Detection of peritoneal, ovarian, and bowel endometriosis using FTIR spectroscopy and machine learning

Gut Dysbiosis in Selected Gynecological Diseases Associated with Female Infertility: A Scoping Review

Nowakowski Ł et al., 2026 · Journal of clinical medicine · Free full text on PubMed Central

Background/Objectives: Female infertility represents a significant public health issue. Available evidence supports the hypothesis that the gut microbiota may play an essential role in women's reproductive health and may serve as a diagnostic or prognostic biomarker in specific gynecological disorders. A substantial part of current research concerns disturbed communication between the hypothalamic-pituitary-ovarian axis and the gut microbiota, providing the basis for analyzing this phenomenon as the gut-ovary axis or the gut-vagina-ovary axis. The primary aim of this scoping review was to map the available evidence on the relationship between gut microbiota composition and female infertility, with particular emphasis on polycystic ovary syndrome (PCOS, currently polyendocrine metabolic ovarian syndrome, PMOS) endometriosis, and uterine fibroids. The review was conducted in accordance with the PRISMA Extension for Scoping Reviews (PRISMA-ScR). PubMed, Scopus, and Google Scholar were searched using terms related to gut microbiota, female infertility, PCOS, endometriosis, and uterine leiomyomas. Peer-reviewed publications in English published between 2015 and 2025 were considered. The included studies were descriptively synthesized to identify recurring microbiota patterns and research gaps. The reviewed evidence indicates that gut dysbiosis may be associated with selected gynecological disorders affecting fertility, including PCOS, endometriosis, and uterine fibroids. The gut microbiome may have potential value as a biomarker supporting diagnosis, treatment selection, and prognosis. The gut microbiome represents a promising but still insufficiently validated area in the management of gynecological diseases associated with female infertility. Further high-quality clinical studies are needed to verify the effectiveness of microbiome-based therapies and to develop evidence-based guidelines for managing infertility associated with gut dysbiosis.

A multiple male and female pregnancy in a patient with stage IV endometriosis undergoing single embryo transfer after IVF-ICSI

Olcha P et al., 2025 · Ginekologia polska · Free to read

Effects of topical dehydroepiandrosterone therapy in women after pelvic organ prolapse surgery

Nowakowski Ł et al., 2023 · Menopause (New York, N.Y.)

Pelvic organ prolapse (POP) occurs predominantly in postmenopausal women. Restoration of the proper estrogenization of vaginal mucosa is important in preoperative and postoperative treatment, increasing the effectiveness of this approach. The objective of this study was the development of intravaginal vaginal suppositories containing DHEA and comparison of the clinical effects of vaginal topical therapy with DHEA, estradiol, or antibiotic after POP surgery. Nine types of vaginal suppositories containing 6.5 mg DHEA in different bases were prepared to find optimal formulation for the vaginal conditions. Ninety women referred for POP surgery were randomly assigned to one of three groups receiving topical treatment in the postoperative period (estradiol, DHEA, or antibiotic). On admission to hospital and during follow-up vaginal pH, vaginal maturation index and vaginal symptoms were assessed. Vaginal suppositories with the base made from polyethylene glycol 1,000 without surfactants characterized the highest percentage of the released DHEA. In women treated with topical estradiol or DHEA a significant decrease in the number of parabasal cells, increase in superficial and intermediate cells in the vaginal smears, decrease in vaginal pH, and reduction of vaginal symptoms were observed. The use of topical therapy with DHEA or the use of topical therapy with estradiol in the postoperative period were both shown to improve maturation index, vaginal pH, and vaginal symptoms. The benefits of topical therapy with DHEA after pelvic organ prolapse repair brings similar results as estradiol, without potential systemic exposure to increased concentrations of sex steroids above levels observed in postmenopausal women.

International Natural Procreative Technology Evaluation and Surveillance of Treatment for Subfertility (iNEST): enrollment and methods

Stanford JB et al., 2022 · Hum Reprod Open · Free full text on PubMed Central

What is the feasibility of a prospective protocol to follow subfertile couples being treated with natural procreative technology for up to 3 years at multiple clinical sites? Overall, clinical sites had missing data for about one-third of participants, the proportion of participants responding to follow-up questionnaires during time periods when participant compensation was available (about two-thirds) was double that of time periods when participant compensation was not available (about one-third) and follow-up information was most complete for pregnancies and births (obtained from both clinics and participants). Several retrospective single-clinic studies from Canada, Ireland and the USA, with subfertile couples receiving restorative reproductive medicine, mostly natural procreative technology, have reported adjusted cumulative live birth rates ranging from 29% to 66%, for treatment for up to 2 years, with a mean women's age of about 35 years. The international Natural Procreative Technology Evaluation and Surveillance of Treatment for Subfertility (iNEST) was designed as a multicenter, prospective cohort study, to enroll subfertile couples seeking treatment for live birth, assess baseline characteristics and follow them up for up to 3 years to report diagnoses, treatments and outcomes of pregnancy and live birth. In addition to obtaining data from medical record abstraction, we sent follow-up questionnaires to participants (both women and men) to obtain information about treatments and pregnancy outcomes, including whether they obtained treatment elsewhere. The study was conducted from 2006 to 2016, with a total of 10 clinics participating for at least some of the study period across four countries (Canada, Poland, UK and USA). The 834 participants were subfertile couples with the woman's age 18 years or more, not pregnant and seeking a live birth, with at least one clinic visit. Couples with known absolute infertility were excluded (i.e. bilateral tubal blockage, azoospermia). Most women were trained to use a standardized protocol for daily vulvar observation, description and recording of cervical mucus and vaginal bleeding (the Creighton Model FertilityCare System). Couples received medical and sometimes surgical evaluation and treatments aimed to restore and optimize female and male reproductive function, to facilitate in vivo conception. MAIN The mean age of women starting treatment was 34.0 years; among those with additional demographic data, 382/478 (80%) had 16 or more years of education, and 199/659 (30%) had a prior live birth. Across 10 clinical sites in four countries (mostly private clinical practices) with family physicians or obstetrician-gynecologists, data about clinic visits were submitted for 60% of participants, and diagnostic data for 77%. For data obtained directly from the couple, 59% of couples had at least one follow-up questionnaire, and the proportion of women and men responding to fill out the follow-up questionnaires was 69% and 67%, respectively, when participant financial compensation was available, compared to 38% and 33% when compensation was not available. Among all couples, 57% had at least one pregnancy and 44% at least one live birth during the follow-up time period, based on data obtained from clinic and/or participant questionnaires. All sites reported on female pelvic surgical procedures, and among all participants, 22% of females underwent a pelvic diagnostic and/or therapeutic procedure, predominantly laparoscopy and hysterosalpingography. Among the 643 (77%) of participants with diagnostic information, ovulation-related disorders were diagnosed in 87%, endometriosis in 31%, nutritional disorders in 47% and abnormalities of semen analysis in 24%. The mean number of diagnoses per couple was 4.7. LIMITATIONS The level of missing data was higher than anticipated, which limits both generalizability and the ability to study different components of treatment and prognosis. Loss to follow-up may also be differential and introduce bias for outcomes. Most of the participating clinicians were not surgeons, which limits the opportunity to study the impact of surgical interventions. Participants were geographically dispersed but relatively homogeneous with regard to socioeconomic status, which may limit the generalizability of current and future findings. Multicenter studies are key to understanding the outcomes of subfertility treatments beyond IVF or IUI in broader populations, and the association of different prognostic factors with outcomes. We anticipate that the iNEST study will provide insight for clinical and treatment factors associated with outcomes of pregnancy and live birth, with appropriate attention to potential biases (including adjustment for potential confounders, multiple imputation for missing data, sensitivity analysis and inverse probability weighting for potential differential loss to follow-up, and assessments for clinical site heterogeneity). Future studies will need to either have: adequate funding to compensate clinics and participants for robust data collection, including targeted randomized trials; or a scaled-down, registry-based approach with targeted data points, similar to the multiple national and regional ART registries. Funding for the study came from the International Institute for Restorative Reproductive Medicine, the University of Utah, Department of Family and Preventive Medicine, Health Studies Fund, the Primary Children's Medical Foundation, the Mary Cross Tippmann Foundation, the Atlas Foundation, the St. Augustine Foundation and the Women's Reproductive Health Foundation. The authors declare no competing interests. The iNEST study is registered at clinicaltrials.gov, NCT01363596.

Clinical use of artificial intelligence in endometriosis: a scoping review

Sivajohan B et al., 2022 · NPJ digital medicine · Free full text on PubMed Central

Endometriosis is a chronic, debilitating, gynecologic condition with a non-specific clinical presentation. Globally, patients can experience diagnostic delays of ~6 to 12 years, which significantly hinders adequate management and places a significant financial burden on patients and the healthcare system. Through artificial intelligence (AI), it is possible to create models that can extract data patterns to act as inputs for developing interventions with predictive and diagnostic accuracies that are superior to conventional methods and current tools used in standards of care. This literature review explored the use of AI methods to address different clinical problems in endometriosis. Approximately 1309 unique records were found across four databases; among those, 36 studies met the inclusion criteria. Studies were eligible if they involved an AI approach or model to explore endometriosis pathology, diagnostics, prediction, or management and if they reported evaluation metrics (sensitivity and specificity) after validating their models. Only articles accessible in English were included in this review. Logistic regression was the most popular machine learning method, followed by decision tree algorithms, random forest, and support vector machines. Approximately 44.4% (n = 16) of the studies analyzed the predictive capabilities of AI approaches in patients with endometriosis, while 47.2% (n = 17) explored diagnostic capabilities, and 8.33% (n = 3) used AI to improve disease understanding. Models were built using different data types, including biomarkers, clinical variables, metabolite spectra, genetic variables, imaging data, mixed methods, and lesion characteristics. Regardless of the AI-based endometriosis application (either diagnostic or predictive), pooled sensitivities ranged from 81.7 to 96.7%, and pooled specificities ranged between 70.7 and 91.6%. Overall, AI models displayed good diagnostic and predictive capacity in detecting endometriosis using simple classification scenarios (i.e., differentiating between cases and controls), showing promising directions for AI in assessing endometriosis in the near future. This timely review highlighted an emerging area of interest in endometriosis and AI. It also provided recommendations for future research in this field to improve the reproducibility of results and comparability between models, and further test the capacity of these models to enhance diagnosis, prediction, and management in endometriosis patients.

Assessing the Utility of artificial intelligence in endometriosis: Promises and pitfalls

Dungate B et al., 2024 · Women's health (London, England) · Free full text on PubMed Central

Endometriosis, a chronic condition characterized by the growth of endometrial-like tissue outside of the uterus, poses substantial challenges in terms of diagnosis and treatment. Artificial intelligence (AI) has emerged as a promising tool in the field of medicine, offering opportunities to address the complexities of endometriosis. This review explores the current landscape of endometriosis diagnosis and treatment, highlighting the potential of AI to alleviate some of the associated burdens and underscoring common pitfalls and challenges when employing AI algorithms in this context. Women's health research in endometriosis has suffered from underfunding, leading to limitations in diagnosis, classification, and treatment approaches. The heterogeneity of symptoms in patients with endometriosis has further complicated efforts to address this condition. New, powerful methods of analysis have the potential to uncover previously unidentified patterns in data relating to endometriosis. AI, a collection of algorithms replicating human decision-making in data analysis, has been increasingly adopted in medical research, including endometriosis studies. While AI offers the ability to identify novel patterns in data and analyze large datasets, its effectiveness hinges on data quality and quantity and the expertise of those implementing the algorithms. Current applications of AI in endometriosis range from diagnostic tools for ultrasound imaging to predicting treatment success. These applications show promise in reducing diagnostic delays, healthcare costs, and providing patients with more treatment options, improving their quality of life. AI holds significant potential in advancing the diagnosis and treatment of endometriosis, but it must be applied carefully and transparently to avoid pitfalls and ensure reproducibility. This review calls for increased scrutiny and accountability in AI research. Addressing these challenges can lead to more effective AI-driven solutions for endometriosis and other complex medical conditions.

Comprehensive Review of Endometriosis Care

Carey ET et al., 2025 · Obstetrics & Gynecology

Endometriosis is an estrogen-dependent, chronic inflammatory disorder characterized by the presence of endometrium-like tissue outside the uterus, affecting approximately 10% of individuals of reproductive age. It contributes to chronic pelvic pain, dysmenorrhea, and subfertility, resulting in substantial societal economic burdens. Genetic and environmental risk factors have been identified, and recent research suggests that endometriosis functions as a systemic disease affecting nonreproductive systems and increasing susceptibility to other health conditions. Various phenotypes-superficial peritoneal endometriosis, ovarian endometriomas, and deep endometriosis-may develop under different mechanisms, yet the relationship between these presentations remains unclear. Diagnosis relies on clinical evaluation, imaging, and surgical staging, and the advent of advanced ultrasonography and magnetic resonance imaging has helped to enhance accuracy. Although medical management focuses on hormonal modulation to alleviate symptoms, surgical intervention remains a critical tool for refractory symptoms. Postoperative care and patient education are essential to manage recurrence and to improve quality of life. Current research emphasizes the need for comprehensive, interdisciplinary approaches to endometriosis management, incorporating novel diagnostic tools, diverse therapeutic avenues, and patient-centered care models. Addressing disparities in treatment access is essential to improving outcomes. To achieve this, recruiting and analyzing data from racially, socioeconomically, and geographically diverse cohorts will reveal how disease presentation and treatment efficacy vary across populations. Continued efforts in research and health care policy are necessary to develop effective and personalized strategies in managing endometriosis.

An artificial intelligence approach for investigating multifactorial pain-related features of endometriosis

Kiser AC et al., 2024 · PLoS One · Free full text on PubMed Central

Endometriosis is a debilitating, chronic disease that is estimated to affect 11% of reproductive-age women. Diagnosis of endometriosis is difficult with diagnostic delays of up to 12 years reported. These delays can negatively impact health and quality of life. Vague, nonspecific symptoms, like pain, with multiple differential diagnoses contribute to the difficulty of diagnosis. By investigating previously imprecise symptoms of pain, we sought to clarify distinct pain symptoms indicative of endometriosis, using an artificial intelligence-based approach. We used data from 473 women undergoing laparoscopy or laparotomy for a variety of surgical indications. Multiple anatomical pain locations were clustered based on the associations across samples to increase the power in the probability calculations. A Bayesian network was developed using pain-related features, subfertility, and diagnoses. Univariable and multivariable analyses were performed by querying the network for the relative risk of a postoperative diagnosis, given the presence of different symptoms. Performance and sensitivity analyses demonstrated the advantages of Bayesian network analysis over traditional statistical techniques. Clustering grouped the 155 anatomical sites of pain into 15 pain locations. After pruning, the final Bayesian network included 18 nodes. The presence of any pain-related feature increased the relative risk of endometriosis (p-value < 0.001). The constellation of chronic pelvic pain, subfertility, and dyspareunia resulted in the greatest increase in the relative risk of endometriosis. The performance and sensitivity analyses demonstrated that the Bayesian network could identify and analyze more significant associations with endometriosis than traditional statistical techniques. Pelvic pain, frequently associated with endometriosis, is a common and vague symptom. Our Bayesian network for the study of pain-related features of endometriosis revealed specific pain locations and pain types that potentially forecast the diagnosis of endometriosis.