Endometriosis · Diagnosis
Abstract
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.
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By this author
Pelvic Pain · Chronic Pelvic Pain
Endometriosis Pain Index: development of a model to predict poor pain-related quality of life after endometriosis surgery through machine learning analysis of registry data
Tucker DR et al., 2026 · Pain · Free to read
Predictive tools are lacking for pain-related outcomes after endometriosis surgery. The objective of this study was to develop and validate a machine learning-based clinical model to predict poor pain-related quality of life after endometriosis surgery. Registry data from a prospective longitudinal cohort at a tertiary referral center (2013-2020) was used for model development and validation. Participants underwent an index endometriosis surgery, and completed the pain subscale of the Endometriosis Health Profile-30 (EHP-30) at baseline and 1-2-year follow-up. The outcome was poor pain-related quality of life defined as EHP-30 pain subscale above the 75th percentile for North America, at 1 to 2 years postsurgery. Thirty-two preoperative factors were evaluated, with final models retaining the top 10 most important predictors. Elastic net logistic regression, random forest (RF) and multilayer perceptron neural network models were developed. Internal validation was performed using 500 bootstrap samples, and a held-out test cohort. The study included 650 488 in the training cohort and 162 in a held-out test cohort. The RF model exhibited the most consistent discrimination, measured by the area under the receiver operating characteristic curve, between the training cohort (0.768; 95% CI: 0.690-0.837) and test cohort (0.766; 95% CI: 0.676-0.863, Δ = -0.002). The RF model also demonstrated the best integrated calibration index (0.029) and highest net benefit. Final preoperative predictors for the RF model included baseline EHP-30 score, surgery type (conservative fertility-sparing vs hysterectomy), anxiety scores, depression scores, pain catastrophizing scale scores, abdominal wall pain, pelvic floor myalgia, smoking status, back pain, and race/ethnicity. We present the RF model as the Endometriosis Pain Index to aid preoperative counselling for endometriosis surgery.
Pelvic Pain · Chronic Pelvic Pain
Endometriosis, symptoms, and risk for depression and/or anxiety: a population-based retrospective study
Goodwin E et al., 2025 · BMC women's health · Free full text on PubMed Central
Endometriosis is a chronic and inflammatory condition that often presents with chronic pelvic pain, dysmenorrhea, and dyspareunia, thus having important effects on quality of life. There are two proposed hypotheses to describe the known association between endometriosis and depression and anxiety: (1) the disease hypothesis, where the inflammatory nature of endometriosis is driving increased risk for depression and anxiety; and (2) the pain hypothesis, where it is the painful symptoms underlying the increased risk. We aimed to shed further light on these two hypotheses by assessing the risk for depression and/or anxiety in three groups of patients pathologically assessed for endometriosis: symptomatic endometriosis patients (Symp Endo), symptomatic patients with no pathological endometriosis diagnosis (Symp No Endo), and asymptomatic endometriosis patients (Asymp Endo). This study included pathologically-confirmed endometriosis patients identified from the pathology records of Vancouver Coastal Health Authority between 2000 and 2008. These data were linked with population-based administrative health data for follow-up to 2017. Depression and anxiety were identified through diagnostics codes from health services use data and prescriptions for antidepressants. Bivariate analyses were performed to assess differences between groups. Cox proportional hazards models were run to generate hazard ratios for incident depression and/or anxiety between the groups. There were 2729 people in Symp Endo, 585 in Symp No Endo, and 326 in Asymp Endo. Symp No Endo was more likely than Symp Endo to be visiting a physician for pelvic pain and to be taking prescription-level pain medications (p < 0.001). After adjusting for several covariates, Symp No Endo had a significantly higher risk of incident depression and/or anxiety (adjusted HR: 1.23, 95% CI: 1.06-1.41) compared to Symp Endo. There was no statistically significant difference in risk of depression and/or anxiety between Symp Endo and Asymp Endo (adjusted HR: 0.94, 95% CI: 0.76-1.17). These results point toward the pain focused hypotheses as Symp No Endo patients were at higher risk for depression/anxiety than the Symp Endo group. However, the results also suggest the disease hypothesis is at play, because the Symp Endo and Asymp Endo groups were at the same level of risk for depression/anxiety.
Sexual Pain · Dyspareunia
Female Dyspareunia and the Relationship to Neurophysiologic Mechanisms: A Scoping Review
Cook E et al., 2026 · Journal of minimally invasive gynecology
This scoping review aims to evaluate recent studies that examine the relationship between dyspareunia and neurophysiologic factors, and to synthesize their results as it pertains to the development and treatment of introital/vulvar dyspareunia and deep dyspareunia A comprehensive search was conducted in PubMed (NLM), Embase (Elsevier), CINAHL (EBSCOhost), Web of Science (Clarivate), Psycinfo (ProQuest), and Cochrane Library (Wiley) to find peer reviewed studies written in English published in 2000 or later that discussed how neurophysiology is related to dyspareunia. Search terms: dyspareunia; painful intercourse; genito-pelvic pain; penetration disorder; neuropsychology; central nervous system sensitization; neur; central sensitization. A total of 1101 studies were screened and 108 were included in the review. Abstract and full text screening were performed by 4 authors. Articles were also excluded if they did not include an objective diagnostic tool or objective treatment outcome of dyspareunia. We included original peer-reviewed published research in the form of randomized control trials, cohort studies, case control studies, case series of greater than 20 participants, and systematic reviews. Multiple study types were noted: 22 randomized control trials, 9 prospective cohort studies, 3 retrospective cohort studies, 30 case control, 16 case series, 17 cross-sectional, and 11 systematic reviews. Of these articles, 72 focused on introital/superficial dyspareunia, 23 focused on deep dyspareunia, and 13 on both. Data was synthesized in text and table format, separated by type of dyspareunia (introital vs deep) and either etiology/diagnosis or treatment. There are complex neurophysiologic mechanisms that influence both introital and deep dyspareunia, highlighting the roles of peripheral and central sensitization, nerve fiber density, and neuroplasticity in this condition. There are several promising treatments, including TENS, botulinum toxin A, physical therapy, and various multimodal approaches; but further research is needed to establish standardized therapeutic guidelines.
Pelvic Pain · Chronic Pelvic Pain
Pain sensitivity questionnaire in endometriosis
Gentles AJ et al., 2025 · The journal of pain · Free to read
There is increasing recognition that nociplastic pain and central sensitization may play a role in endometriosis-associated pain. The Pain Sensitivity Questionnaire Minor (PSQ-M) evaluates subjective widespread pain sensitivity, and is linked to pain outcomes in chronic pain populations. However, evidence connecting the PSQ-M to central sensitization in endometriosis is limited. Using the Central Sensitization Inventory (CSI) as a comparison, this study compared the PSQ-M as a clinical proxy for central sensitization in endometriosis individuals. Data collected from 983 endometriosis participants (mean age of 34 years), between January 2020 and December 2022, were analyzed from a prospective registry. A significant but weak positive correlation was observed between PSQ-M and CSI scores (r=0.099, p<0.001). A significant but weak correlation was found between the number of central sensitivity syndromes and pelvic pain-related comorbidities with the PSQ-M (r=0.093, p<0.001), compared to a stronger correlation with the CSI (r=0.687, p<0.05). PSQ-M scores were not significantly associated with baseline (r=0.013, p=0.797) or post-operative (r=-0.046, p=0.801) quality-of-life. There was no change in the PSQ-M and a small change in CSI after endometriosis surgery, suggesting that surgical treatment of endometriosis does not directly address central sensitization. In conclusion, the PSQ-M may not be the optimal clinical proxy for central sensitization in endometriosis. This study evaluates the Pain Sensitivity Questionnaire - Minor (PSQ-M) as a proxy for central sensitization in endometriosis. The PSQ-M showed weak correlations with central sensitivity syndromes and pain scores and was not associated with post-surgical quality-of-life, suggesting it may not be the optimal tool for assessing central sensitization in endometriosis.
Related research
Diagnosis · Diagnostic Delay
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.
Diagnosis · Diagnostic Delay
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.
Diagnosis · Staging and Classification
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.
Diagnosis · Biomarkers
Detection of peritoneal, ovarian, and bowel endometriosis using FTIR spectroscopy and machine learning
Olcha P et al., 2026 · Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study evaluated the diagnostic potential of Fourier-transform infrared (FTIR) spectroscopy combined with machine learning for the detection of ovarian, bowel, and peritoneal endometriosis. The Boruta algorithm was applied to identify the most informative spectral intervals for each endometriosis type, revealing characteristic wave number ranges associated with molecular changes in endometriotic tissue. For ovarian endometriosis, key intervals included 741-748 cm-1, 984-993 cm-1, and 1125-1132 cm-1 bowel endometriosis, 1055-1063 cm-1, 1077-1079 cm-1, 1561-1572 cm-1, and 1717-1720 cm-1, and for peritoneal endometriosis, 917-919 cm-1, 1542-1547 cm-1, and 1573-1576 cm-1. Three machine learning algorithms, Deep Learning (DL), Support Vector Machine (SVM), and XGBoost, were tested using both the full spectral range and the Boruta-selected feature subsets. Across all endometriosis types, XGBoost consistently outperformed DL and SVM. Using the full spectrum, XGBoost achieved accuracies of 0.81, 0.77, and 0.78 for ovarian, bowel, and peritoneal endometriosis, respectively. Feature selection with Boruta significantly improved performance, increasing accuracies to 0.93, 0.88, and 0.90, respectively, and enhancing sensitivity, specificity, precision, F1 score, MCC, and ROC AUC across all datasets. DL models often exhibited high sensitivity but poor specificity, while SVM performance improved moderately with feature selection. Overall, these results demonstrate that targeted spectral feature selection enhances the diagnostic accuracy of machine learning models for endometriosis. XGBoost, in combination with Boruta-selected spectral intervals, provides the most reliable and balanced predictions, highlighting its potential for non-invasive detection and differentiation of ovarian, bowel, and peritoneal endometriotic lesions.
Endometriosis › Diagnosis › Imaging · General Gynecology › Clinical Practice › Practice Patterns · Diagnostics › Ultrasound › Transvaginal Ultrasound
Brie Dungate, Dwayne R Tucker, Emma Goodwin
B Dungate, D Tucker, E Goodwin
PMID 38686828 38686828 DOI 10.1177/17455057241248121 10.1177/17455057241248121 Dungate et al. 2024, Dungate 2024