Accuracy and efficacy of embryo transfer based on the previous measurement of cervical length and total uterine length

Bakas, Panagiotis; Simopoulou, Mara; Giner, Maria; Tzanakaki, Despina; Deligeoroglou, Eythimios

doi:10.1007/s00404-018-4971-6

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…Primary outcome was to estimate the accuracy of embryo transfer based on the measurement of the embryo distance from middle of uterine cavity after embryo transfer and secondary outcome was to assess the effect of embryo distance from uterine fundus and internal cervical os to clinical pregnancy rate. The study concluded that ET by a single operator with the previous measurement of utero-cervical length and estimation of embryo transfer position will be very accurate 16 .…”

Section: Measurement Of Utero-cervical Length Before Etmentioning

confidence: 90%

“…Bakas et al examined the accuracy of embryo transfer based on the previous measurement of the utero-cervical length 16 . All patients had transvaginal ultrasound measurement of utero-cervical length prior to embryo transfer and measurement of embryo distance (intrauterine air bubbles) from fundal surface of uterine cavity and internal cervical os immediately after embryo transfer.…”

Section: Measurement Of Utero-cervical Length Before Etmentioning

confidence: 99%

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Embryo Transfer is the last Frontier for Deep Machine Learning & Artificial Intelligence in Medically Assisted Reproduction (MAR)

Allahbadia

2023

TJOR

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Embryo Transfer is the last Frontier for Deep Machine Learning & Artificial Intelligence in Medically Assisted Reproduction (MAR). In the last five years, nearly every aspect of an IVF cycle has been investigated by artificial intelligence(AI) & deep machine learning (ML), including sperm morphology, sperm identification, identification of empty or oocyte containing follicles, prediction of embryo cell stages, prediction of blastulation from oocytes, scoring blastocyst quality, prediction of euploid blastocysts and live birth from blastocysts, improving the embryo selection process, and for developing algorithms for optimal IVF stimulation protocols. Moreover, AI-based methods can be implemented for other clinical aspects of IVF, such as assessing patient reproductive potential and individualizing gonadotropin stimulation protocols. As AI has the capability to analyze "big" data, the ultimate goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data in an effort to provide patient-tailored treatments. Embryo Transfer is the only step of IVF that is outside the realm of AI & ML today. Embryo Transfer success is presently human skill dependent and deep machine learning may one day intrude into this sacred space with the advent of specialized humanoid robots. Embryo transfer is arguably the rate limiting step in the sequential events that complete an IVF cycle. Many variables play a role in the success of embryo transfer, including catheter type, atraumatic technique, and the use of sonography guidance. In this clinical review we will cover the contemporary research goals of AI & ML as well as the variables influencing Embryo Transfer success.

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Section: Measurement Of Utero-cervical Length Before Etmentioning

confidence: 90%

Section: Measurement Of Utero-cervical Length Before Etmentioning

confidence: 99%

Embryo Transfer is the last Frontier for Deep Machine Learning & Artificial Intelligence in Medically Assisted Reproduction (MAR)

Allahbadia

2023

TJOR

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“…Bakas doesn't agree with the need of a 2 nd operator, as he believes that the embryo transfer can be done with precision by a single operator if the total measurement of the uterus has been determined beforehand with therefore an accurate estimate of the place of embryo deposit [9].…”

Section: Interest Of the Mock Transfermentioning

confidence: 99%

Is the Mock Transfer Enough Reliable to Evaluate Hysterometry for Embryo Transfer?

Ecra¹,

Nigué²,

Kourouma³

2023

OJOG

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Objectives: The aim of this study was to assess the reliability of the mock transfer during in vitro fertilization process Method: A case-control study was conducted on 134 patients included in IVF/ICSI cycles. From the hysterometry obtained during the mock transfer, the ideal embryo replacement site, i.e. two cm from the uterine fundus has been determined. Results: Significant differences were noted between the area estimated from the mock transfer and the area where the embryo was deposited during the actual embryo transfer. In fact, 15.9% of the patients had a difference between four and six cm, and 32% of the patients returning for a subsequent transfer had at least 2 cm of difference between the embryo deposit zones. This difference was significant (P < 0.00) Conclusion: More than ultrasound guidance, the challenge with embryo transfer is to be able to minimize variations in the length of the uterus. This would make it possible to determine the ideal transfer depot area without multiple manipulations.

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Embryo transfer

Inaudi

Scarselli²

2023

Management of Infertility

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Accuracy and efficacy of embryo transfer based on the previous measurement of cervical length and total uterine length

Cited by 4 publications

References 22 publications

Embryo Transfer is the last Frontier for Deep Machine Learning & Artificial Intelligence in Medically Assisted Reproduction (MAR)

Embryo Transfer is the last Frontier for Deep Machine Learning & Artificial Intelligence in Medically Assisted Reproduction (MAR)

Is the Mock Transfer Enough Reliable to Evaluate Hysterometry for Embryo Transfer?

Embryo transfer

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