:
The study is expected to show increased predictive efficiency, rigor, and consistency with AI-driven grading of Day 5 embryos, providing a more economical solution for IVF.
Study Implications:
This study focuses on enhancing embryo selection using Life Whisperer Genetics (LWG), which could potentially improve embryo assessment in IVF. Further research is needed to incorporate other embryonic developmental stages for a more comprehensive evaluation process. |
| format | Article |
| id | doaj-art-6d397690aeb0476d837f6aba90f4310f |
| institution | DOAJ |
| issn | 0976-4879 0975-7406 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Pharmacy and Bioallied Sciences |
| spelling | doaj-art-6d397690aeb0476d837f6aba90f4310f2025-08-20T03:07:49ZengWolters Kluwer Medknow PublicationsJournal of Pharmacy and Bioallied Sciences0976-48790975-74062025-05-0117Suppl 1S956S95910.4103/jpbs.jpbs_386_25Study Protocol: Evaluation of AI-Driven Grading Compared to Manual Grading in Predicting Embryo Viability and Successful Implantation and Clinical Pregnancy Outcomes in IVF Using Static Microscopic ImagesPuja DhamijaAkash MoreNamrata ChoudharyTejaswini WadheDevanshi R. ShahBackground: Infertility affects 8–12% of couples globally, with 5–10% seeking Assisted Reproductive Technology (ART) annually. In-vitro fertilization (IVF) is the most common infertility treatment, involving the retrieval of oocytes and their fertilization in a laboratory setting. Embryo selection, crucial for IVF success, is traditionally performed manually by embryologists using the Gardner Scale. However, this process is subject to variability. Time-lapse microscopy and artificial intelligence (AI)-based methods are being explored for improved embryo selection, though AI’s full potential has not been realized across diverse clinical settings. Objectives: The primary objective of this study is to compare AI-based embryo grading with conventional manual grading by embryologists in predicting clinical pregnancy outcomes. Methodology: This prospective study will be conducted at an IVF clinic in Sawangi, Wardha, Maharashtra, involving 222 participants aged 23–40 years undergoing Intra-Cytoplasmic Sperm Injection (ICSI). Embryos on Day 5 (blastocyst stage) will be imaged and graded using Life Whisperer Genetics (LWG), an AI-based tool, and by skilled embryologists using the ASEBIR criteria. The success rate of clinical pregnancy, confirmed by the presence of a gestational sac, will be the primary outcome. Expected Results: The study is expected to show increased predictive efficiency, rigor, and consistency with AI-driven grading of Day 5 embryos, providing a more economical solution for IVF. Study Implications: This study focuses on enhancing embryo selection using Life Whisperer Genetics (LWG), which could potentially improve embryo assessment in IVF. Further research is needed to incorporate other embryonic developmental stages for a more comprehensive evaluation process.https://journals.lww.com/10.4103/jpbs.jpbs_386_25artificial intelligenceassisted reproductive technologyembryo gradingembryo selectioninfertilityivflife whisperer geneticspregnancy outcomes |
| spellingShingle | Puja Dhamija Akash More Namrata Choudhary Tejaswini Wadhe Devanshi R. Shah Study Protocol: Evaluation of AI-Driven Grading Compared to Manual Grading in Predicting Embryo Viability and Successful Implantation and Clinical Pregnancy Outcomes in IVF Using Static Microscopic Images Journal of Pharmacy and Bioallied Sciences artificial intelligence assisted reproductive technology embryo grading embryo selection infertility ivf life whisperer genetics pregnancy outcomes |
| title | Study Protocol: Evaluation of AI-Driven Grading Compared to Manual Grading in Predicting Embryo Viability and Successful Implantation and Clinical Pregnancy Outcomes in IVF Using Static Microscopic Images |
| title_full | Study Protocol: Evaluation of AI-Driven Grading Compared to Manual Grading in Predicting Embryo Viability and Successful Implantation and Clinical Pregnancy Outcomes in IVF Using Static Microscopic Images |
| title_fullStr | Study Protocol: Evaluation of AI-Driven Grading Compared to Manual Grading in Predicting Embryo Viability and Successful Implantation and Clinical Pregnancy Outcomes in IVF Using Static Microscopic Images |
| title_full_unstemmed | Study Protocol: Evaluation of AI-Driven Grading Compared to Manual Grading in Predicting Embryo Viability and Successful Implantation and Clinical Pregnancy Outcomes in IVF Using Static Microscopic Images |
| title_short | Study Protocol: Evaluation of AI-Driven Grading Compared to Manual Grading in Predicting Embryo Viability and Successful Implantation and Clinical Pregnancy Outcomes in IVF Using Static Microscopic Images |
| title_sort | study protocol evaluation of ai driven grading compared to manual grading in predicting embryo viability and successful implantation and clinical pregnancy outcomes in ivf using static microscopic images |
| topic | artificial intelligence assisted reproductive technology embryo grading embryo selection infertility ivf life whisperer genetics pregnancy outcomes |
| url | https://journals.lww.com/10.4103/jpbs.jpbs_386_25 |
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