ArticleScientific reports2022
Machine learning algorithms as new screening approach for patients with endometriosis.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06100471 (Evaluation of Genetic Signature in Endometriosis Disease by Non Invasive Sampling), which is not on this map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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.
Evaluation of Genetic Signature in Endometriosis Disease by Non Invasive Sampling
Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.
- Artificial intelligence-based risk assessment tools for sexual, reproductive and mental health: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Predictive markers of endometriosis: a future perspective.Archives of gynecology and obstetrics · 2026Review
- Emerging Pathways to Non-Invasive Diagnosis in Endometriosis: Integrating Machine Learning, Deep Learning and Multi-Omics Biomarkers.Diagnostics (Basel, Switzerland) · 2026Review
- 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
- sMICA/sMICB and Immune Checkpoint in Endometriosis: Toward a Minimally Invasive Diagnostic Model Based on Machine Learning.Biomedicines · 2026Article
- Systemic immune-inflammation (SII) index as a novel prognostic biomarker in critically ill patients with sepsis: analysis of the MIMIC-IV cohort and predictive modeling based on machine learning.BMC infectious diseases · 2026Article
- Identifying key determinants of cumulative live birth in women with ovarian endometrioma undergoing ethanol sclerotherapy followed byFrontiers in cell and developmental biology · 2026Article
- Non-Invasive Diagnosis of Endometriosis by Questionnaires in Patients Using Contraception.Journal of clinical medicine · 2025Article
- Urine and Serum miRNA Signatures for the Non-Invasive Diagnosis of Adenomyosis: A Machine Learning-Based Pilot Study.Diagnostics (Basel, Switzerland) · 2025Article
- Potential biomarkers for early detection of endometriosis: current state of art (what we know so far).Journal of applied genetics · 2025Review
- Endometriosis MDC: role of the radiologist.Abdominal radiology (New York) · 2025Review
- Screening for endometriosis: A scoping review of screening measures that could support early diagnosis.BMC women's health · 2025Article
- Role of Salivary MicroRNA as a Marker of Progesterone Resistance in Endometriosis: Preliminary Results from a Single-Institution Experience.Biomolecules · 2025Article
- Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity.Scientific reports · 2025Article
- miRNA expression haplotype in Hispanics with endometriosis.Frontiers in reproductive health · 2025Article
- AI-Powered Clinical Decision Support Systems in Disease Diagnosis, Treatment Planning, and Prognosis: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2025Review
- Identifying and characterizing clinical subgroups in individuals with endometriosis.Frontiers in pain research (Lausanne, Switzerland) · 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
- Machine Learning-Based Detection of Endometriosis: A Retrospective Study in A Population of Iranian Female Patients.International journal of fertility & sterility · 2024Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endometriosis-a systemic and chronic condition occurring in women of childbearing age-is a highly enigmatic disease with unresolved questions. While multiple biomarkers, genomic analysis, questionnaires, and imaging techniques have been advocated as screening and triage tests for endometriosis to replace diagnostic laparoscopy, none have been implemented routinely in clinical practice. We investigated the use of machine learning algorithms (MLA) in the diagnosis and screening of endometriosis based on 16 key clinical and patient-based symptom features. The sensitivity, specificity, F1-score and AUCs of the MLA to diagnose endometriosis in the training and validation sets varied from 0.82 to 1, 0-0.8, 0-0.88, 0.5-0.89, and from 0.91 to 0.95, 0.66-0.92, 0.77-0.92, respectively. Our data suggest that MLA could be a promising screening test for general practitioners, gynecologists, and other front-line health care providers. Introducing MLA in this setting represents a paradigm change in clinical practice as it could replace diagnostic laparoscopy. Furthermore, this patient-based screening tool empowers patients with endometriosis to self-identify potential symptoms and initiate dialogue with physicians about diagnosis and treatment, and hence contribute to shared decision making.
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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.