Materiały źródłowe

• #1 Prognosis-based management of unexplained infertility—why not?

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11075788/

  Up to a half of couples seeking medical assistance for infertility are diagnosed with unexplained infertility, characterized by normal ovulation, tubal patency, and semen analysis results. […] Prognosis-based management is identified as a research priority, and various prediction models have been developed to guide treatment decisions. […] Prognostic factors include female age, duration of subfertility, and sperm parameters, among others. […] Prognosis-based strategies can enhance cost-effectiveness, safety, and patient outcomes, offering less invasive options to those with good prognoses and more aggressive interventions to those with poor prognoses. […] In couples diagnosed with unexplained infertility, natural conception is inherently a possibility. Therefore, assessing the prognostic profile of these couples becomes crucial in understanding the likelihood of natural conception.

• #1 Should we adopt a prognosis-based approach to unexplained infertility?

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9662706/

  The treatment of unexplained infertility is a contentious topic that continues to attract a great deal of interest amongst clinicians, patients and policy makers. […] Over the years, several prognostic and prediction models based on routinely collected clinical data have been developed, but these are not widely used by clinicians and patients. […] In this opinion paper, we propose a prognosis-based approach such that a decision to access treatment is based on the estimated chances of natural and treatment-related conception, which, in the same couple, can change over time. […] This approach avoids treating all couples as a homogeneous group and minimizes unnecessary treatment whilst ensuring access to those who need it early. […] A key prognostic factor in unexplained infertility is female age, which has a major impact on the chances of conception both with and without treatment.

• #1

  https://journals.lww.com/jehp/fulltext/2025/03280/machine_learning_in_predicting_infertility.103.aspx

  Assisted reproductive technology (ART) is one of the major developments that has had a significant impact on infertility treatment. A predictive model of ART success based on machine learning (ML) techniques can provide a robust basis for estimating treatment success. This study aimed to identify predictive models of ART success and their determinants. […] Infertility clinicians use patients specific characteristics, together with their knowledge and clinical experience, to recommend this treatment when it is likely to succeed. Yet, due to the number of variables and complex relations among them, the prediction of infertility treatment success turns out to be a challenging task. […] In reproductive medicine, the ML techniques could help to improve the prediction of infertility treatment success resulting in making informed decisions for infertile couples. A predictive model of ART success based on ML techniques could provide a robust basis for the estimation of pregnancy chance by identifying interlinks among variables, making a realistic expectation for both clinicians and couples.

• #1

  https://journals.lww.com/md-journal/fulltext/2022/12090/clinical_pregnancy_outcomes_prediction_in_vitro.93.aspx

  The findings of our study might provide a clinical guidance for the treatment in infertility patients receiving IVF-ET and improve the success rate of clinical pregnancy in those patients. […] According to the results from multivariable Logistic stepwise regression analysis, age (OR=1.093, 95%CI: 1.0361.156, P=.001), BMI (OR=1.094, 95%CI: 1.0211.176, P=.012), 3 cycles (OR=0.144, 95%CI: 0.0280.534, P=.008), HCT (OR=0.865, 95% CI: 0.7910.943, P=.001), LH (OR=0.678, 95%CI: 0.5490.823, P.001), P (OR=2.126, 95%CI: 1.1124.141, P=.024), endometrial thickness (OR=0.132, 95%CI: 0.0340.496, P=.003) and FSH (OR=1.151, 95%CI: 1.0431.275, P=.006) were predictors associated with the clinical pregnancy outcome of patients receiving IVF-ET. […] The random forest prediction model had good predictive value for distinguishing patients with high risk of poor clinical pregnancy outcome and may provide a reference for the obstetricians to make timely intervention to prevent the occurrence of poor clinical pregnancy outcome in those patients.

• #1

  https://journals.lww.com/md-journal/fulltext/2022/12090/clinical_pregnancy_outcomes_prediction_in_vitro.93.aspx

  The present study aimed to analyze the risk factors influencing the in vitro fertilization embryo transfer (IVF-ET) pregnancy and to construct a prediction model for clinical pregnancy outcome in patients receiving IVF-ET based on the predictors. […] The data delineated that age (odds ratio [OR]= 1.093, 95% confidence interval [CI]: 1.0361.156, P=.0010), body mass index (BMI) (OR=1.094, 95%CI: 1.0211.176, P=.012), 3 cycles (OR=0.144, 95%CI: 0.0280.534, P=.008), hematocrit (HCT) (OR=0.865, 95% CI: 0.7910.943, P=.001), luteinizing hormone (LH) (OR=0.678, 95%CI: 0.5490.823, P.001), progesterone (P) (OR=2.126, 95%CI: 1.1124.141, P=.024), endometrial thickness (OR=0.132, 95%CI: 0.0340.496, P=.003) and FSH (OR=1.151, 95%CI: 1.0431.275, P=.006) were predictors associated with the clinical pregnancy outcome of patients receiving IVF-ET.

• #1 Proof of concept and development of a couple-based machine learning model to stratify infertile patients with idiopathic infertility | Scientific Reports

  https://www.nature.com/articles/s41598-021-03165-3

  We aimed to develop and evaluate a machine learning model that can stratify infertile/fertile couples on the basis of their bioclinical signature helping the management of couples with unexplained infertility. […] Our results have shown that: (i) a couple-modeling approach was more discriminant than models in which mens and womens parameters are considered separately; (ii) the most discriminating variables were anthropometric, or related to the metabolic and oxidative status; (iii) a refined model capable to stratify fertile vs. infertile couples with accuracy 73.8% was proposed after the variables selection (from 80 to 13). […] The model proposed takes place in the management of couples with idiopathic infertility, for whom the decision-making tools are scarce. […] In this study, we propose to evaluate the performance of a couple-based Orthogonal Partial Least Square (OPLS) model facilitating the medical decision-making process in the context of idiopathic infertility.

• #1 Diagnosis and Treatment of Infertility in Men: AUA/ASRM Guideline – American Urological Association

  https://www.auanet.org/guidelines-and-quality/guidelines/male-infertility

  Infertility is due in whole or in part to the male in approximately one-half of all infertile couples. […] The purpose of this Guideline is to outline the appropriate evaluation and management of the male partner in an infertile couple. […] Recommendations proceed from obtaining an appropriate history and physical exam, as well as diagnostic testing, where indicated. […] Clinicians should initiate concurrent assessment of both male and female partners. […] Clinicians should include a reproductive history during initial evaluation of the male for fertility. […] Male reproductive experts should evaluate patients with a complete history and physical examination as well as other directed tests, when indicated by one or more abnormal semen parameters or presumed male infertility. […] In couples with failed assisted reproductive technology cycles or recurrent pregnancy losses, clinicians should evaluate the male partner.

• #1 Future diagnostics in male infertility: genomics, epigenetics, metabolomics and proteomics – Krzastek – Translational Andrology and Urology

  https://tau.amegroups.org/article/view/36740/html

  A male factor is involved in 50% of couples with infertility. […] The semen analysis (SA) continues to be first line for the workup of male infertility, but it is an imperfect test with high variability between samples. […] While additional serum blood tests such as testosterone, luteinizing hormone, follicle stimulating hormone and prolactin may point the clinician towards an underlying etiology for male factor infertility, these tests in combination with the SA still may not provide a diagnosis. […] The most common genetic causes include azoospermia factor (AZF) deletions of the Y chromosome, Klinefelter syndrome with an extra copy of the X chromosome, and monogenic disorders including Kallman syndrome and cystic fibrosis. […] However, these genetic conditions account for only 30% of cases of male infertility and subfertility, with approximately 50% of male factor infertility still classified as idiopathic, after controlling for hormonal abnormalities or other anatomic etiologies.

• #1 A new model for determining risk of male infertility from serum hormone levels, without semen analysis | Scientific Reports

  https://www.nature.com/articles/s41598-024-67910-0

  We investigated a screening method using only serum hormone levels and AI (artificial intelligence) predictive analysis. […] Using data from 2021 and 2022 to verify the Prediction One-based AI model, the predicted and actual results for NOA were 100% matched in both years. […] Since semen analysis results and serum hormone levels are correlated, we examined whether machine learning could be used to predict potential male infertility from serum hormone levels alone. […] If a successful screening system applying this became commercially available, the risk of male infertility could be determined without the need for semen analysis. […] We could make an AI model for evaluating male infertility using serum hormone levels, without conventional sperm analysis, and the Prediction One-based model had high accuracy as indicated by an AUC of 74.42%.

• #1

  https://link.springer.com/article/10.1007/s43032-024-01664-y

  Infertility affects approximately 15% of couples at child-bearing ages and assisted reproductive technologies (ART), especially in vitro fertilization and embryo transfer (IVF-ET), provided infertile patients with an effective solution. […] Therefore, to avoid multiple transfer of embryos, it is urgent to explore biomarkers for IVF prognosis to select high-quality oocytes and embryos. […] With the advances in metabolomics methodology, metabolomics, as an accurate and sensitive analyzing method, has been utilized to explore predictors in FF for ART success. […] Therefore, in this review, we categorically demonstrate the study of FF metabolomics according to specific infertility causes, expecting to reveal the predicting value of metabolomics for IVF outcomes. […] Therefore, persistent effort has been made to explore and establish models to predict the IVF prognosis including oocyte quality, embryo viability, implantation rate and live-birth rate in a cycle to raise the success rate, alleviating patients physical, psychological and financial burden.

• #2 Prognosis-based management of unexplained infertility—why not?

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11075788/

  The ESHRE guideline does indeed recognize the potential of a prognosis-centred strategy for couples facing unexplained infertility. […] The adoption of a prognosis-based approach has been steadily increasing in the field of medicine, proving to offer significant advantages for healthcare systems and patients alike. […] Similarly, there is a growing demand for the implementation of a prognosis-based strategy in the management of couples facing unexplained infertility. […] The prediction models can be used to determine if IUI and IVF are expected to add to natural fertility chances. […] Prediction models can be used to triage couples with unexplained infertility as having a good, intermediate, or poor prognosis for natural conception. […] Thus, the prognostic index for natural conception, as established with the Hunault model, can be used as a treatment selection marker in couples with unexplained infertility.

• #2 Should we adopt a prognosis-based approach to unexplained infertility?

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9662706/

  The treatment of unexplained infertility is a contentious topic that continues to attract a great deal of interest amongst clinicians, patients and policy makers. […] Over the years, several prognostic and prediction models based on routinely collected clinical data have been developed, but these are not widely used by clinicians and patients. […] In this opinion paper, we propose a prognosis-based approach such that a decision to access treatment is based on the estimated chances of natural and treatment-related conception, which, in the same couple, can change over time. […] This approach avoids treating all couples as a homogeneous group and minimizes unnecessary treatment whilst ensuring access to those who need it early. […] A key prognostic factor in unexplained infertility is female age, which has a major impact on the chances of conception both with and without treatment.

• #2 Should we adopt a prognosis-based approach to unexplained infertility?

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9662706/

  A prognosis-based approach could ensure that women who are young and have a better prognosis could benefit from a longer period of expectant management while those who are older, with a poorer chance of natural conception, could have early treatment. […] A prognosis-based paradigm of decision-making is predicated on patients and clinics having access to accurate prognostic and predictive models which can determine the chances of conception with and without treatment. […] The Hunault model, currently in use in the Netherlands, can only be used once at the point of completion of fertility investigations to estimate chances of spontaneous conception over the subsequent 12 months leading to live birth. […] Existing models have tended to rely on the analysis of relatively small- to moderate-sized datasets using multivariable regression techniques.

• #2

  https://journals.lww.com/jehp/fulltext/2025/03280/machine_learning_in_predicting_infertility.103.aspx

  The results showed that the studies used 107 various features in predictive models. The plentifulness of factors affecting the outcome of ART indicates the usefulness of ML methods for prediction. […] The vast majority of the studies have applied supervised ML approaches to predict success. In the supervised learning approach, an ML technique uses a labeled dataset to develop a predictive model. […] This systematic review provides insight into the evolving landscape of ML applications in predicting ART success and guiding future researchers and practitioners in developing more robust and clinically relevant prediction models.

• #2

  https://journals.lww.com/md-journal/fulltext/2022/12090/clinical_pregnancy_outcomes_prediction_in_vitro.93.aspx

  The findings of our study might provide a clinical guidance for the treatment in infertility patients receiving IVF-ET and improve the success rate of clinical pregnancy in those patients. […] According to the results from multivariable Logistic stepwise regression analysis, age (OR=1.093, 95%CI: 1.0361.156, P=.001), BMI (OR=1.094, 95%CI: 1.0211.176, P=.012), 3 cycles (OR=0.144, 95%CI: 0.0280.534, P=.008), HCT (OR=0.865, 95% CI: 0.7910.943, P=.001), LH (OR=0.678, 95%CI: 0.5490.823, P.001), P (OR=2.126, 95%CI: 1.1124.141, P=.024), endometrial thickness (OR=0.132, 95%CI: 0.0340.496, P=.003) and FSH (OR=1.151, 95%CI: 1.0431.275, P=.006) were predictors associated with the clinical pregnancy outcome of patients receiving IVF-ET. […] The random forest prediction model had good predictive value for distinguishing patients with high risk of poor clinical pregnancy outcome and may provide a reference for the obstetricians to make timely intervention to prevent the occurrence of poor clinical pregnancy outcome in those patients.

• #2 Proof of concept and development of a couple-based machine learning model to stratify infertile patients with idiopathic infertility | Scientific Reports

  https://www.nature.com/articles/s41598-021-03165-3

  Our validation on an independent cohort showed that: (i) the couple-modeling approach was indeed more discriminant than a model in which mens and womens are considered separately; (ii) the most important variables for the projection were related to metabolic disorders, oxidative status, and antropometry; and (iii) after reduction of our system dimension (from 80 down to 13 variables), we proposed a new algorithm to discriminate fertile vs. infertile patients. […] The proposed model could find its place in the standard care management of couples with parental project. This approach is interesting since personalized lifestyle intervention should be considered as a first-choice treatment for idiopathic infertility and should be systematicaly suggested before ART.

• #2 A new model for determining risk of male infertility from serum hormone levels, without semen analysis | Scientific Reports

  https://www.nature.com/articles/s41598-024-67910-0

  The feature importance ranking of the AI models indicated that FSH had the highest importance level among all features. […] A potential limitation of this study was the collection of a single semen sample to assess semen parameters, and the collection of a single blood sample to measure serum hormone levels, from each patient. […] Currently, male infertility is widely considered a harbinger for a man’s general health and a growing body of literature has identified male infertility as a potential biomarker for both present and future health. […] In conclusion, ability to predict the probability of male infertility without semen analysis would be useful to all physicians and male patients.

• #2 Future diagnostics in male infertility: genomics, epigenetics, metabolomics and proteomics – Krzastek – Translational Andrology and Urology

  https://tau.amegroups.org/article/view/36740/html

  The aim of this review is to outline the role of biomarkers in the diagnosis of male infertility, and to define the current role and future use of genomics, epigenetics, metabolomics and proteomics in the workup of the infertile or subfertile male. […] The limited utility of the historically used biomarkers in identifying the underlying etiology of male infertility has prompted the search for markers which would be easier to employ, more cost effective, and more predictive of reproductive outcomes. […] The study of genomics, including epigenetics, metabolomics, and proteomics, holds promise as a potential area of targeted diagnostics in the setting of male infertility. […] As we gain a deeper understanding of the genes involved, karyotyping, FISH, and gene sequencing may be able to further stratify male infertility into phenotypic subsets.

• #2

  https://link.springer.com/article/10.1007/s43032-024-01664-y

  Due to the finding that variation in FF composition reflects oocyte developmental competence and embryo viability, an increasing number of investigations into the metabolic profile of FF have been carried out to provide potential biomarkers for oocyte quality, embryo viability and even clinical pregnancy, seeking for supplemental assessment method for ART. […] As a by-product of follicular aspiration during in vitro fertilization procedures, FF may be an ideal study object considering its potent sample source and convenient acquisition and to a certain extent, FF research can be regarded as non-invasive, since obtaining FF cause no extra injury to patients. […] Although the study of FF metabolomics has unveiled considerable information of the mechanism of infertility and provided a great possibility for screening markers with diagnostic and predictive value, its effectiveness and practicability has drawn some skepticism.

• #2 Infertility – Medical Clinical Policy Bulletins | Aetna

  https://www.aetna.com/cpb/medical/data/300_399/0327.html

  No fertility treatment other than oocyte donation has been shown to be effective for women over 40 years of age with compromised ovarian reserve. Elevated follicle-stimulating hormone (FSH) and estradiol levels are independent predictors of poor prognosis in older women. […] Aetna covers ovarian stimulation medications and techniques only for women who have a biologic capacity to effectively respond to ovarian stimulation. […] Guidelines from the Society for Reproductive Medicine and Society for Assisted Reproductive Technology (Pfeifer et a., 2013) recommend that oocyte cryopreservation with appropriate counseling is recommended in patients facing infertility due to chemotherapy or other gonadotoxic therapies. […] The American Society for Reproductive Medicine (ASRM) concluded: „AMH is a promising screening test and is likely to be more useful in the general IVF population or in women at high risk for DOR than in women at low risk for DOR.” […] The authors concluded that the findings of this study did not support the routine use of ERA in an unselected patient population undergoing 1st autologous single euploid programmed ET.”

• #2 Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda | BMC Pregnancy and Childbirth | Full Text

  https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-022-04594-2

  The primary objective of this study is to explore the state-of-the-art views of research and development focusing on ML to forecast and detect different conditions of pregnancy. […] The common maternal complications responsible for the majority of maternal deaths are gestational diabetes, severe bleeding, infection, preeclampsia, eclampsia, prolonged labor, preterm labor, and unsafe abortion. […] The review study revealed multiple open issues for further investigation. Future research can be conducted on the following issues for predicting pregnancy outcomes and ensuring maternal safety. […] In the future, some unsupervised ML algorithms such as clustering algorithms can be used to aggregate data in different groups based on mothers pregnancy characteristics. […] A number of studies have been conducted with the goal of predicting preterm birth or determining the factors that cause it.

• #3 Future diagnostics in male infertility: genomics, epigenetics, metabolomics and proteomics – Krzastek – Translational Andrology and Urology

  https://tau.amegroups.org/article/view/36740/html

  The use of epigenetic changes in DNA as a marker to identify male factor infertility is difficult, as these changes may have a subtle or even insignificant biological impact, and multiple different changes may lead to similar infertility phenotypes. […] With the growing concern about the decline in male fertility, much research is being targeted towards the impact of environmental factors on sperm quality. […] The study of these factors, along with epigenetic changes to the sperm DNA, remains an attractive target for the development of specific biomarkers which may be able to identify the etiology of male infertility in a minimally invasive way.

• #3

  https://link.springer.com/article/10.1007/s43032-024-01664-y

  Therefore, in this paper, we categorically discuss the predicting value of metabolomics for IVF outcomes in terms of different infertility-related diseases. […] Therefore, researchers turned to metabolomics to obtain metabolic profiles as fingerprints for perturbation characteristics of endometriosis and furthermore, to explore biomarkers to improve the IVF outcome of patients with endometriosis. […] In order to explore biomarkers of IVF outcomes for DOR patients, 9 different oxidized lipid metabolites (20-HDoHE, 5-iso PGF2-VI, 12 S-HHTrE, 8 S,15 S-DiHETE,1a,1b-dihomo PGE2, 20-COOH-AA, PGA2, PGE1 and PGF2) were furtherly analyzed and testified to be positively correlated with DOR markers such as AMH, the number of oocytes retrieved, MII oocytes and fertilization, whilst only 20-COOH-AA was positively associated with the number of high-quality embryos after ART.

• #3 Prognosis-based management of unexplained infertility—why not?

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11075788/

  The adoption of a prognosis-based approach confers several advantages, such as increased cost-effectiveness, improved safety, and reduced invasiveness or medicalization. […] Owing to the aforementioned considerations, it is in our opinion crucial to evaluate couples with unexplained infertility to assess the individual prognosis as part of the clinical management.

• #3 Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda | BMC Pregnancy and Childbirth | Full Text

  https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-022-04594-2

  The selected studies related to ensuring maternal safety have been carried out. […] The review findings indicated that the pregnancy risks/ complications and mode of delivery were focused mostly in nearly all of the regions, while only a few studies focused on the outcome of IVF treatment. […] The review study proposed a framework adopting the ML algorithms for the future advancement of maternal healthcare through monitoring the progress of pregnancy; diagnosing avoidable pregnancy complications; recommending treatment to patients, and aiding the clinicians in decision making.

• #4 Trump is wrong on IVF—support restorative fertility instead | Catholic Culture

  https://www.catholicculture.org/commentary/trump-wrong-on-ivf-support-restorative-fertility-instead/

  Twelve meta-analyses studying 433,330 IVF/ICSI initiated pregnancies were undertaken to determine rates of preterm births from two IVF practices as contrasted to natural conceptions. […] The findings follow: The rate of preterm birth of singletons conceived through in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) is increased, being as high as 15% to 16% across Europe and the United States. […] IVF/ICSI singletons showed a significantly higher PTB risk compared to natural conception (PTB 37 weeks: OR: 1.72, 95% CI: 1.571.89; PTB32 weeks: OR: 2.19, 95% CI: 1.822.64). […] Dr. Williams urges NaProTechnology which relies on modern medicine to diagnose the cause of infertility for individual women or men. […] In contrast to IVF, Restorative Reproductive Medicine (RRM), provides a thorough evaluation of the cause of infertility and treatment to address it.

• #4

  https://link.springer.com/article/10.1007/s43032-024-01664-y

  Metabonomic study on the FF of patients with DOR not only facilitates DOR diagnosis with prediction model but also provides data support for the research of the pathogenesis of DOR as well as promising biomarkers for predicting IVF outcomes. […] These findings by metabolomics allow a deeper and thorough understanding of the pathophysiological processes of uterine abnormality and provide insights for the development of personalized approaches to improve implantation outcomes. […] In general, metabolomics exploration in tubal infertility makes great contribution to ART study. […] In a word, the metabolomic approach motivates the exploration in infertility and meanwhile provides potential biomarker discovery of IVF. […] Therefore, in this review, we classified and analyzed existing research to assess the role of metabolomic investigation in improving clinical pregnant outcomes in women undergoing according to their differed pathologic categories.

• #5 Trump is wrong on IVF—support restorative fertility instead | Catholic Culture

  https://www.catholicculture.org/commentary/trump-wrong-on-ivf-support-restorative-fertility-instead/

  RRM methods, including NaPro Technology, FEMM and NeoFertility, consistently lead to a higher rate of successful pregnancies with reduced risks and financial costs compared to IVF. […] The impressive record the Catholic Medical Society refers to has been documented by Dr. Thomas W. Hilgers at Creighton Universitys St. Paul VI Institute. […] The 2003 revision U.S. Standard Certificate of Live Birth was the first time U.S. public health forms requested IVF related complications, because Artificial Reproductive Technology (ART) complications were so common that the NCHS added Box 41 to record the adverse effect incidence and prevalence. […] Lower fertility is, in part, a result of personal life style or behavioral choices. […] The question provoked here is: What past voluntary choices that reduced childbearing capacity should now be subsidized by third parties, including tax incentives, to reverse the effects of these fertility-reducing choices? […] His suggestion that increasing IVF access would solve the problem of lower fertility, while it is certainly a possible means to that goal, does not really properly serve that goal. […] For both moral and pragmatic reasons, it is restorative fertility, not IVF, which deserves our support.

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