Biomarkers, Imaging, and AI Drive a New Era in Endometriosis Diagnosis

Non-Invasive Tools May Advance the Diagnosis of Endometriosis

Key Points

Highlights:

• Non-invasive diagnostics are reshaping endometriosis detection across biomarkers, imaging, and artificial intelligence.
• No single tool replaces laparoscopy, but combined approaches enable earlier and safer diagnosis.

Importance:

• Diagnostic delay in endometriosis remains driven by reliance on invasive laparoscopy.
• Advancing non-invasive strategies is critical to reduce delay, patient burden, and healthcare costs

What’s done here?

• This article is a narrative review of non-invasive diagnostic developments over the past decade.
• It focus on biomarkers, imaging (TVUS, MRI), and the emerging role of artificial intelligence in improving diagnostic accuracy.
• Also includes emerging technologies, clinical protocols, and translational efforts.
• Commercially available diagnostic products and ongoing clinical trials were summarized.

Key results:

• Biomarker research identifies circulating, endometrial, and menstrual-derived candidates, though validation remains limited.
• Standardized TVUS improves detection of deep endometriosis and endometriomas.
• MRI protocols are increasingly harmonized, enhancing diagnostic consistency and preoperative assessment.
• Artificial intelligence shows potential in pattern recognition and integration of complex datasets.
• Saliva-based testing, 109-miRNA salivary signature showed high sensitivity and specificity in validation studies.
• Still no current modality reliably detects all disease phenotypes, particularly peritoneal lesions.

Strengths and Limitations:

• Strengths are: The review provides an integrative overview of biomarkers, imaging, and artificial intelligence within a clinical framework. It highlights the convergence toward standardized imaging protocols and captures translational progress across multiple diagnostic domains.
• Limitations are: The narrative (non-systematic) design introduces potential selection bias. Biomarker studies lack robust external validation and reproducibility. Imaging remains operator-dependent, superficial peritoneal lesions remain poorly detectable; and the clinical implementation of artificial intelligence is still at an early stage.

From the Editor-in-Chief – EndoNews

"This review reflects the ongoing shift in endometriosis diagnostics—from surgical confirmation toward non-invasive, multimodal assessment. Advances in biomarkers, imaging, and artificial intelligence collectively indicate that earlier and less invasive detection is increasingly feasible.

However, these developments remain uneven. Imaging performs well for deep and ovarian disease but continues to miss superficial peritoneal lesions. Biomarker research is extensive but lacks consistency and external validation. Artificial intelligence, while promising, is not yet integrated into routine clinical workflows.

Taken together, the field is evolving, but not yet converged. No single modality currently provides a comprehensive diagnostic solution, and surgical evaluation remains central to disease assessment.

Progress will depend on integrating these approaches within personalized, multimodal, and multidisciplinary frameworks, supported by standardization and large-scale validation.

The future of diagnosis lies not in replacing laparoscopy, but in in using more selectively."

Lay Summary

Endometriosis remains a chronic condition with substantial diagnostic delay, largely due to continued reliance on invasive laparoscopy despite its limitations.

In a narrative review published in the International Journal of Gynecology and Obstetrics, Cosgriff et al. from USA summarize a decade of progress in non-invasive diagnostic approaches, focusing on biomarkers, imaging, and artificial intelligence.

Biomarker research has identified multiple candidates across blood, saliva, and menstrual fluid, with microRNAs emerging as particularly promising.

However, reproducibility and external validation remain limited. Advances in imaging—particularly standardized transvaginal ultrasound and optimized MRI protocols—have improved detection of deep infiltrating endometriosis and support preoperative assessment.

Artificial intelligence is increasingly explored as a complementary tool, enabling pattern recognition and integration of complex imaging and molecular data, though its clinical application remains in early stages.

Importantly, no current modality reliably detects all disease phenotypes, and peritoneal endometriosis remain a diagnostic challenge. Variability in laboratory methods and operator-dependent imaging further limit immediate clinical translation.

Collectively, these developments indicate a shift toward multimodal, non-invasive diagnostic strategies, although large-scale validation and standardization are required before replacing surgical diagnosis.

Research Source: https://pubmed.ncbi.nlm.nih.gov/40728311/