ArticleScientific reports2023
Self-report symptom-based endometriosis prediction using machine learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 31 citations in OpenAlex.
- Interpretable machine learning for endometriosis classification: a rule-based approach.BMC medical informatics and decision making · 2026Article
- Clinical Value of Circulating Endometrial Cells in the Diagnosis and Stratified Diagnosis of Endometriosis.Journal of clinical medicine · 2026Article
- Using artificial intelligence to predict sexual health outcomes in endometriosis: a decision tree model algorithm.Sexual medicine · 2026Article
- Development of an early prediction model for endometriosis risk: the simplified adolescent factors for endometriosis (SAFE) score.EClinicalMedicine · 2026Article
- Article
- Current Insights into Endometriosis: Hormonal Management, Clinical Outcomes, and Opportunities for Progress.Current pharmaceutical design · 2026Review
- Non-Invasive Diagnosis of Endometriosis by Questionnaires in Patients Using Contraception.Journal of clinical medicine · 2025Article
- Utilizing Artificial Intelligence: Machine Learning Algorithms to Develop a Preoperative Endometriosis Prediction Model.Journal of minimally invasive gynecology · 2025Article
- Using artificial intelligence to predict patient outcomes from patient-reported outcome measures: a scoping review.Health and quality of life outcomes · 2025Article
- Immunomodulatory insights of monoterpene glycosides in endometriosis: immune infiltration and target pathways analysis.Hereditas · 2025Article
- Effects of dysmenorrhea on work productivity and quality of life in Japanese women: A large-scale web-based cross-sectional study.PloS one · 2025Article
- Exploring Self-Reported Symptoms for Developing and Evaluating Digital Symptom Checkers for Polycystic Ovarian Syndrome, Endometriosis, and Uterine Fibroids: Exploratory Survey Study.JMIR formative research · 2024Article
- A Comprehensive Review of Advanced Diagnostic Techniques for Endometriosis: New Approaches to Improving Women's Well-Being.Medicina (Kaunas, Lithuania) · 2024Review
- Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse Than Humans?Journal of clinical medicine · 2024Review
- Review
- Nonsurgical approaches to the diagnosis and evaluation of endometriosis.Fertility and sterility · 2024Review
- Article
- Assessing the Utility of artificial intelligence in endometriosis: Promises and pitfalls.Women's health (London, England)Review
- Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endometriosis is a chronic gynecological condition that affects 5-10% of reproductive age women. Nonetheless, the average time-to-diagnosis is usually between 6 and 10 years from the onset of symptoms. To shorten time-to-diagnosis, many studies have developed non-invasive screening tools. However, most of these studies have focused on data obtained from women who had/were planned for laparoscopy surgery, that is, women who were near the end of the diagnostic process. In contrast, our study aimed to develop a self-diagnostic tool that predicts the likelihood of endometriosis based only on experienced symptoms, which can be used in early stages of symptom onset. We applied machine learning to train endometriosis prediction models on data obtained via questionnaires from two groups of women: women who were diagnosed with endometriosis and women who were not diagnosed. The best performing model had AUC of 0.94, sensitivity of 0.93, and specificity of 0.95. The model is intended to be incorporated into a website as a self-diagnostic tool and is expected to shorten time-to-diagnosis by referring women with a high likelihood of having endometriosis to further examination. We also report the importance and effectiveness of different symptoms in predicting endometriosis.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.