Active ovulation management increases the monthly probability of pregnancy occurrence in ovulatory women who receive intrauterine insemination

1987 ◽  
Vol 48 (6) ◽  
pp. 916-920 ◽  
Author(s):  
Ekkehard Kemmann ◽  
Michael Bohrer ◽  
Robert Shelden ◽  
Gary Fiasconaro ◽  
Leah Beardsley
Keyword(s):  
Intrauterine Insemination ◽  
Monthly Probability

1630: Sufficient Total Motile Sperm for Intrauterine Insemination is Easily Obtained by Assisted Ejaculation of Men with Spinal Cord Injury

2006 ◽  
Vol 175 (4S) ◽  
pp. 525-525
Author(s):  
Charles M. Lynne ◽  
Nancy L. Brackett ◽  
Teodoro C. Aballa ◽  
Emad Ibrahim ◽  
Apostolos Kafetsoulis
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Intrauterine Insemination ◽  
Motile Sperm ◽  
Cord Injury ◽  
Assisted Ejaculation

scholarly journals Controlled ovarian stimulation should not be preferred for male infertility treated with intrauterine insemination: a retrospective study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yan Tang ◽  
Qian-Dong He ◽  
Ting-Ting Zhang ◽  
Jing-Jing Wang ◽  
Si-Chong Huang ◽  
...  
Keyword(s):  
Ectopic Pregnancy ◽  
Pregnancy Rate ◽  
Ovarian Stimulation ◽  
Intrauterine Insemination ◽  
Multiple Pregnancy ◽  
Clinical Pregnancy ◽  
Male Factor Infertility ◽  
Male Factor ◽  
Ovulatory Follicles ◽  
Multiple Pregnancy Rate

Abstract Background Some studies have stated that intrauterine insemination (IUI) with controlled ovarian stimulation (COS) might increase the pregnancy rate, while others suggest that IUI in the natural cycle (NC) should be the first line of treatment. It remains unclear whether it is necessary to use COS at the same time when IUI is applied to treat isolated male factor infertility. Thus, we aimed to investigate efficacy of IUI with COS for isolated male factor infertility. Methods A total of 601 IUI cycles from 307 couples who sought medical care for isolated male factor infertility between January 2010 and February 2020 were divided into two groups: NC-IUI and COS-IUI. The COS-IUI group was further divided into two subgroups according to the number of pre-ovulatory follicles on the day of HCG: cycles with monofollicular development (one follicle group) and cycles with at least two pre-ovulatory follicles (≥ 2 follicles group). The IUI outcomes, including clinical pregnancy, live birth, spontaneous abortion, ectopic pregnancy, and multiple pregnancy rates were compared. Results The clinical pregnancy, live birth, spontaneous abortion, and ectopic pregnancy rates were comparable between the NC-IUI and COS-IUI group. Similar results were also observed among the NC-IUI, one follicle, and ≥ 2 follicles groups. However, with respect to the multiple pregnancy rate, a trend toward higher multiple pregnancy rate was observed in the COS-IUI group compared to the NC-IUI group (8.7% vs. 0, P = 0.091), and a significant difference was found between the NC-IUI and ≥ 2 follicles group (0 vs. 16.7%, P = 0.033). Conclusion In COS cycles, especially in those with at least two pre-ovulatory follicles, the multiple pregnancy rate increased without a substantial gain in overall pregnancy rate; thus, COS should not be preferred in IUI for isolated male factor infertility. If COS is required, one stimulated follicle and one healthy baby should be the goal considering the safety of both mothers and foetuses.

scholarly journals Laparotomic intrauterine insemination with frozen-thawed semen in Sopravissana breed ewes in Central Italy

2021 ◽  
Vol 20 (1) ◽  
pp. 928-934
Author(s):  
Lakamy Sylla ◽  
Alessia Pistolesi ◽  
Ivan Corsi ◽  
Martina Crociati ◽  
Giuseppe Stradaioli ◽  
...  
Keyword(s):  
Intrauterine Insemination ◽  
Central Italy

scholarly journals CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sima Ranjbari ◽  
Toktam Khatibi ◽  
Ahmad Vosough Dizaji ◽  
Hesamoddin Sajadi ◽  
Mehdi Totonchi ◽  
...  
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Semen Quality ◽  
Intrauterine Insemination ◽  
Machine Learning Algorithms ◽  
Semen Parameters ◽  
Feature Engineering ◽  
Operating Characteristics ◽  
Scoring Method ◽  
Novel Approach

Abstract Background Intrauterine Insemination (IUI) outcome prediction is a challenging issue which the assisted reproductive technology (ART) practitioners are dealing with. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. Many previous studies have been focused on predicting the in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) outcome using machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to propose an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking the most significant features. Methods For this purpose, a novel approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute describing 11,255 IUI treatment cycles for 8,360 couples. Our dataset includes the couples' demographic characteristics, historical data about the patients' diseases, the clinical diagnosis, the treatment plans and the prescribed drugs during the cycles, semen quality, laboratory tests and the clinical pregnancy outcome. Results Experimental results show that the proposed method outperforms the compared methods with Area under receiver operating characteristics curve (AUC) of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female body mass index (BMI).

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