A Methodological Framework for the Development and Validation of Reliable Artificial Intelligence in Embryo Evaluation Article Swipe
Daniella Gilboa
,
Akhil Garg
,
Maya Shapiro
,
Marcos Meseguer
,
Y Amar
,
Nicole Lustgarten
,
Nina Desai
,
Tal Shavit
,
Vanessa Ferreira Belo da Silva
,
Achilleas Papatheodorou
,
Alexia Chatziparasidou
,
Sameer Angras
,
Jae Ho Lee
,
L. Thiel
,
Carol Lynn Curchoe
,
Y Tauber
,
Daniel S. Seidman
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5438430/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5438430/v1
Background Artificial intelligence (AI) models analyzing embryo time-lapse images have been developed to predict the likelihood of pregnancy following in vitro fertilization (IVF). However, limited research exists on methods ensuring AI consistency and reliability in clinical settings during its development and validation process. We present a methodology for developing and validating an AI model across multiple datasets to demonstrate reliable performance in evaluating blastocyst-stage embryos. Methods This multicenter analysis utilizes time-lapse images, pregnancy outcomes, and morphologic annotations from embryos collected at 10 IVF clinics across 9 countries between 2018 and 2022. The four-step methodology for developing and evaluating the AI model include: (I) curating annotated datasets that represent the intended clinical use case; (II) developing and optimizing the AI model; (III) evaluating the AI’s performance by assessing its discriminative power and associations with pregnancy probability across variable data; and (IV) ensuring interpretability and explainability by correlating AI scores with relevant morphologic features of embryo quality. Three datasets were used: the training and validation dataset (n = 16,935 embryos), the blind test dataset (n = 1,708 embryos; 3 clinics), and the independent dataset (n = 7,445 embryos; 7 clinics) derived from previously unseen clinic cohorts. Results The AI was designed as a deep learning classifier ranking embryos by score according to their likelihood of clinical pregnancy. Higher AI score brackets were associated with increased fetal heartbeat (FH) likelihood across all evaluated datasets, showing a trend of increasing odds ratios (OR). The highest OR was observed in the top G4 bracket (test dataset G4 score ≥ 7.5: OR 3.84; independent dataset G4 score ≥ 7.5: OR 4.01), while the lowest was in the G1 bracket (test dataset G1 score < 4.0: OR 0.40; independent dataset G1 score < 4.0: OR 0.45). AI score brackets G2, G3, and G4 displayed OR values above 1.0 (P < 0.05), indicating linear associations with FH likelihood. Average AI scores were consistently higher for FH-positive than for FH-negative embryos within each age subgroup. Positive correlations were also observed between AI scores and key morphologic parameters used to predict embryo quality. Conclusions Strong AI performance across multiple datasets demonstrates the value of our four-step methodology in developing and validating the AI as a reliable adjunct to embryo evaluation.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-5438430/v1
- OA Status
- gold
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405478578
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4405478578Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-5438430/v1Digital Object Identifier
- Title
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A Methodological Framework for the Development and Validation of Reliable Artificial Intelligence in Embryo EvaluationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-12-17Full publication date if available
- Authors
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Daniella Gilboa, Akhil Garg, Maya Shapiro, Marcos Meseguer, Y Amar, Nicole Lustgarten, Nina Desai, Tal Shavit, Vanessa Ferreira Belo da Silva, Achilleas Papatheodorou, Alexia Chatziparasidou, Sameer Angras, Jae Ho Lee, L. Thiel, Carol Lynn Curchoe, Y Tauber, Daniel S. SeidmanList of authors in order
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https://doi.org/10.21203/rs.3.rs-5438430/v1Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.21203/rs.3.rs-5438430/v1Direct OA link when available
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Interpretability, Artificial intelligence, Discriminative model, Machine learning, Computer scienceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 47 |
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