ReviewCureus2026
Shared Pathophysiology and Early Detection Biomarkers in Endometriosis and Polycystic Ovary Syndrome (PCOS): Opportunities for AI-Enabled Screening.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endometriosis and polycystic ovary syndrome (PCOS) are common, multifactorial gynecological disorders shaped by endocrine imbalance, immune dysfunction, metabolic disruption, genetic susceptibility, and environmental exposures. Despite their major contribution to infertility and long-term cardiometabolic morbidity, early detection remains poor because symptoms are nonspecific, phenotypes are heterogeneous, and diagnosis is still dominated by single-modality and symptom-driven pathways. This review addresses this gap by synthesizing 2015-2025 evidence on shared and disease-specific biological mechanisms and evaluating how artificial intelligence (AI) can improve scalable screening and risk stratification. A narrative and integrative methodology was applied using peer-reviewed studies retrieved from PubMed, Scopus, Web of Science, and Google Scholar, emphasizing diagnostic rigor and external validity. Key findings identify convergent pathways involving chronic low-grade inflammation, adipokine dysregulation, oxidative stress, microbiome-mediated estrogen signaling, ferroptosis-linked iron imbalance, mitochondrial dysfunction, and epigenetic regulation through microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). Promising early-detection signals include age-stratified anti-Müllerian hormone (AMH) thresholds, circulating cell-free deoxyribonucleic acid (cfDNA) methylation markers, and reproductive tract microbial signatures. AI-based models, including transformer architectures and multimodal machine learning, show strong potential to integrate clinical, hormonal, imaging, omics, and digital symptom phenotyping into reproducible early screening frameworks. Clinical translation requires standardized diagnostic definitions, longitudinal multi-ethnic cohorts, explainable algorithms, and prospective validation. AI-enabled precision screening offers a practical pathway to shorten diagnostic delay and improve reproductive outcomes.
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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 Socratic graph.