Determination of Cut Off for Endometrial Thickness in Couples with Unexplained Infertility: Trustable AI

Mehrjerd, Ameneh; Rezaei, Hassan; Eslami, Saeid; Ghaebi, Nayyere Khadem

doi:10.3233/shti220450

Cited by 3 publications

(3 citation statements)

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“…Mehrjerd et al aimed to analyze the impact of endometrial thickness on the ongoing pregnancy rate in couples with unexplained infertility using deep machine learning & artificial intelligence based algorithms [13]. They obtained a 7.7 mm cut-off point for IUI and 9.99 mm for IVF/ICSI treatment.…”

Section: Implantation Prediction Algorithms Studying the Endometriummentioning

confidence: 99%

In Contemporary Reproductive Medicine Human Beings are Not Yet Dispensable

Allahbadia

Gupta³

2023

J Obstet Gynecol India

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In the past few years almost every aspect of an IVF cycle has been investigated, including research on sperm, color doppler in follicular studies, prediction of embryo cleavage, prediction of blastocyst formation, scoring blastocyst quality, prediction of euploid blastocysts and live birth from blastocysts, improving the embryo selection process, and for developing deep machine learning (ML) algorithms for optimal IVF stimulation protocols. Also, artificial intelligence (AI)-based methods have been implemented for some clinical aspects of IVF, such as assessing patient reproductive potential and individualizing gonadotropin stimulation protocols. As AI has the inherent capacity to analyze "Big" data, the goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data to provide patient-tailored individualized treatments. Human skillsets including hand eye coordination to perform an embryo transfer is probably 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 programed 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 before and during the procedure of embryo transfer. In contemporary Reproductive Medicine human beings are not yet dispensable.

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Section: Implantation Prediction Algorithms Studying the Endometriummentioning

confidence: 99%

In Contemporary Reproductive Medicine Human Beings are Not Yet Dispensable

Allahbadia

Gupta³

2023

J Obstet Gynecol India

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“…Despite extensive studies on endometrial thickness prediction, research is still needed. Mehrjerd et al aimed to analyze the impact of endometrial thickness on the ongoing pregnancy rate in couples with unexplained infertility using deep machine learning & artificial intelligence based algorithms 36 . A total of 729 couples with unexplained infertility were included in this study.…”

Section: Deep Machine Learning Aided Implantation Prediction Algorith...mentioning

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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“…In a randomized controlled study conducted by Quaas et al, although it was thought that increased endometrial thickness was associated with high live birth rates, it was reported that there were significant live pregnancy rates even in thin endometrium and that the cycle should not be canceled due to thin endometrium (18). Nonetheless, another studystated that endometrial thickness was important for achieving viable pregnancy, and in patients with unexplained infertility, the optimal thickness for IUI treatment was considered to be 7.7 mm (19). A meta-analysis that was conducted revealed that there was no connection between endometrial thickness and pregnancy rates in IUI cycles (20).…”

Section: Characteristicmentioning

confidence: 99%

Açıklanamayan İnfertilitede IUI Tedavisinde GND ve Klomifen Sitrat İle Ovulasyon İndüksiyonu İle Gelişen Preovulatuar Folikül Sayısının Gebelik Oranlarına Etkisi

HALİLZADE,

KAHYAOĞLU,

HALİLZADE

et al. 2023

Jinekoloji-Obstetrik Ve Neonatoloji Tıp Dergisi

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Aim: The aim of this study is to investigate whether the number of dominant follicles formed in IUI with ovarian stimulation (OS) affects pregnancy outcomes in couples with unexplained infertility. Material and Method: 217 couples aged 24-39 years with unexplained infertility and treated with IUI by way of OS participated in our study. The patients were divided into 2 groups of which the first included the clinically pregnant and the second did not, and were compared in terms of age, monofollicle and bifollicle development, infertility duration, treatment initiation day, hcg day, leading follicle diameter, endometrial thickness, number of dominant follicles and treatment type. Results: Of the 217 patients who underwent IUI for unexplained infertility, no significant difference was found between the groups with and without clinical pregnancy in terms of the number of dominant follicles (p=0.73). There was a positive but low correlation between endometrial thickness and treatment success (Spearman's correlation coefficient value = 0.14, p=0.03). The receiver operating characteristic curve analysis revealed that the optimal cut-off value for endometrial thickness to predict pregnancy success was 9.5 mm (55.0% sensitivity, 65.0% specificity). Conclusions: As a result, we suggest that gonadotropins are not superior to oral agents in terms of clinical pregnancy rates in unexplained infertility, therefore treatment should commence with less invasive and less costly oral agents. Furthermore, we think that the number of dominant follicles (monofollicles or bifollicles) created by ovarian stimulation does not affect clinical pregnancy success, but endometrial thickness is closely related to pregnancy rates and deserves attention.

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Determination of Cut Off for Endometrial Thickness in Couples with Unexplained Infertility: Trustable AI

Cited by 3 publications

References 0 publications

In Contemporary Reproductive Medicine Human Beings are Not Yet Dispensable

In Contemporary Reproductive Medicine Human Beings are Not Yet Dispensable

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

Açıklanamayan İnfertilitede IUI Tedavisinde GND ve Klomifen Sitrat İle Ovulasyon İndüksiyonu İle Gelişen Preovulatuar Folikül Sayısının Gebelik Oranlarına Etkisi

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