Press

Grants and Recognitions

• State of California CASCADE Grant

Accelerator Programs

• Ad Astra Bootcamp
  • Meet the Ad Astra Spring 2020 Cohort
  • San Diego Metro Reviews the Ad Astra Graduates
  • Forbes on Programs Supporting AI Startups

Media Features

• Denver Albrecht Women on ART Compass in the Clinic
• Digital Health Buzz: AI for IVF
• Digital Quality Control and Assurance for the IVF Lab
• Quality Control and Assurance for IVF in The Cloud
• Scientific American Blog Post on AI for IVF
• Guest Blog Post on Embryo Freezing
• Meet Your Embryo’s First Babysitter

Podcast Appearances

• Talent Finders – Kerrin Black Interview
• Interview with Alice Crisci of MedAnswers
• If These Ovaries Could Talk Podcast
• Recurring Guest on BEAT INFERTILITY PODCAST
  • Inside the IVF Lab
  • Artificial Intelligence in Assisted Reproduction
  • Epigenetics 101
  • Evaluating Your IVF Lab
• Recurring Guest on The Egg Whisperer
• Recurring Guest on LORI METZ PODCAST
  • What Goes On in the Lab and the Impact of COVID-19
  • ABCs of Creating an Embryo in the Lab with Carol Lynn Curchoe

Research and Publications

• Peer-Reviewed Data Publications
  • Dimitriadis, I., Bormann, C. L., & Sakkas, D. (2019). Artificial intelligence-enabled system for embryo classification and selection based on image analysis. Fertility and Sterility, 112(4), e19. https://doi.org/10.1016/j.fertnstert.2019.07.54
  • Khosravi, P., Kazemi, E., Zhan, Q., Malmsten, J. E., Toschi, M., Zisimopoulos, P., Sigaras, A., Lavery, S., Cozzubbo, T., Rosenwaks, Z., Elemento, O., & Zaninovic, N. (2019). Improved monitoring of human embryo culture conditions using a deep learning-based image analysis system. Fertility and Sterility, 112(4), e52. https://doi.org/10.1016/j.fertnstert.2019.07.200
  • Khosravi, P., Kazemi, E., Zhan, Q., Malmsten, J. E., Toschi, M., Zisimopoulos, P., Sigaras, A., Lavery, S., Cozzubbo, T., Rosenwaks, Z., Elemento, O., & Zaninovic, N. (2019). Automated quality assessment of individual embryologists performing ICSI using deep learning-enabled fertilization and embryo grading technology. Fertility and Sterility, 112(4), e53. https://doi.org/10.1016/j.fertnstert.2019.07.201
• Abstracts Presented at Scientific Symposia
  • Curchoe, C. L., & Bormann, C. L. (2018). Artificial intelligence and machine learning in reproductive medicine and embryology: A systematic review. Fertility and Reproductive Medicine, Abstract 32. http://cme-utilities.com/mailshotcme/FRM/2018/Abstracts/32.pdf

  • Khosravi, P., Kazemi, E., Zhan, Q., Malmsten, J. E., Toschi, M., Zisimopoulos, P., Sigaras, A., Lavery, S., Cozzubbo, T., Rosenwaks, Z., Elemento, O., & Zaninovic, N. (2019). Improved monitoring of human embryo culture conditions using a deep learning-based image analysis system. Fertility and Sterility, 112(4), e52. https://doi.org/10.1016/j.fertnstert.2019.07.200

  • Khosravi, P., Kazemi, E., Zhan, Q., Malmsten, J. E., Toschi, M., Zisimopoulos, P., Sigaras, A., Lavery, S., Cozzubbo, T., Rosenwaks, Z., Elemento, O., & Zaninovic, N. (2019). Automated quality assessment of individual embryologists performing ICSI using deep learning-enabled fertilization and embryo grading technology. Fertility and Sterility, 112(4), e53. https://doi.org/10.1016/j.fertnstert.2019.07.201

  • Curchoe, C. L., & Bormann, C. L. (2019). Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. Journal of Assisted Reproduction and Genetics, 36(4), 591–600. https://doi.org/10.1007/s10815-019-01408-x