Intrauterine insemination (IUI) is a commonly used non-invasive and affordable procedure to treat infertility caused by different underlying causes such as male subfertility, unexplained infertility, ovulatory dysfunction, and cervical factor infertility [16]. The overall success rate of IUI varies, with pregnancy rates ranging from as low as 2.7% to as high as 66% [17]. Despite the improvements in semen preparation and controlled ovarian stimulation techniques, the success rates reported for IUI are lower than the rates reported for other ART procedures [18]. Data from the European Society of Human Reproduction and Embryology indicates that the pregnancy rate per IUI cycle has remained stable for many years at about 12% [19,20,21]. Several prognostic factors that help determine the IUI treatment outcome have been identified including the woman’s age, cause and duration of infertility, mature follicle number, endometrial thickness, number of sperm inseminated, sperm morphology, and progressive motile sperm count [18]. In the present study, we evaluated the impact of multiple baseline parameters which might affect the IUI success rate at the beginning of the cycle, without including the intra-cycle characteristics, to devise a method to provide an individualized infertility treatment plan and a proper counseling regarding the chance of achieving pregnancy. The purpose was to select the couples with higher success probability for IUI treatment and use other ART methods for those individuals with less success probability. This strategy would help us to shorten the time to pregnancy (TTP) as an extremely important goal for every modern infertility clinic. Considering multiple prognostic factors for IUI success, machine learning, and artificial intelligence (AI) showed promising results in selecting the best candidates for IUI in order to optimize TTP.
Today, the use of machine learning techniques due to their superior performance compared to other statistical methods in predicting, modeling, and classifying biomedical systems has increasingly attracted the attention of medical researchers. Logistics and linear regression methods are not able to classify nonlinear and complex problems. Although machine learning techniques including neural networks are widely used in medical sciences, their most significant success has been in diagnosis and predicting the treatment results, including the predicting the success of infertility treatments. At present, machine learning algorithms in the field of infertility are commonly used to predict the success of IVF/ART. This may be due to the high cost and more sophisticated IVF techniques compared to IUI.
Wołczyński et al. in a retrospective study including 1007 infertility treatment cycles among 899 patients undergoing IVF/ICSI/ET designed a three-layer neural network that included 45 neurons in the input layer, 14 neurons in the latent layer and a single output neuron to predict the results of the treatment cycles. Using their model, the treatment cycle outcomes were correctly predicted in 68.5% of cases. Pregnancy was accurately confirmed in 49.1% of cases and abortion in 86.5% of cases [22]. Also it was possible to predict the failure of treatment with almost 90% certainty. Vogiatzi et al. collected data from 257 infertile couples who were treated during 426 IVF/ICSI cycles from 2010 to 2017 and designed an artificial intelligence network. This model was able to predict the results of the treatment cycles with 76.7% sensitivity and 73.4% specificity [14]. To the best of our knowledge, no previous study has been published on the performance of artificial intelligence to predict the success of IUI, and our study is the first report in this field. Trial and error in performing IUI without the help of robust predictor algorithms may lead to high financial costs, wasting the time, and psychological crises for infertile couples, so the study of machine learning methods can be very helpful.
We also determined the importance of each possible prognostic factor on IUI success rate. In our study, among all the baseline parameters, sperm characteristics (normal morphology, total motility, and total progressive motility) had the highest impact on conceiving chance after IUI. In agreement with our results, Butcher et al., Pereira et al., and Nikbakht also found that sperm morphology and progressive motility play an important role in IUI success rate [23,24,25]. One of the earliest studies on the sperm parameters’ predictive value on IUI success rate by Badawy et al. [26] reported that IUI has little chance of success when the number of motile spermatozoa inseminated is <5 × 10 [6] or normal sperm morphology is <30%. They also reported a lower chance of IUI success for women older than 35 years. In another study, Zadehmodarres et al. [27] concluded that IUI is a convenient and useful treatment option in women with younger age (<30 years), fewer treatment cycles, and lower infertility duration (4 years). Another study in Iran showed that total motile sperm count of 5×106 to < 10×106, normal sperm morphology of ≥ 5%, and number of motile sperm inseminated of ≥ 10×106 are useful prognostic factors for IUI success rate [24]. Bahadur et al. [28] emphasized that greater than 3 million progressive motile sperm in the insemination are related with higher IUI success rate.
In contrast to our findings, Sicchieri et al. [21] reported that female patient age was the only variable significantly correlated with IUI success rate and found no association between sperm progressive motility and pregnancy rate.
Recently, Hansen et al. performed a secondary analysis of 2462 IUI cycles from the Assessment of Multiple Intrauterine Gestations from Ovarian Stimulation (AMIGOS) clinical trial to assess the impact of some cycle characteristics of couples with unexplained infertility on live birth rate. They reported that patient discomfort during the IUI procedure was associated with a reduction in live birth rate. Also higher total motile sperm count (TMC) was associated with greater live birth rate and TMC of 15.1–20.0 million resulted in a 14.8% live birth rate, when TMC of ≤5 million resulted in only 5.5% live birth rate. They also found that most other factors associated with the performance of IUI were not significantly related to live birth rate [29]. Ainsworth et al. [3] in a retrospective cohort study aimed to define IUI cycle characteristics (female age, semen characteristics, and ovarian stimulation type) associated with viable birth. They reported that IUI is a futile treatment for women age > 43, regardless of stimulation type or inseminate motility (IM). Also very poor prognosis (viable birth rate < 5%) was reported among women who used oral medications or Clomid plus gonadotropins and were under 35 years old with IM < 49%, or between 35 and 37 years with IM < 56%, or over 38 years, and those women over 38 years who used gonadotropins only with IM < 60%. Their study also provided a nomogram to individualize counseling regarding the probability of a viable birth [3].
Based on our findings, AI is a superior tool to predict the IUI success with a good predictive value (which was more than 70% in the present study), since it utilizes multiple baseline male and female factors. Including multi-centric data from a larger group of patients and considering more possible prognostic factors might increase this predictive value in future studies. We think designing application based on machine learning can help infertility specialists to select the most appropriate patients for IUI treatment based on their personal characteristics and help to shorten the time interval to pregnancy (TTP) in future (Fig. 2).
