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'''Embryo Ranking Intelligent Classification Algorithm''' ('''ERICA''') is a deep learning<ref>{{cite web |title=P-413 - DEEP LEARNING FOR AUTOMATIC DETERMINATION OF BLASTOCYST EMBRYO DEVELOPMENT STAGE |url=https://www.onlineevent.com/asrm/events/1598/video_presentations/145039 |website=onlineevent.com }}</ref> AI software designed to assist embryologists and clinicians during the embryo selection process leading to embryo transfer,<ref>{{cite journal |last1=Chavez-Badiola |first1=Alejandro |last2=Flores-Saiffe Farias |first2=Adolfo |last3=Mendizabal-Ruiz |first3=Gerardo |last4=Garcia-Sanchez |first4=Rodolfo |last5=Drakeley |first5=Andrew J. |title=Development and preliminary validation of an automated static digital image analysis system utilizing machine learning for blastocyst selection |journal=Fertility and Sterility |date=September 2019 |volume=112 |issue=3 |pages=e149–e150 |doi=10.1016/j.fertnstert.2019.07.511 }}</ref> a critical step of [[in vitro fertilisation]] treatments (IVF).
'''Embryo Ranking Intelligent Classification Algorithm''' ('''ERICA''') is a deep learning<ref>{{cite web |title=P-413 - DEEP LEARNING FOR AUTOMATIC DETERMINATION OF BLASTOCYST EMBRYO DEVELOPMENT STAGE |url=https://www.onlineevent.com/asrm/events/1598/video_presentations/145039 |website=onlineevent.com }}</ref> AI software designed to assist embryologists and clinicians during the embryo selection process leading to embryo transfer,<ref>{{cite journal |last1=Chavez-Badiola |first1=Alejandro |last2=Flores-Saiffe Farias |first2=Adolfo |last3=Mendizabal-Ruiz |first3=Gerardo |last4=Garcia-Sanchez |first4=Rodolfo |last5=Drakeley |first5=Andrew J. |title=Development and preliminary validation of an automated static digital image analysis system utilizing machine learning for blastocyst selection |journal=Fertility and Sterility |date=September 2019 |volume=112 |issue=3 |pages=e149–e150 |doi=10.1016/j.fertnstert.2019.07.511 }}</ref> a critical step of [[in vitro fertilisation]] treatments (IVF).


This AI-based software relies on [[Computer vision|artificial vision]] to extract features not identifiable with the use of conventional microscopy.<ref>{{cite journal |last1=Chavez-Badiola |first1=Alejandro |last2=Flores-Saiffe Farias |first2=Adolfo |last3=Mendizabal-Ruiz |first3=Gerardo |last4=Drakeley |first4=Andrew J. |last5=Garcia-Sánchez |first5=Rodolfo |last6=Zhang |first6=John J. |title=Artificial vision and machine learning designed to predict PGT-A results |journal=Fertility and Sterility |date=September 2019 |volume=112 |issue=3 |pages=e231 |doi=10.1016/j.fertnstert.2019.07.715 }}</ref> Following feature extraction, ERICA accurately ranks embryos according to their prognosis (defined as euploidy and implantation potential). In this way, ERICA removes the subjectivity inherent to previously existing classifications and, by efficiently assisting clinicians, increases the chances of selecting the one embryo with the best chances to become a baby.<ref>{{cite journal |last1=Chavez-Badiola |first1=Alejandro |last2=Flores-Saiffe Farias |first2=Adolfo |last3=Mendizabal-Ruiz |first3=Gerardo |last4=Garcia-Sanchez |first4=Rodolfo |last5=Drakeley |first5=Andrew J. |last6=Garcia-Sandoval |first6=Juan Paulo |title=Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning |journal=Scientific Reports |date=10 March 2020 |volume=10 |issue=1 |doi=10.1038/s41598-020-61357-9 }}</ref>
This AI-based software relies on [[Computer vision|artificial vision]] to extract features not identifiable with the use of conventional microscopy.<ref>{{cite journal |last1=Chavez-Badiola |first1=Alejandro |last2=Flores-Saiffe Farias |first2=Adolfo |last3=Mendizabal-Ruiz |first3=Gerardo |last4=Drakeley |first4=Andrew J. |last5=Garcia-Sánchez |first5=Rodolfo |last6=Zhang |first6=John J. |title=Artificial vision and machine learning designed to predict PGT-A results |journal=Fertility and Sterility |date=September 2019 |volume=112 |issue=3 |pages=e231 |doi=10.1016/j.fertnstert.2019.07.715 }}</ref> Following feature extraction, ERICA accurately ranks embryos according to their prognosis (defined as euploidy and implantation potential). In this way, ERICA removes the subjectivity inherent to previously existing classifications and, by efficiently assisting clinicians, increases the chances of selecting the one embryo with the best chances to become a baby.<ref>{{cite journal |last1=Chavez-Badiola |first1=Alejandro |last2=Flores-Saiffe Farias |first2=Adolfo |last3=Mendizabal-Ruiz |first3=Gerardo |last4=Garcia-Sanchez |first4=Rodolfo |last5=Drakeley |first5=Andrew J. |last6=Garcia-Sandoval |first6=Juan Paulo |title=Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning |journal=Scientific Reports |date=10 March 2020 |volume=10 |issue=1 |pages=4394 |doi=10.1038/s41598-020-61357-9 |pmid=32157183 |pmc=7064494 |bibcode=2020NatSR..10.4394C }}</ref>


ERICA's algorithms and the EmbryoRanking.com associated software are cloud-based and base their ranking system on predicting individual embryo's genetic status in a non-invasive fashion.<ref>{{cite journal |last1=Chavez-Badiola |first1=Alejandro |last2=Mendizabal-Ruiz |first2=Gerardo |last3=Ocegueda-Hernandez |first3=Vladimir |last4=Flores-Saiffe Farias |first4=Adolfo |last5=Drakeley |first5=Andrew J. |title=Deep learning for automatic determination of blastocyst embryo development stage |journal=Fertility and Sterility |date=September 2019 |volume=112 |issue=3 |pages=e273 |doi=10.1016/j.fertnstert.2019.07.809 }}</ref><ref>https://www.machinelearning.ai/artificial-intelligence/presenting-erica-an-artificial-intelligence-clinical-assistant-for-embryo-ranking/</ref>
ERICA's algorithms and the EmbryoRanking.com associated software are cloud-based and base their ranking system on predicting individual embryo's genetic status in a non-invasive fashion.<ref>{{cite journal |last1=Chavez-Badiola |first1=Alejandro |last2=Mendizabal-Ruiz |first2=Gerardo |last3=Ocegueda-Hernandez |first3=Vladimir |last4=Flores-Saiffe Farias |first4=Adolfo |last5=Drakeley |first5=Andrew J. |title=Deep learning for automatic determination of blastocyst embryo development stage |journal=Fertility and Sterility |date=September 2019 |volume=112 |issue=3 |pages=e273 |doi=10.1016/j.fertnstert.2019.07.809 }}</ref><ref>https://www.machinelearning.ai/artificial-intelligence/presenting-erica-an-artificial-intelligence-clinical-assistant-for-embryo-ranking/</ref>

Revision as of 15:49, 17 April 2020

Embryo Ranking Intelligent Classification Algorithm (ERICA) is a deep learning[1] AI software designed to assist embryologists and clinicians during the embryo selection process leading to embryo transfer,[2] a critical step of in vitro fertilisation treatments (IVF).

This AI-based software relies on artificial vision to extract features not identifiable with the use of conventional microscopy.[3] Following feature extraction, ERICA accurately ranks embryos according to their prognosis (defined as euploidy and implantation potential). In this way, ERICA removes the subjectivity inherent to previously existing classifications and, by efficiently assisting clinicians, increases the chances of selecting the one embryo with the best chances to become a baby.[4]

ERICA's algorithms and the EmbryoRanking.com associated software are cloud-based and base their ranking system on predicting individual embryo's genetic status in a non-invasive fashion.[5][6]

See also

References

  1. ^ "P-413 - DEEP LEARNING FOR AUTOMATIC DETERMINATION OF BLASTOCYST EMBRYO DEVELOPMENT STAGE". onlineevent.com.
  2. ^ Chavez-Badiola, Alejandro; Flores-Saiffe Farias, Adolfo; Mendizabal-Ruiz, Gerardo; Garcia-Sanchez, Rodolfo; Drakeley, Andrew J. (September 2019). "Development and preliminary validation of an automated static digital image analysis system utilizing machine learning for blastocyst selection". Fertility and Sterility. 112 (3): e149 – e150. doi:10.1016/j.fertnstert.2019.07.511.
  3. ^ Chavez-Badiola, Alejandro; Flores-Saiffe Farias, Adolfo; Mendizabal-Ruiz, Gerardo; Drakeley, Andrew J.; Garcia-Sánchez, Rodolfo; Zhang, John J. (September 2019). "Artificial vision and machine learning designed to predict PGT-A results". Fertility and Sterility. 112 (3): e231. doi:10.1016/j.fertnstert.2019.07.715.
  4. ^ Chavez-Badiola, Alejandro; Flores-Saiffe Farias, Adolfo; Mendizabal-Ruiz, Gerardo; Garcia-Sanchez, Rodolfo; Drakeley, Andrew J.; Garcia-Sandoval, Juan Paulo (10 March 2020). "Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning". Scientific Reports. 10 (1): 4394. Bibcode:2020NatSR..10.4394C. doi:10.1038/s41598-020-61357-9. PMC 7064494. PMID 32157183.
  5. ^ Chavez-Badiola, Alejandro; Mendizabal-Ruiz, Gerardo; Ocegueda-Hernandez, Vladimir; Flores-Saiffe Farias, Adolfo; Drakeley, Andrew J. (September 2019). "Deep learning for automatic determination of blastocyst embryo development stage". Fertility and Sterility. 112 (3): e273. doi:10.1016/j.fertnstert.2019.07.809.
  6. ^ https://www.machinelearning.ai/artificial-intelligence/presenting-erica-an-artificial-intelligence-clinical-assistant-for-embryo-ranking/
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