Ensemble Machine Learning Models for Evaluation of Sperm Quality with Respect to Success Rate of Clinical Pregnancy in IVF, ICSI, and IUI Methods

Ensemble Machine Learning Models for Evaluation of Sperm Quality with Respect to Success Rate of Clinical Pregnancy in IVF, ICSI, and IUI Methods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Ensemble Machine Learning Models for Evaluation of Sperm Quality with Respect to Success Rate of Clinical Pregnancy in IVF, ICSI, and IUI Methods Ameneh Mehrjerd, Toktam Dehghani, Saeid Eslami, Mahdiyeh Jajroudi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2481505/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Objective: Evaluation of the effect of sperm quality on the success rate of clinical pregnancy and the possibility of infertility. The primary objective was to determine the success rate of clinical pregnancy (CPR). The secondary objective was to evaluate the clinical pregnancy rate (FHR). Method: This retrospective study evaluated 1929 couples who were treated with In Vitro Fertilization (IVF), in Intracytoplasmic Sperm Injection (ICSI), and Intrauterine Insemination (IUI) was conducted in two infertility centers; while data from donated eggs or sperm and a surrogate uterus along with data from infertile couples with a combination of male and female factors were excluded. In this study, five ensemble machine-learning models were utilized to predict the success rate of clinical pregnancy. Results: Among the proposed ensemble models, the Random Forest (RF) model achieved the highest mean accuracy and area under the curve (AUC) and outperformed all other models in three procedures. Our results show that in cycles with 1 to 5 retrieved eggs, sperm motility and the count of sperm had a positive effect on the rate of clinical pregnancy. Furthermore, the results indicated that cut-off values of 54 (p-value=0.02, 95%-CIs (1.05, 2.13)) and 35 (p-value=0.03, 95% 95%-CIs (1.06, 2.86)) for the count parameter in IVF/ICSI, and IUI, respectively. In addition, a significant cut-off points of 30 (p-value < 0.001) was obtained for the morphology parameter in all procedures. Sperm parameters were negatively weighted in the model obtained by the RF. In addition, the acquired data illustrated that in each procedure, the morphology parameter demonstrated a significant difference in clinical pregnancy between successful and unsuccessful groups. Conclusion: The second course of IVF procedure increased success rates in clinical pregnancy in patients with lower-than-average sperm parameters, while the IUI technique was demonstrated to be more effective in patients with above-average of sperm parameters. Health sciences/Medical research Health sciences/Signs and symptoms/Reproductive signs and symptoms Infertility Clinical Pregnancy Sperm Quality IVF ICSI IUI Machine learning algorithms Random Forest. Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Infertility is an issue that affects couples whose pregnancy fails after 12 months of unprotected sexual activity. The infertility prevalence rate is 15–20% and almost 40–50% of all cases are caused only by the male factor 1 – 3 . Assisted Reproduction Technology (ART) treatments are defined as medical procedures aiming to achieve pregnancy, but their success is influenced by a myriad of intrinsic and extrinsic factors. There are numerous ART treatments available, including IUI (intrauterine insemination), IVF (in vitro fertilization), and intracytoplasmic sperm injection (ICSI) 4 . IUI is the first-line treatment option and also the most cost-effective strategy for males with unexplained or mild infertility which is defined as a single abnormal finding of the semen analysis or a total motile sperm count between 10–20 × 106/mL 5 . However, patients with infertility using the IVF method have a total fertilization failure rate of 5% of IVF cycles 6 . Many variable parameters are considered to predict effective methods and select a treatment that is suitable for each infertile couple. Female and male factors are both considered. Male factors such as concentration, morphology, motility, volume, total number, and vitality are used to represent abnormal sperm in male infertility 1 , 7 , whereas female parameters such as type and duration of infertility, number of mature follicles, endometrial thickness, and various seminal parameters 5 are used to represent abnormal sperm in female infertility. According to previous research, these factors are the source of standard laboratories in sperm analysis, and "normal" or "reference" values as cut-offs have a limitation and are difficult to understand 7 . In 2014, a systematic review reported that total motile sperm count (> 1 million) and morphology are possible predictive variables for IUI success, however, the quality of evidence was low 5 , 8 . Various investigations have shown different outcomes. One study published in 2019 showed that prewash total motile sperm is a poor predictor of live births in IUI cycles, with no correlation between live births and prewash total motile sperm counts of 2 million sperm 9 . Another study in 2021 revealed that the relation between IUI and prediction variables in patients with unexplained infertility was not statistical significance. Also, the difference between quartiles of total progressive motile sperm count (TPMSC) with live birth rate and clinical pregnancy rate was not statistical significance whether the patient was older than 40 or the TPMSC less than 10%. Moreover, they reported only one clinical pregnancy and no live birth 10 . Regarding the IVF study in 2020 that represented male partners with total sperm count greater than 5 million, there was no correlation between low total testosterone levels and semen parameter changes. Also in 2020, the importance of sperm quality in the efficacy of assisted reproduction IVF procedure was emphasized, and higher prewash total sperm count values had a positive impact on cumulative success rates in cycles with few retrieved oocytes (1 to 5), but had no effect on the outcome of cycles with a normal (6 to 10) or high (> 10) number of retrieved oocytes 4 . In this paper, different ensemble models of machine learning were developed to predict the possibility of clinical pregnancy as well as infertility based on the effect of sperm parameters. The primary objective is to determine the success rate of clinical pregnancy (CPR) refers to a positive beta test or gestational sec in ultrasonography in the fourth week of pregnancy. The secondary objective is to evaluate the clinical pregnancy rate (FHR) refers to the fetal heart rate in the eleventh week of pregnancy (FH). 2. Materials And Methods 2.1. Data collecting and preprocessing In this study, we analyzed three widely used treatment methods, IVF, ICSI and IUI, and the data was collected from two universities, an affiliated infertility center and a private center in Iran. We included all treated patients who had received a maximum of three courses. Additionally, no information was collected on donated eggs, embryos, or also surrogates. Patients were excluded from the study if their courses were not completed or if they lost more than 70% of the required clinical factors (missing values). Finally, 733 couples (courses) under IVF/ICSI and 1196 couples (courses) were used. Couples with multiple infertility factors were excluded from the study to evaluate the effect of sperm parameters more precisely. Sperm stimulation and preparation protocol: After 3 to 5 days of abstinence on the day of coitus, semen samples were collected in a sterile, conical plastic container (OPU). About 15 minutes after arrival, samples are processed (a maximum of 60 minutes from ejaculation time). First, 15 cc of sperm were transferred into a sterilized container containing a gradient containing 1 cc of 45% and 90 cc of 90% silica salt. It was centrifuged at 2500 to 3000 RPM for 15 minutes. Then, the top layer is removed with a syringe and 3 to 4 cc of hamsF10 is added to the motile sperm deposition solution. Centrifuged again at 2500 to 3000 RPM for 10 minutes. Then removed the top solution with a syringe. At this stage, if the IUI method is used for treatment, we kept 0.5 cc at the end of the syringe, and in the IVF/ICSI method, the last 250 cc is kept in the syringe. After sperm preparation, sperm analysis parameters such as morphology, motility, and sperm volume are evaluated. Ovarian Stimulation Protocol in IUI patients: In the IUI method, first a transvaginal ultrasound is performed on the second day of the cycle and the number of follicles is evaluated. If there is no cyst in the ovaries and the suitable thickness of the endometrium and the patient's condition are suitable, gonadotropin drugs such as FSH (F. signal) are prescribed. Six days after the initial ultrasound, a follow-up ultrasound was conducted to assess the follicles' growing process. If the follicle was greater than 16 mm, the physician injected the HCG ampoule. It was carried out around 36 hours following egg retrieval. Ovarian Stimulation Protocol in IVF/ICSI patients: The ovarian stimulation protocol in IVF/ICSI is slightly different from the IUI method. After a primary transvaginal ultrasound on the second day of the cycle, gonadotropin-releasing hormone analogues (GnRH) and steroid hormone inhibitors such as clomiphene citrate, letrozole, and FSH were prescribed. If the treatment regimen includes agonists, these drugs were given in the luteal phase before ovulation is stimulated. If up to 2 dominant follicles with a size of 18 to 20 mm were observed, HCG ampoules were injected. Then, the dominant follicles were retrieved from the ovary by the puncture. 2.2. Methodology Machine learning approaches are concerned with identifying hidden patterns and extracting information from data. Machine learning provides a variety of methods and algorithms for predicting the output of certain input predictors that can be used for clinical decision-making 11 . Here, we capitalized on the advantages of ensemble methods, which combine multiple models with a single type of algorithm to create an optimal prediction model. Bagging, Boosting, Random Forest, Xgboost, and ADABoosting are all popular ensemble algorithms. In the following paragraphs, brief descriptions of models are presented 12: Bagging is a widely used technique that makes use of decision trees and significantly improves model stability by increasing accuracy and reducing variance, as well as removing the challenge of overfitting. Indeed, it assembles the predictions of several weak models to obtain the most accurate predictions. Random forest is one of the bagging methods. In this method, poor learning methods are combined to build a strong model, and one of its advantages is its robustness toward missing data. Bootstrapping is a sampling technique in which an alternative method is used to select a sample from a set. Following that, the learning algorithm is applied to the selected samples. AdaBoost is a boosting technique that identifies and weights unclassified data in each iteration. Gradient boosting (Boost) strengthens the gradient based on the difference between the predictor and the correct value. We used ensemble methods to predict clinical pregnancy for predicting the outcome variable three features were utilized which included morphology, motility, and count. The examined data were utilized to train and test prediction models in proportions of 80% to 20%, respectively. We utilize the SMITING approach to balance the data since the data investigated in both treatments is unbalanced in terms of the number of samples in the classes 13 . Technically, we performed cross-validation with k-fold = 10 to assess the models. 2.3 Model evaluation metrics To assess the performance of the obtained models, we utilized well-known metrics such as accuracy, recall, F-Score, precision, or Positive Predictive Value (PPV), and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). These metrics for model performance have been used in several studies to assess infertility prediction, thereby facilitating the comparison of the results of this study with earlier studies 14 . The study protocol was approved and supervised by the Institutional Review Board (IRB code: 1399.060.) of Mashhad University of Medical Sciences. The data in this study did not include patients' names or personal information. 3. Results In this study, most-selective ensemble models are developed for the prediction of the success rate of clinical pregnancy. These models are compared from different aspects of three different types of treatments, IVF/ICSI and IUI. Comparison of Accuracy and AUC of Models: In the first step, the results were achieved by each approach and their criteria in three treatments were compared in terms of accuracy, and AUC as in Figure 1 is shown. Among these models, Bagging and Random Forest accomplished the best mean values of accuracy and AUC for IVF/ICSI procedure, Bagging accuracy (0.74) and AUC (0.79) and Random Forest accuracy (0.72) and AUC (0.80). However, for IUI procedure, Bagging and Random Forest's mean of accuracy is equally 0.85 and Random Forest's mean of AUC is higher than Bagging, it almost gained better results. Overall, it is observed that Random Forest is a suitable model for three treatments. Comparison SHAP VALUE of Models: In the second step, the value of sperm parameters in predicting the success rate of the clinical pregnancy is illustrated based on the best model (Random Forest) from the previous step. In Figure 2, the impact of each feature in predicting the model and their relationship, are shown for each treatment procedure. Notable, in the IUI procedure, sperm parameters (morphology, motility, and count) had significant negative impacts on clinical pregnancy according to the Random Forest model. However, the coefficient of influence of sperm motility on predicting clinical pregnancy has been reported to be positive for some patients having IVF/ICSI procedures, while the morphology and count factors had negative impacts. Evaluation of the association between sperm parameters and pregnancy: In addition, the statistical results related to the association between sperm parameters and pregnancy are illustrated in Figure 3. In this figure, the difference between the values of sperm parameters in successful and unsuccessful pregnancy groups was assessed using a student t-test. The findings revealed that in clinical pregnancies after the IVF/ICSI procedure, there was a significant difference in values of parameters (morphology and count) in the successful and unsuccessful groups. Notable, the morphological parameter was significantly different in both pregnancy outcomes in IUI procedure. In contrast, the count parameter was not considerably different between the successful and unsuccessful groups. Comparison of sperm parameters in treatments based on cycles: Determining a cut-off value for the feature can provide valuable information for gynecologists. In this study, for each of these sperm parameters, a cut-off was computed to derive an evidence-based decision rule. In terms of clinical pregnancy, the optimal count parameter cut-offs were 54 million (p-value:0.02, 95%-CIs:1.05- 2.13) and 35 (p-value: 0.03, 95%-CIs:1.06-2.86) in IVF/ICSI and IUI procedures, respectively. The cut-off point for the morphological parameter in each of the procedures approach was also found to be 30 with a p-value of 0.001. However, no significant cut-off for the motility factor was obtained for the methods. In Additional, statistical information about sperm parameters in different courses for three methods is provided based on the WHO recommendation 15 . From the results obtained in Tables 1 and 2, it can be seen that most of the patients in the three procedures were in the first course of the procedure. Also, the results showed the fact that in the IVF/ICSI groups for most patients in the first and second course of the procedure, sperm parameters are lower than the average set by WHO. However, in the IUI group, sperm parameters report a higher-than-average level (Table 2). Moreover, for patients whose sperm parameters are below average, IVF procedures achieved better results. Especially for the second course of procedure, they gained higher success rates in clinical pregnancies. Furthermore, for patients with sperm parameters above the mean value reported by WHO, the IUI method reached better results and two courses of procedure were more successful. Table 1: Comparison of sperm parameters in IVF/ICSI procedure based on courses. Patients (N=599 ( IVF/ICSI 1 ) N=433 ( IVF/ICSI 2 ) N=124 ( IVF/ICSI 3 ) N=42 ( Spermiogram below Avg (N=29.04%) (CPR=(38/5%),FHR=(30/4%)) 128 (CPR =37/5%; FHR =20/3% 36 (CPR =100%; FHR =97/2% 10 (CPR =20%; FHR =20% Spermiogram Avg (N=5) (CPR =0.0%, FHR =0.0%)) 3 (CPR =0.0%, FHR =0.0%) 1 (CPR =0.0%, FHR =0.0%)) 1 (CPR =0.0%, FHR =0.0%)) Spermiogram above Avg (N=140) (CPR =22/1%, FHR =18/5%) 103 (CPR =22/3%, FHR =15/53%) 26 (CPR =23/7%, FHR =23.7%) 11 (CPR =36.3%, FHR =36/3% *Spermiogram Average for Sperm Count (10*6) WHO Standard Count is 33-46; Sperm Motility is 38-42; and Sperm Morphology is 30-40. ** CPR as clinical pregnancy rate and FHR as fetal heart rate. Table 2: Comparison of sperm parameters in IUI procedure based on courses. Patients (N=954) IUI 1 (N=775) IUI 2 (N=150) IUI 3 (N=29) Spermiogram below Avg(N=0.9%) (CPR =0.0%, FHR =0.0%)) 8 (CPR =0.0%, FHR =0.0%)) 1 (CPR =0.0%, FHR =0.0%)) 0 (CPR =0.0%, FHR =0.0%)) Spermiogram Avg (N=0.3%) (CPR =1(33/3), FHR =1(33/3)) 2 (CPR =33/3%, FHR =33/3%) 1 (CPR =33/3%, FHR=33/3%) 0 (CPR =33/3%, FHR =33/3%) Spermiogram above Avg (N=52.3%) (CPR =69 (13/82); FHR =61 (12/2)) 409 (CPR =13/93%; FHR =12/22% 76 (CPR =15/78%; FHR =14/47%) 14 (CPR =0.0%, FHR =0.0%)) *Spermiogram Average for Sperm Count (10*6) WHO Standard Count is 33-46; Sperm Motility is 38-42; and Sperm Morphology is 30-40. ** CPR as clinical pregnancy rate and FHR as fetal heart rate. Analysis of sperm parameters based on infertility diagnosis for IVF/ICSI and IUI procedures: Since infertility is strongly related to sperm parameters, we performed this analysis for IVF/ICSI and IUI groups by examining the successful and unsuccessful pregnancy groups. The results obtained from Figure 4 shows that in the IVF/ICSI procedure, only the values of morphology parameter in terms of clinical pregnancy had a significant difference between the successful and unsuccessful groups. However, in the two groups of unexplained infertility and female infertility, this difference was also significant in terms of clinical pregnancy. Furthermore, in the IUI procedure, only for patients with female infertility, the morphology factor in both types of pregnancy is significantly different between successful and unsuccessful groups. 4. Discussion According to the results, in the IUI procedure sperm parameters (morphology, motility, and count) had significant negative impacts on clinical pregnancy. Similar to the previous research, the morphology factor had a negative impact on the IVF/ICSI-related model. In addition, the difference between the values of sperm parameters in successful and unsuccessful pregnancy groups in clinical pregnancy illustrated a significant difference in values of the morphology of the three procedures. Furthermore, the difference in the count parameter could be observed between the successful and unsuccessful groups. Moreover, the negative impact of the morphology factor in the three methods must be considered. 5. Conclusion A comprehensive study on five ensemble machine-learning models was developed for the evaluation of the most effective parameters related to sperm quality for the success rate of clinical pregnancy in IVF, ICSI, and IUI Methods. Initially, these models were evaluated on 733 couples in IVF/ICSI groups and 1196 couples in IUI group. Among the proposed machine learning models, the Random Forest model achieved the best accuracy (0.72) and AUC (0.80) in the three procedures. Moreover, the analysis of the impacts of sperm parameters in predicting the success rate of procedures illustrated that cut-off values for the morphology parameter was 30 for three procedures and the count parameter were 54 and 35 for IVF/ICSI and IUI procedures for successful clinical pregnancy. Based on the achieved results, in patients with lower-than-average sperm parameters, the success rates in clinical pregnancies enhanced in the second course of the IVF procedure, while in patients with above-average sperm parameters, the IUI technique and two courses were more effective. In conclusion, the proposed enable machine learning models, especially the Random Forest model can predict the success rate of clinical pregnancy and the possibility of infertility being dependent on sperm parameters. Declarations Author Contributions Conceptualization, SE, HR, AM, and TD; formal analysis, SE, HR, AM, TD, and NKG, funding acquisition, HR and SE; investigation, SE, HR, and AM; methodology, AM, HR, TD, and SE; software, AM; writing original draft, AM, HR, SE, TD, MJ and NKG; writing, review and editing, all authors have read and agreed to the published version of the manuscript. Conflicts of Interest and Source of Funding The authors declare no conflict of interest. Acknowledgment This work was supported in part by the Research Opportunity in the Medical University of Mashhad from the University of Sistan and Baluchestan, Zahedan, Iran. We are grateful for the help and support and members of the Laboratory of Medical Data Sciences and Image Processing - Faculty members in Medical Informatics Department at Mashhad Medical University and Reproduction Centers in Mashhad, Iran. Authors Statements The authors confirm that all methods were carried out in accordance with relevant guidelines and regulations. Also, we confirm that informed consent was obtained from all subjects and/or their legal guardian(s). Data Availability Statement The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. References Cannarella, R., et al., FSH dosage effect on conventional sperm parameters: a meta-analysis of randomized controlled studies. Asian Journal of Andrology, 2020. 22(3): p. 309. Maharlouei, N., et al., Prevalence and pattern of infertility in Iran: A systematic review and meta-analysis study. Women's Health Bulletin, 2021: p. 63–71. Moein, M.R., et al., Prevalence of Primary Infertility in Iranian Men; A Systematic Review. Men's Health Journal, 2021. 5 (1): p. e12-e12. Zacà, C., et al., Sperm count affects cumulative birth rate of assisted reproduction cycles in relation to ovarian response. 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Lin, H., et al., Role of the total progressive motile sperm count (TPMSC) in different infertility factors in IUI: a retrospective cohort study. BMJ open, 2021. 11(2): p. e040563. Gu, Y., et al., Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data. Scientific Reports, 2021. 11(1): p. 1–13. Rosly, R., et al., Comprehensive study on ensemble classification for medical applications. International Journal of Engineering & Technology, 2018. 7(2.14): p. 186–190. Vaegter, K.K., et al., Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers. Fertility and sterility, 2017. 107(3): p. 641–648. e2. Liu, L., et al., Machine learning algorithms to predict early pregnancy loss after in vitro fertilization-embryo transfer with fetal heart rate as a strong predictor. Computer Methods and Programs in Biomedicine, 2020. 196: p. 105624. Sebastian Findeklee, Julia Caroline Radosa, Marc Philipp Radosa & Mohamad Eid Hammadeh, Correlation between total sperm count and sperm motility and pregnancy rate in couples undergoing intrauterine insemination, Scientific Reports volume 10, Article number: 7555 (2020) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Jan, 2024 Reviews received at journal 25 Jan, 2024 Reviewers agreed at journal 23 Jan, 2024 Reviewers agreed at journal 04 Jan, 2024 Reviews received at journal 27 Dec, 2023 Reviewers agreed at journal 14 Dec, 2023 Reviewers agreed at journal 03 May, 2023 Reviews received at journal 26 Mar, 2023 Reviewers agreed at journal 21 Mar, 2023 Reviewers invited by journal 02 Mar, 2023 Editor assigned by journal 02 Mar, 2023 Editor invited by journal 06 Feb, 2023 Submission checks completed at journal 06 Feb, 2023 First submitted to journal 15 Jan, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2481505","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":173698155,"identity":"8ec5e050-a3d0-4b74-b4fe-04fb755f6b55","order_by":0,"name":"Ameneh Mehrjerd","email":"","orcid":"","institution":"Unversity Medicine Greifswald","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ameneh","middleName":"","lastName":"Mehrjerd","suffix":""},{"id":173698156,"identity":"378090a2-2faf-4060-b706-aa80276a128b","order_by":1,"name":"Toktam Dehghani","email":"","orcid":"","institution":"Mashhad University of Medical 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19:44:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2481505/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2481505/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-73326-7","type":"published","date":"2024-10-16T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":32600964,"identity":"7f2beede-d3b9-459f-ba06-f6a45ce2fbce","added_by":"auto","created_at":"2023-02-07 15:56:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109751,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of accuracy and AUC of models in IVF/ICSI and IUI treatment.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2481505/v1/e3d0a3596590e7348233b944.png"},{"id":32600967,"identity":"4fd33b55-fc44-4c4e-b452-eb464b30aca9","added_by":"auto","created_at":"2023-02-07 15:56:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":232241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of Sperm parameters on RF model prediction in IVF/ICSI and IUI procedures.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2481505/v1/319fcbd3da3e3b16303dd646.png"},{"id":32600968,"identity":"482dc4eb-0bdb-4370-9746-087428ab87bc","added_by":"auto","created_at":"2023-02-07 15:56:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":214153,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of sperm parameters with CPR as clinical pregnancy rate and FHR as fetal heart rate for IUI and IVF/ICSI.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2481505/v1/a9550900a31a1b92ea757aaf.png"},{"id":32600969,"identity":"702078b8-b499-43c0-889f-fea9b3d0f9f0","added_by":"auto","created_at":"2023-02-07 15:56:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":235330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of sperm parameters based on infertility diagnosis for IVF/ICSI and IUI procedures.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2481505/v1/ad8d1ab629beec96041303f2.png"},{"id":67149023,"identity":"2013134b-438e-4bb2-a525-9b01003f52ee","added_by":"auto","created_at":"2024-10-21 16:11:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1505583,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2481505/v1/685157f0-19bb-41d2-a58c-17a36ebff572.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ensemble Machine Learning Models for Evaluation of Sperm Quality with Respect to Success Rate of Clinical Pregnancy in IVF, ICSI, and IUI Methods","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eInfertility is an issue that affects couples whose pregnancy fails after 12 months of unprotected sexual activity. The infertility prevalence rate is 15\u0026ndash;20% and almost 40\u0026ndash;50% of all cases are caused only by the male factor\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Assisted Reproduction Technology (ART) treatments are defined as medical procedures aiming to achieve pregnancy, but their success is influenced by a myriad of intrinsic and extrinsic factors. There are numerous ART treatments available, including IUI (intrauterine insemination), IVF (in vitro fertilization), and intracytoplasmic sperm injection (ICSI)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. IUI is the first-line treatment option and also the most cost-effective strategy for males with unexplained or mild infertility which is defined as a single abnormal finding of the semen analysis or a total motile sperm count between 10\u0026ndash;20 \u0026times; 106/mL\u003csup\u003e5\u003c/sup\u003e. However, patients with infertility using the IVF method have a total fertilization failure rate of 5% of IVF cycles\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Many variable parameters are considered to predict effective methods and select a treatment that is suitable for each infertile couple. Female and male factors are both considered. Male factors such as concentration, morphology, motility, volume, total number, and vitality are used to represent abnormal sperm in male infertility\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, whereas female parameters such as type and duration of infertility, number of mature follicles, endometrial thickness, and various seminal parameters\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e are used to represent abnormal sperm in female infertility. According to previous research, these factors are the source of standard laboratories in sperm analysis, and \"normal\" or \"reference\" values as cut-offs have a limitation and are difficult to understand\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In 2014, a systematic review reported that total motile sperm count (\u0026gt;\u0026thinsp;1\u0026nbsp;million) and morphology are possible predictive variables for IUI success, however, the quality of evidence was low\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Various investigations have shown different outcomes. One study published in 2019 showed that prewash total motile sperm is a poor predictor of live births in IUI cycles, with no correlation between live births and prewash total motile sperm counts of 2\u0026nbsp;million sperm\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Another study in 2021 revealed that the relation between IUI and prediction variables in patients with unexplained infertility was not statistical significance. Also, the difference between quartiles of total progressive motile sperm count (TPMSC) with live birth rate and clinical pregnancy rate was not statistical significance whether the patient was older than 40 or the TPMSC less than 10%. Moreover, they reported only one clinical pregnancy and no live birth\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Regarding the IVF study in 2020 that represented male partners with total sperm count greater than 5\u0026nbsp;million, there was no correlation between low total testosterone levels and semen parameter changes. Also in 2020, the importance of sperm quality in the efficacy of assisted reproduction IVF procedure was emphasized, and higher prewash total sperm count values had a positive impact on cumulative success rates in cycles with few retrieved oocytes (1 to 5), but had no effect on the outcome of cycles with a normal (6 to 10) or high (\u0026gt;\u0026thinsp;10) number of retrieved oocytes\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this paper, different ensemble models of machine learning were developed to predict the possibility of clinical pregnancy as well as infertility based on the effect of sperm parameters. The primary objective is to determine the success rate of clinical pregnancy (CPR) refers to a positive beta test or gestational sec in ultrasonography in the fourth week of pregnancy. The secondary objective is to evaluate the clinical pregnancy rate (FHR) refers to the fetal heart rate in the eleventh week of pregnancy (FH).\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Data collecting and preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we analyzed three widely used treatment methods, IVF, ICSI and IUI, and the data was collected from two universities, an affiliated infertility center and a private center in Iran. We included all treated patients who had received a maximum of three courses. Additionally, no information was collected on donated eggs, embryos, or also surrogates. Patients were excluded from the study if their courses were not completed or if they lost more than 70% of the required clinical factors (missing values). Finally, 733 couples (courses) under IVF/ICSI and 1196 couples (courses) were used. Couples with multiple infertility factors were excluded from the study to evaluate the effect of sperm parameters more precisely.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eSperm stimulation and preparation protocol:\u0026nbsp;\u003c/strong\u003eAfter 3 to 5 days of abstinence on the day of coitus, semen samples were collected in a sterile, conical plastic container (OPU). About 15 minutes after arrival, samples are processed (a maximum of 60 minutes from ejaculation time).\u0026nbsp;First, 15 cc of sperm were transferred into a sterilized container containing a gradient containing 1 cc of 45% and 90 cc of 90% silica salt.\u0026nbsp;It was centrifuged at 2500 to 3000 RPM for 15 minutes. Then, the top layer is removed with a syringe and 3 to 4 cc of hamsF10 is added to the motile sperm deposition solution. Centrifuged again at 2500 to 3000 RPM for 10 minutes.\u0026nbsp;Then removed the top solution with a syringe. At this stage, if the IUI method is used for treatment, we kept 0.5 cc at the end of the syringe, and in the IVF/ICSI method, the last 250 cc is kept in the syringe.\u0026nbsp;After sperm preparation, sperm analysis parameters such as morphology, motility, and sperm volume are evaluated.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOvarian Stimulation Protocol in IUI patients:\u0026nbsp;\u003c/strong\u003eIn the IUI method, first a transvaginal ultrasound is performed on the second day of the cycle and the number of follicles is evaluated.\u0026nbsp;If there is no cyst in the ovaries and the suitable thickness of the endometrium and the patient\u0026apos;s condition are suitable, gonadotropin drugs such as FSH (F. signal) are prescribed.\u0026nbsp;Six days after the initial ultrasound, a follow-up ultrasound was conducted to assess the follicles\u0026apos; growing process. If the follicle was greater than 16 mm, the physician injected the HCG ampoule. It was carried out around 36 hours following egg retrieval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOvarian Stimulation Protocol in IVF/ICSI patients:\u0026nbsp;\u003c/strong\u003eThe ovarian stimulation protocol in IVF/ICSI is slightly different from the IUI method. After a primary transvaginal ultrasound on the second day of the cycle, gonadotropin-releasing hormone analogues (GnRH) and steroid hormone inhibitors such as clomiphene citrate, letrozole, and FSH were prescribed. If the treatment regimen includes agonists, these drugs were given in the luteal phase before ovulation is stimulated. If up to 2 dominant follicles with a size of 18 to 20 mm were observed, HCG ampoules were injected. Then, the dominant follicles were retrieved from the ovary by the puncture.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Methodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMachine learning approaches are concerned with identifying hidden patterns and extracting information from data. Machine learning provides a variety of methods and algorithms for predicting the output of certain input predictors that can be used for clinical decision-making\u0026nbsp;\u003csup\u003e11\u003c/sup\u003e. Here, we capitalized on the advantages of ensemble methods, which combine multiple models with a single type of algorithm to create an optimal prediction model. Bagging, Boosting, Random Forest, Xgboost, and ADABoosting are all popular ensemble algorithms. In the following paragraphs, brief descriptions of models are presented 12:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eBagging\u003c/strong\u003e is a widely used technique that makes use of decision trees and significantly improves model stability by increasing accuracy and reducing variance, as well as removing the challenge of overfitting. Indeed, it assembles the predictions of several weak models to obtain the most accurate predictions.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRandom forest\u003c/strong\u003e is one of the bagging methods. In this method, poor learning methods are combined to build a strong model, and one of its advantages is its robustness toward missing data.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBootstrapping\u003c/strong\u003e is a sampling technique in which an alternative method is used to select a sample from a set. Following that, the learning algorithm is applied to the selected samples.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAdaBoost\u003c/strong\u003e is a boosting technique that identifies and weights unclassified data in each iteration.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;\u003cstrong\u003eGradient boosting (Boost)\u003c/strong\u003e strengthens the gradient based on the difference between the predictor and the correct value.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe used ensemble methods to predict clinical pregnancy\u0026nbsp;for predicting the outcome variable three features were utilized which included morphology, motility, and count. The examined data were utilized to train and test prediction models in proportions of 80% to 20%, respectively. We utilize the SMITING approach to balance the data since the data investigated in both treatments is unbalanced in terms of the number of samples in the classes\u003csup\u003e13\u003c/sup\u003e.\u0026nbsp;Technically, we performed cross-validation with k-fold = 10 to assess the models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Model evaluation metrics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the performance of the obtained models, we utilized well-known metrics such as accuracy, recall, F-Score, precision, or Positive Predictive Value (PPV), and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). These metrics for model performance have been used in several studies to assess infertility prediction, thereby facilitating the comparison of the results of this study with earlier studies\u0026nbsp;\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved and supervised by the Institutional Review Board (IRB code: 1399.060.) of Mashhad University of Medical Sciences. The data in this study did not include patients\u0026apos; names or personal information.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eIn this study, most-selective ensemble models are developed for the prediction of\u0026nbsp;the success rate of clinical pregnancy. These models are compared from different aspects of three different types of\u0026nbsp;treatments, IVF/ICSI and IUI.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eComparison of Accuracy and AUC of Models:\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn the first step, the results were achieved by each approach and their criteria in three treatments were compared in terms of accuracy, and AUC as in Figure 1 is shown. Among these models, Bagging and Random Forest accomplished the best mean values of accuracy and AUC for IVF/ICSI procedure, Bagging accuracy (0.74) and AUC (0.79) and Random Forest accuracy (0.72) and AUC (0.80). However, for IUI procedure, Bagging and Random Forest\u0026apos;s mean of accuracy is equally 0.85 and Random Forest\u0026apos;s mean of AUC is higher than Bagging, it almost gained better results. Overall, it is observed that Random Forest is a suitable model for three treatments.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eComparison SHAP VALUE of Models:\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn the second step, the value of sperm parameters in predicting the success rate of the clinical pregnancy is illustrated based on the best model (Random Forest) from the previous step. In Figure 2, the impact of each feature in predicting the model and their relationship, are shown for each treatment procedure. Notable, in the IUI procedure, sperm parameters (morphology, motility, and count) had significant negative impacts on clinical pregnancy according to the Random Forest model. However, the coefficient of influence of sperm motility on predicting clinical pregnancy has been reported to be positive for some patients having IVF/ICSI procedures, while the morphology and count factors had negative impacts.\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eEvaluation of the association between sperm parameters and pregnancy: \u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition, the statistical results related to the association between sperm parameters and pregnancy are illustrated in Figure 3. In this figure, the difference between the values of sperm parameters in successful and unsuccessful pregnancy groups was assessed using a student t-test. The findings revealed that in clinical pregnancies after the IVF/ICSI procedure, there was a significant difference in values of parameters (morphology and count) in the successful and unsuccessful groups. Notable, the morphological parameter was significantly different in both pregnancy outcomes in IUI procedure. In contrast, the count parameter was not considerably different between the successful and unsuccessful groups.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eComparison of sperm parameters in treatments based on cycles:\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDetermining a cut-off value for the feature can provide valuable information for gynecologists. In this study, for each of these sperm parameters, a cut-off was computed to derive an evidence-based decision rule. In terms of\u0026nbsp;clinical\u0026nbsp;pregnancy, the optimal count parameter cut-offs were 54 million (p-value:0.02, 95%-CIs:1.05- 2.13) and 35 (p-value: 0.03, 95%-CIs:1.06-2.86) in IVF/ICSI and IUI procedures, respectively. The cut-off point for the morphological parameter in each of the procedures approach was also found to be 30 with a p-value of 0.001.\u0026nbsp;However, no significant cut-off for the motility factor was obtained for the methods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn\u0026nbsp;Additional, statistical information about sperm parameters in different courses for three methods is provided based on the WHO\u0026nbsp;recommendation\u0026nbsp;\u003csup\u003e15\u003c/sup\u003e. From the results obtained in Tables 1 and 2, it can be seen that most of the patients in the three procedures were in the first course of the procedure. Also, the results showed the fact that in the IVF/ICSI groups for most patients in the first and second course of the procedure, sperm parameters are lower than the average set by WHO. However, in the IUI group, sperm parameters report a higher-than-average level (Table 2).\u0026nbsp;Moreover, for patients whose sperm parameters are below average, IVF procedures achieved better results. Especially for the second course of procedure, they gained higher success rates in clinical pregnancies. Furthermore, for patients with sperm parameters above the mean value reported by WHO, the IUI method reached better results and two courses of procedure were more successful.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Comparison of sperm parameters in IVF/ICSI procedure based on courses.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients (N=599\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e(\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIVF/ICSI 1\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e)\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003eN=433\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e(\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIVF/ICSI 2\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e)\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003eN=124\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e(\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIVF/ICSI 3\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e)\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003eN=42\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e(\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpermiogram below Avg (N=29.04%)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(CPR=(38/5%),FHR=(30/4%))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u003cstrong\u003e128\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CPR =37/5%;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFHR =20/3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003e36\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CPR =100%;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFHR =97/2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e10\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(CPR =20%;\u003c/p\u003e\n \u003cp\u003eFHR =20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpermiogram Avg (N=5)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CPR =0.0%, FHR =0.0%))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(CPR =0.0%,\u003c/p\u003e\n \u003cp\u003eFHR =0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(CPR =0.0%,\u003c/p\u003e\n \u003cp\u003eFHR =0.0%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(CPR =0.0%,\u003c/p\u003e\n \u003cp\u003eFHR =0.0%))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpermiogram above Avg (N=140)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CPR =22/1%, FHR =18/5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003cp\u003e(CPR =22/3%,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFHR =15/53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003cp\u003e(CPR =23/7%,\u003c/p\u003e\n \u003cp\u003eFHR =23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CPR =36.3%,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFHR =36/3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Spermiogram Average for Sperm Count (10*6) WHO Standard Count is 33-46; Sperm Motility is 38-42; and Sperm Morphology is 30-40. \u0026nbsp;** CPR as clinical pregnancy rate and FHR as fetal heart rate.\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Comparison of sperm parameters in IUI procedure based on courses.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.03883495145631%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients (N=954)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.24271844660194%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIUI 1 (N=775)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.359223300970875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIUI 2 (N=150)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.359223300970875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIUI 3 (N=29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.03883495145631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpermiogram below Avg(N=0.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(CPR =0.0%, FHR =0.0%))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.24271844660194%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(CPR =0.0%,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFHR =0.0%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.359223300970875%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(CPR =0.0%,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFHR =0.0%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.359223300970875%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(CPR =0.0%,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFHR =0.0%))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.03883495145631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpermiogram Avg (N=0.3%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CPR =1(33/3), FHR =1(33/3))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.24271844660194%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(CPR =33/3%,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFHR =33/3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.359223300970875%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(CPR =33/3%,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFHR=33/3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.359223300970875%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(CPR =33/3%,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFHR =33/3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.03883495145631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpermiogram above Avg (N=52.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CPR =69 (13/82); FHR =61 (12/2))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.24271844660194%\"\u003e\n \u003cp\u003e\u003cstrong\u003e409\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CPR =13/93%;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFHR =12/22%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.359223300970875%\"\u003e\n \u003cp\u003e\u003cstrong\u003e76\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CPR =15/78%;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;FHR =14/47%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.359223300970875%\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CPR =0.0%,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFHR =0.0%))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Spermiogram Average for Sperm Count (10*6) WHO Standard Count is 33-46; Sperm Motility is 38-42; and Sperm Morphology is 30-40.\u003c/p\u003e\n\u003cp\u003e** CPR as clinical pregnancy rate and FHR as fetal heart rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of sperm parameters based on infertility diagnosis for IVF/ICSI and IUI procedures:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince infertility is strongly related to sperm parameters, we performed this analysis for IVF/ICSI and IUI groups by examining the successful and unsuccessful pregnancy groups. The results obtained from Figure 4 shows that in the IVF/ICSI procedure, only the values of morphology parameter in terms of clinical pregnancy had a significant difference between the successful and unsuccessful groups. However, in the two groups of unexplained infertility and female infertility, this difference was also significant in terms of clinical pregnancy. Furthermore, in the IUI procedure, only for patients with female infertility, the morphology factor in both types of pregnancy is significantly different\u0026nbsp; between successful and unsuccessful groups.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAccording to the results, in the IUI procedure sperm parameters (morphology, motility, and count) had significant negative impacts on clinical pregnancy. Similar to the previous research, the morphology factor had a negative impact on the IVF/ICSI-related model. In addition, the difference between the values of sperm parameters in successful and unsuccessful pregnancy groups in clinical pregnancy illustrated a significant difference in values of the morphology of the three procedures. Furthermore, the difference in the count parameter could be observed between the successful and unsuccessful groups. Moreover, the negative impact of the morphology factor in the three methods must be considered.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eA comprehensive study on five ensemble machine-learning models was developed for the evaluation of the most effective parameters related to sperm quality for the success rate of clinical pregnancy in IVF, ICSI, and IUI Methods. Initially, these models were evaluated on 733 couples in IVF/ICSI groups and 1196 couples in IUI group. Among the proposed machine learning models, the Random Forest model achieved the best accuracy (0.72) and AUC (0.80) in the three procedures. Moreover, the analysis of the impacts of sperm parameters in predicting the success rate of procedures illustrated that cut-off values for the morphology parameter was 30 for three procedures and the count parameter were 54 and 35 for IVF/ICSI and IUI procedures for successful clinical pregnancy. Based on the achieved results, in patients with lower-than-average sperm parameters, the success rates in clinical pregnancies enhanced in the second course of the IVF procedure, while in patients with above-average sperm parameters, the IUI technique and two courses were more effective. In conclusion, the proposed enable machine learning models, especially the Random Forest model can predict the success rate of clinical pregnancy and the possibility of infertility being dependent on sperm parameters.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, SE, HR, AM, and TD; formal analysis, SE, HR, AM, TD, and NKG, funding acquisition, HR and SE; investigation, SE, HR, and AM; methodology, AM, HR, TD, and SE; software, AM; writing original draft, AM, HR, SE, TD, MJ and NKG; writing, review and editing, all authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest and Source of Funding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by the Research Opportunity in the Medical University of Mashhad from the University of Sistan and Baluchestan, Zahedan, Iran. We are grateful for the help and support and members of the Laboratory of Medical Data Sciences and Image Processing - Faculty members in Medical Informatics Department at Mashhad\u0026nbsp;Medical University and Reproduction Centers in Mashhad, Iran.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that all methods were carried out in accordance with relevant guidelines and regulations. Also, we confirm that informed consent was obtained from all subjects and/or their legal guardian(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eCannarella, R., et al., FSH dosage effect on conventional sperm parameters: a meta-analysis of randomized controlled studies. Asian Journal of Andrology, 2020. 22(3): p. 309.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMaharlouei, N., et al., Prevalence and pattern of infertility in Iran: A systematic review and meta-analysis study. Women\u0026apos;s Health Bulletin, 2021: p.\u0026nbsp;63\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMoein, M.R., et al., Prevalence of Primary Infertility in Iranian Men; A Systematic Review. Men\u0026apos;s Health Journal, 2021. \u003cstrong\u003e5\u003c/strong\u003e(1): p.\u0026nbsp;e12-e12.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZac\u0026agrave;, C., et al., Sperm count affects cumulative birth rate of assisted reproduction cycles in relation to ovarian response. Journal of assisted reproduction and genetics, 2020. \u003cstrong\u003e37\u003c/strong\u003e(7): p.\u0026nbsp;1653\u0026ndash;1659.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGubert, P.G., et al., Number of motile spermatozoa inseminated and pregnancy outcomes in intrauterine insemination. Fertility Research and Practice, 2019. \u003cstrong\u003e5\u003c/strong\u003e(1): p.\u0026nbsp;1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAnbari, F., et al., Does sperm DNA fragmentation have negative impact on embryo morphology and morphokinetics in IVF programme? Andrologia, 2020. 52(11): p.\u0026nbsp;e13798.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eCooper, T.G., et al., World Health Organization reference values for human semen characteristics. Human reproduction update, 2010. 16(3): p.\u0026nbsp;231\u0026ndash;245.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eOmbelet, W., et al., Semen quality and prediction of IUI success in male subfertility: a systematic review. Reproductive biomedicine online, 2014. 28(3): p.\u0026nbsp;300\u0026ndash;309.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMankus, E.B., et al., Prewash total motile count is a poor predictor of live birth in intrauterine insemination cycles. Fertility and sterility, 2019. 111(4): p.\u0026nbsp;708\u0026ndash;713.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLin, H., et al., Role of the total progressive motile sperm count (TPMSC) in different infertility factors in IUI: a retrospective cohort study. BMJ open, 2021. 11(2): p.\u0026nbsp;e040563.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGu, Y., et al., Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data. Scientific Reports, 2021. 11(1): p.\u0026nbsp;1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRosly, R., et al., Comprehensive study on ensemble classification for medical applications. International Journal of Engineering \u0026amp; Technology, 2018. 7(2.14): p.\u0026nbsp;186\u0026ndash;190.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eVaegter, K.K., et al., Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers. Fertility and sterility, 2017. 107(3): p.\u0026nbsp;641\u0026ndash;648. e2.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLiu, L., et al., Machine learning algorithms to predict early pregnancy loss after in vitro fertilization-embryo transfer with fetal heart rate as a strong predictor. Computer Methods and Programs in Biomedicine, 2020. 196: p.\u0026nbsp;105624.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSebastian Findeklee, Julia Caroline Radosa, Marc Philipp Radosa \u0026amp; Mohamad Eid Hammadeh, Correlation between total sperm count and sperm motility and pregnancy rate in couples undergoing intrauterine insemination, Scientific Reports volume 10, Article number: 7555 (2020)\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Infertility, Clinical Pregnancy, Sperm Quality, IVF, ICSI, IUI, Machine learning algorithms, Random Forest.","lastPublishedDoi":"10.21203/rs.3.rs-2481505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2481505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e Evaluation of the effect of sperm quality on the success rate of clinical\u003cstrong\u003e \u003c/strong\u003epregnancy and the possibility of infertility. The primary objective was to determine the success rate of clinical pregnancy (CPR). The secondary objective was to evaluate the clinical pregnancy rate (FHR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e This retrospective study evaluated 1929 couples who were treated with In Vitro Fertilization (IVF), \u0026nbsp;in Intracytoplasmic Sperm Injection (ICSI), and Intrauterine Insemination (IUI) was conducted in two infertility centers; while data from donated eggs or sperm and a surrogate uterus along with data from infertile couples with a combination of male and female factors were excluded. In this study, five ensemble machine-learning models were utilized to predict the success rate of clinical pregnancy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eAmong the proposed ensemble models, the Random Forest (RF) model achieved the highest mean accuracy and area under the curve (AUC) and outperformed all other models in three procedures. Our results show that in cycles with 1 to 5 retrieved eggs, sperm motility and the count of sperm had a positive effect on the rate of clinical pregnancy. Furthermore, the results indicated that cut-off values of 54 (p-value=0.02, 95%-CIs (1.05, 2.13)) and 35 (p-value=0.03, 95% 95%-CIs (1.06, 2.86)) for the count parameter in IVF/ICSI, and IUI, respectively. In addition, a significant cut-off points of 30 (p-value \u0026lt; 0.001) was obtained for the morphology parameter in all procedures. Sperm parameters were negatively weighted in the model obtained by the RF. In addition, the acquired data illustrated that in each procedure, the morphology parameter demonstrated a significant difference in clinical pregnancy between successful and unsuccessful groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e \u0026nbsp;The second course of IVF procedure increased success rates in clinical pregnancy in patients with lower-than-average sperm parameters, while the IUI technique was demonstrated to be more effective in patients with above-average of sperm parameters.\u003c/p\u003e","manuscriptTitle":"Ensemble Machine Learning Models for Evaluation of Sperm Quality with Respect to Success Rate of Clinical Pregnancy in IVF, ICSI, and IUI Methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-07 15:56:23","doi":"10.21203/rs.3.rs-2481505/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-01-29T06:13:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-01-26T01:33:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"032af998-55f9-4192-95e9-97d26c66a177","date":"2024-01-23T08:44:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"380efc6b-1c65-4dbc-8322-c5a587e2bd37","date":"2024-01-04T16:09:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-12-27T15:11:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3868c854-a9a4-4a08-a386-1596263fc6ae","date":"2023-12-14T10:13:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"855d827c-cf71-4b72-a4cc-630c41f6347a","date":"2023-05-03T12:53:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-03-27T03:33:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6dd91650-55c0-48e1-b7b1-4f601e23981a","date":"2023-03-21T14:46:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-03-02T11:07:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-03-02T10:55:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-02-06T10:32:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-02-06T10:05:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-01-15T19:43:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"81ecf146-d846-42bb-8052-5260d6ae2be7","owner":[],"postedDate":"February 7th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":18980603,"name":"Health sciences/Medical research"},{"id":18980604,"name":"Health sciences/Signs and symptoms/Reproductive signs and symptoms"}],"tags":[],"updatedAt":"2024-10-21T16:03:08+00:00","versionOfRecord":{"articleIdentity":"rs-2481505","link":"https://doi.org/10.1038/s41598-024-73326-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-16 15:57:44","publishedOnDateReadable":"October 16th, 2024"},"versionCreatedAt":"2023-02-07 15:56:23","video":"","vorDoi":"10.1038/s41598-024-73326-7","vorDoiUrl":"https://doi.org/10.1038/s41598-024-73326-7","workflowStages":[]},"version":"v1","identity":"rs-2481505","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2481505","identity":"rs-2481505","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}