The Influence Of Intrauterine Pressure On Embryo Retention In A Catheter After Embryo Transfer

Kozikowska, Małgorzata; Grusza, Mirosław; Mrugacz, Grzegorz; Gagan, Jerzy; Zbucka-Krętowska, Monika; Grygoruk, Cezary

doi:10.1038/s41598-019-48077-5

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…The exact mechanism of embryo retention has not been explained. Therefore, Kozikowska et al's study aimed to investigate the mechanism of embryo retention in the catheter during embryo transfer by using a transparent uterus model equipped with pressure sensors and a video recorder 22 . Their results indicated that pressure changes in the uterine cavity during ET can influence the distribution of the transferred fluid containing the embryo.…”

Section: Retention Of Embryos At 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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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: Retention Of Embryos At 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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One step further: randomised single-centre trial comparing the direct and afterload techniques of embryo transfer

et al. 2021

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STUDY QUESTION What are the differences in ease of use between two different embryo transfer (ET) techniques: the preload direct approach and the afterload approach. SUMMARY ANSWER The afterload technique seems to reduce the rate of difficult ETs. WHAT IS KNOWN ALREADY Numerous published trials now document that the ET procedure has an impact on pregnancy and delivery rates after IVF. Difficult transfers should be avoided, as they reduce implantation and pregnancy rates. Preload direct ETs with soft catheters under ultrasound guidance is currently considered the best procedure. However, when using soft catheters, it is not known which technique is preferable or which one should be implemented to reduce the operator factor. STUDY DESIGN, SIZE, DURATION This prospective randomised unblinded controlled clinical trial, included 352 ultrasound-guided ETs assigned to either direct ET or afterload ET, between September 2017 and March 2019. The sample size was calculated based on the historical rate of difficult ETs encountered between 2014 and 2015 with a direct ET procedure. PARTICIPANTS/MATERIALS, SETTING, METHODS The inclusion criteria were women 18–38 years old, with BMI between 18 and 28, receiving a single-thawed blastocyst transfer. The exclusion criteria were use of testicular sperm and preimplantation genetic testing (PGT) cycles. The primary outcome was the rate of difficult or suboptimal transfers defined as: advancement of the outer sheath (specific for the direct transfer), multiple attempts, use of force, required manipulation, use of a stylet or tenaculum, dilatation, or use of a different catheter. The secondary outcome was clinical pregnancy rate. MAIN RESULTS AND THE ROLE OF CHANCE A total of 352 frozen ETs were randomised, with 176 patients in each group. The two arms were homogeneous for female and male age, female BMI, duration of infertility, secondary infertility, previous deliveries or miscarriages, myomas, previous surgery to the uterine cavity, cycle day at ovulation trigger, freeze all cycles, first transfers, indication for treatment, endometrial preparation protocol and duration, endometrial thickness, and blastocyst grade at vitrification. Across the entire population, 85 (24.1%) ETs were defined as difficult. The rate of difficult transfers was significantly higher in the direct ET group than in the afterload group: 68 (38.6%) versus 17 (9.7%), respectively (OR 0.17, 95% CI 0.09–0.30, P < 0.001). The mean percentage in the rate of difficult transfers per operator was 22.5% (SD ± 14.5%), of which 36.1% (SD ± 23.4%) were in the direct group compared with 8.6% (± 8.2%) in the afterload group (P < 0.001). The difficult transfer rate among operators varied from 0 to 43.8% (0–77.8% in the direct group and 0 to 25.0% in the afterload group). The clinical pregnancy rates (42.0% vs 48.3%, P = 0.239 in the direct and afterload groups, respectively) were not significantly different between the groups. LIMITATIONS, REASONS FOR CAUTION There were 18 experienced operators who participated in the trial. Conclusions about the pregnancy rate should not be generalised, since the sample analysis was not performed on this outcome and, although clinically relevant, the difference was not significantly different. WIDER IMPLICATIONS OF THE FINDINGS The rate of difficult transfers was significantly higher in the direct ET group compared with the afterload ET group, although a wide variation was observed among operators. Further studies regarding the association between transfer technique and ART outcomes are required. STUDY FUNDING/COMPETING INTEREST(S) No specific funding was sought and there are no competing interests. TRIAL REGISTRATION NUMBER NCT03161119. TRIAL REGISTRATION DATE 5 April 2017. DATE OF FIRST PATIENT'S ENROLMENT 26 September 2017.

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Biomedical Catheters With Integrated Miniature Piezoresistive Pressure Sensors: A Review

Meena

Sankar

2021

IEEE Sensors J.

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The Influence Of Intrauterine Pressure On Embryo Retention In A Catheter After Embryo Transfer

Cited by 5 publications

References 32 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)

One step further: randomised single-centre trial comparing the direct and afterload techniques of embryo transfer

Biomedical Catheters With Integrated Miniature Piezoresistive Pressure Sensors: A Review

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