ArticleBMC women's health2024
Application of machine learning techniques in the diagnosis of endometriosis.
Article in BMC women's health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Endometriosis: Epidemiology, Risk Factors, Molecular Mechanisms, Diagnosis, and Management.MedComm · 2026Review
- Interpretable machine learning for endometriosis classification: a rule-based approach.BMC medical informatics and decision making · 2026Article
- sMICA/sMICB and Immune Checkpoint in Endometriosis: Toward a Minimally Invasive Diagnostic Model Based on Machine Learning.Biomedicines · 2026Article
- Article
- A Combined Transcriptomic and Machine Learning Study Reveals PAX8 as a Promising Diagnostic Biomarker in Endometriosis.Human mutation · 2026Article
- Potential biomarkers for early detection of endometriosis: current state of art (what we know so far).Journal of applied genetics · 2025Review
- Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset.World journal of clinical cases · 2025Article
- The Role of Artificial Intelligence in Female Infertility Diagnosis: An Update.Journal of clinical medicine · 2025Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of this study is to assess the use of machine learning methodologies in the diagnosis of endometriosis (EM).
methodsThis study included a total of 106 patients with EM and 203 patients with non-EM conditions (like simple cysts and simple uterine fibroids), all admitted to the Shunyi Women's and Children's Hospital of Beijing Children's Hospital between January 2017 and September 2022. All participants were free of comorbidities and their diagnoses were confirmed via postoperative pathology. Comparative analysis was conducted between the EM and non-EM groups. Baseline data were assessed, including white blood cell count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, mean platelet volume, hemoglobin, carbohydrate antigen 125 (CA125), carbohydrate antigen 199, coagulation parameters, and other serologic indicators. An optimal predictive model was developed using an artificial intelligence algorithm to determine the presence of EM. The objective is to provide new insights for the clinical diagnosis and treatment of EM.
resultsThe random forest algorithm demonstrated superior performance when compared to decision trees, LogitBoost, artificial neural networks, naïve Bayes, support vector machines, and linear regression in machine learning methods. Combining CA125 with the NLR yielded a better prediction of EM than using CA125 alone when applying the random forest algorithm. The accuracy of predicting EM with CA125 combined with NLR was 78.16%, with a sensitivity of 86.21% and an area under the curve (AUC) of 0.85 (P < 0.05). In contrast, using CA125 alone resulted in an EM prediction accuracy of 75.8%, with a sensitivity of 79.3% and an AUC of 0.82 (P < 0.05).
conclusionThe diagnostic value of serum CA125 combined with the NLR for EM is higher than that of serum CA125 alone. This finding indicates that NLR could serve as a new supplementary biomarker along with serum CA125 in the diagnosis of EM.
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