Publications &
Conference Presentations

Bridging the gap between embryo euploidy, pregnancy potential and morphology using artificial intelligence for ploidy estimation: a retrospective evaluation

Giménez-Rodríguez C, Shapiro M, Tauber Y, Gilboa D, et al.

Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation

Gilboa D, Garg A, Shapiro M, Seidman DS, et al.

Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization (DISCOVER)

Rotem O, Schwartz T, Maor R, Gilboa D, Zaritsky A, et al.

Using unlabeled information of embryo siblings from the same cohort cycle to enhance in vitro fertilization implantation prediction

Tzukerman N, Rotem O, Shapiro MT, Gilboa D, Zaritsky A, et al.

Why do women choose to undergo oocyte aspiration without sedation or analgesia?

Gilboa D, Seidman L, Kimiagarov P, Seidman DS, et al.

Significant increase in serum hCG levels following methotrexate therapy is associated with lower treatment success rates in ectopic pregnancy patients

Bouaziz J, Mashiach R, Seidman DS, Gilboa D, Mazaki-Tovi S, et al.

Artificial Intelligence (AI) — Chapter 18

Gilboa D

In: Gardner DK, Weissman A, Howles CM, Shoham Z (Eds.), Textbook of Assisted Reproductive Techniques, Volume 1: Laboratory Perspectives, 6th Edition. Routledge.

AI-based deselection of at-risk aneuploid embryos using a validated score threshold and time-lapse imaging in IVF

Gilboa D, Tauber Y, Amar Y, Seidman DS, et al.

AI score dynamics from Day 3 to Day 5: stability vs. progression in predicting clinical outcomes in IVF embryos

Sisi P, Polia A, Sialakouma A, Gilboa D, et al.

Dual-stage AI scoring improves pregnancy prediction accuracy for IVF embryos

Efstathiou P, Zacharia S, Kyrli V, Gilboa D, Makrakis E, et al.

Machine learning outperforms blastocyst morphological scoring across time-lapse imaging systems, excelling in evaluating morphologically similar high-quality embryos

Karagianni A, Mastromina I, Mentorou C, Gilboa D, Makrakis E, et al.

Machine learning unveils consistent pregnancy predictions across maternal age in real-world IVF embryo selection

Kouini E, Anagnostara K, Giannelou P, Gilboa D, Makrakis E, et al.

AI-driven aneuploidy prediction: a non-invasive solution for safer and accurate embryo evaluation in fresh transfers

Asimakopoulou M, Nikiforaki D, Dinopoulou V, Gilboa D, Sfontouris I, et al.

Clinical application of an AI-based preimplantation genetic screening tool

Seidman DS, Shapiro M, Lustgarten N, Gilboa D, et al.

An AI-based screening test can reliably predict embryo ploidy

Seidman DS, Gilboa D, Tauber Y, Shapiro M, et al.

Simulated cohort ranking analysis estimates the number of IVF cycles needed to reach fetal heartbeat with and without AI embryo triage

Shapiro M, Amar Y, Tauber Y, Gilboa D, et al.

Artificial intelligence for embryo genetic risk assessment: a novel form of preimplantation genetic screening

Gilboa D, Tauber Y, Sayegh I, Seidman DS, et al.

A visual interpretability method to unbox "black-box" deep learning image-based classification of embryo properties

Rotem O, Gilboa D, Seidman DS, Schwartz T, et al.

Can computer vision identify features not visible to the human eye that can assist in non-invasively identifying aneuploid embryos?

Gilboa D, Meseguer M, Maor R, Harton G, et al.

Blastocyst "pumping" is a detrimental feature predicting implantation failure: highly accurate assessment by computer vision analysis of time-lapse videos

Gilboa D, Seidman DS, Kedar L, Maor R, Goldberg JM, Desai N, et al.

Can computer vision algorithms noninvasively recognize aneuploidy in blastocysts? "Pumping" appears to be a strong predictive feature

Meseguer M, Bori L, Maor R, Kedar L, Desai N, Gilboa D, et al.

An artificial neural network is capable of accurately identifying blastocysts within the culture well

Gilboa D, Maor R, Bori L, Meseguer M, et al.

Could the EMA artificial neural network grade blastocysts as an embryologist?

Bori L, Gilboa D, Maor R, Viloria T, Kottel I, Seidman DS, Meseguer M, et al.

Artificial intelligence is moving closer to reproductive medicine: prediction of blastulation and embryo implantation

Bori L, Maor R, Meseguer F, Kottel I, Seidman DS, Gilboa D, Meseguer M, et al.

In-depth analysis of embryo development: differences among monosomic, trisomic and chromosomally chaotic embryos compared to euploid embryos

Meseguer F, Bori L, Maor R, Kottel I, Gilboa D, Seidman DS, et al.

Automated halo identification: a novel predictive feature for IVF success identified through an artificial intelligence algorithm

Meseguer M, Maor RU, Alegre L, Del Gallego R, Pellicer A, Seidman DS, Gilboa D, et al.

Chair-Elect, Artificial Intelligence Special Interest Group (AI SIG)

American Society for Reproductive Medicine (ASRM)

Elected to lead the global committee responsible for defining guidelines, education, and ethical standards for AI adoption in fertility medicine.

Member, Technology Committee

American Society for Reproductive Medicine (ASRM)

Strategic advisor on the evaluation, integration, and clinical impact of emerging technologies in reproductive medicine.