Endometriosis has been globally recognized as one of the most common and yet under-diagnosed gynecologic disorders, with current estimates suggesting that approximately one in 10 women of reproductive age are affected.¹ Impacting tens of millions globally, endometriosis often presenting with chronic pelvic pain, menstrual irregularities, and fertility challenges.²
Sean Raj, MD, MBA
Despite its prevalence, the path to diagnosis remains frustratingly long, frequently spanning seven to 10 years from the onset of symptoms to definitive diagnosis.³ This has been attributed to the nonspecific nature of the condition and its varied clinical presentation, mainly due to the overdependence on invasive surgical diagnosis. However, emerging developments in preventive screening and multidisciplinary care are beginning to reshape the future of endometriosis management.
The case for earlier identification
Traditionally, the diagnosis of endometriosis has been established through laparoscopic procedures, with histopathological confirmation. However, approaches primarily based on surgical interventions are inherently reactive, undertaken only after the patient has suffered for years with pain and/or infertility. Patient-reported data goes as far to show that as many as 42% of women are initially told their pain is just “part of being a woman.”⁴
The consequences are significant. Delayed diagnosis is associated with central sensitization, diminished quality of life, infertility, and mental health burden. This is especially exaggerated considering symptom severity does not always correlate with disease burden. Some individuals with extensive disease report mild discomfort, while others with smaller lesions experience debilitating pain. While laparoscopic confirmation remains important for operative planning, relying exclusively on invasive diagnosis has perpetuated delayed care for many patients.
This is where a preventive mindset becomes critical. Earlier recognition of symptom patterns, which include chronic dysmenorrhea, cyclic bowel or bladder symptoms, and pain affecting daily function, should trigger structured referral pathways for imaging. This approach mirrors what we’ve already embraced in other areas of women’s health, such as breast cancer screening: identify risk early, image appropriately, and intervene sooner.
The evolving role of advanced imaging
Advanced imaging has shifted beyond a confirmatory role to a diagnostic and management tool with a meaningful impact on patient outcomes. Transvaginal ultrasound (TVUS) and MRI both show strong diagnostic performance in detecting deep infiltrating endometriosis (DIE), a clinically significant subtype of the condition. Pelvic MRI additionally offers superior soft-tissue contrast and allows radiologists to evaluate areas where endometriosis commonly hides, including ovarian endometriomas and involvement of the uterosacral ligaments.
Recent data reinforce this shift. Studies show TVUS sensitivity for rectosigmoid DIE ranging from approximately 79% to 94%, while MRI demonstrates sensitivity up to 94%, with both modalities showing high specificity.⁵ A complementary meta-analysis showed the sensitivity and specificity of MRI in the detection of rectosigmoid DIE, and as 85% and 92% for TVUS.⁶ When performed with high-quality technique and interpreted by experienced readers, these modalities can be highly complementary, and in some cases, equivalent.
These figures emphasize the significance of image quality and reader expertise. Structured MRI reporting, including standardized lesion characterization and compartment mapping, has been shown to have a significant impact on preoperative planning and surgical decision-making without the immediate need for invasive procedures.⁷
For imaging to reach its full potential, radiology should emphasize standardized imaging protocols, thorough interpretations, and structured reporting. Special sequences in imaging, such as multiplanar high resolution T2 and fat-suppressed T1, have become vital in mapping disease and surgical planning. We are also gaining deeper insight into more complex disease patterns, including pelvic nerve involvement, which has meaningful implications for surgical risk and patient outcomes.⁸
AI: A catalyst for consistency and scale
The integration of artificial intelligence and imaging is opening new doors to the possibility of earlier detection and quantification. AI-assisted tools can support radiologists in identifying subtle findings, improving consistency across readers, and reducing operator dependence. A recent scoping review highlights AI’s potential to enhance diagnostic accuracy and standardize interpretation across imaging modalities.⁹
The preliminary findings of hybrid AI techniques have demonstrated enhanced nerve involvement and anatomy, which emphasize the importance of AI in creating reproducible image biomarkers and creating large-scale screening strategies to support risk stratification.¹⁰
Radiology has already seen how AI can transform breast imaging and lung cancer screening workflows. A similar evolution in pelvic imaging could enable more scalable, standardized detection, particularly important for a condition as heterogeneous and challenging as endometriosis. While these technologies are still maturing, they have the potential to support risk stratification models that use a combination of clinical features, symptom scores, and image phenotypes to prioritize patients for early, targeted imaging.
Multidisciplinary care and equitable access
Endometriosis is not an isolated condition limited to a single organ system, and its management cannot be either. Radiologic findings often serve as the foundation for multidisciplinary collaboration, informing gynecologists, colorectal surgeons, urologists, fertility specialists, and pain management teams. This coordinated approach is essential for delivering personalized, effective care.
However, access remains uneven. Variability in imaging quality, limited availability of specialized protocols, and persistent diagnostic bias all contribute to disparities in care. It is also a challenge for the radiology leadership to ensure the availability of quality imaging protocols and the elimination of bias among the clinicians who diagnose the condition.
Looking ahead: Biomarkers and integrated diagnostics
The future of endometriosis diagnosis is expected to include biomarkers and multimodal diagnostics with imaging. Currently, no validated blood or menstrual biomarker exists for screening or diagnosis. However, ongoing research suggests that combining biomarkers with imaging may enable predictive models for earlier detection.
Emerging research on the molecular composition of menstrual fluid is exploring the possibility of using a less invasive approach for the diagnosis of endometriosis. While these strategies remain investigational, they reflect a wider movement toward layered, risk-based evaluation rather than overreliance on surgical confirmation.
Advances in imaging and AI continue to improve how we evaluate complex conditions like endometriosis. Emerging AI-enhanced MRI tools hold promise for further improving detection and consistency in interpretation. As these technologies evolve, they may contribute to more standardized assessments and better integration of imaging findings with clinical risk profiles.
Conclusion
The future of endometriosis care is not about honing in on a single diagnostic tool. Developing a comprehensive and preventive diagnostic strategy that incorporates advanced imaging and emerging technologies will define the next chapter. Earlier recognition, advanced imaging, AI-driven consistency, and multidisciplinary care must come together to create a more proactive, patient-centered diagnostic pathway.
Radiology is uniquely positioned to lead this transformation. By shortening the time from symptom onset to diagnosis, we can reduce years of uncertainty, and ultimately, improve outcomes for millions of patients worldwide.
References
1. World Health Organization. (2025, October 15). Endometriosis. https://www.who.int/news-room/fact-sheets/detail/endometriosis.
2. Leoni, C., Buggio, L., Diani, F., Somigliana, E., & Vercellini, P. (2025). Quality of life in women with endometriosis: The importance of socio-demographic, diagnostic-therapeutic, and psychological factors. Journal of Clinical Medicine, 14(12), 4268. https://doi.org/10.3390/jcm14124268.
3. https://obgyn.onlinelibrary.wiley.com/doi/full/10.1111/1471-0528.17973.
4. Surgical Review Corporation. (2025, March 31). Dr. Ramiro Cabrera Carranco is helping shape the future of endometriosis treatment in Mexico. Surgical Review. https://www.surgicalreview.org/dr-ramiro-cabrera-carranco-is-helping-shape-the-future-of-endometriosis-treatment-in-mexico/.
5. O’Leary, M., Neary, C., & Lawrence, E. (2025). The diagnostic accuracy of magnetic resonance imaging versus transvaginal ultrasound in deep infiltrating endometriosis and their impact on surgical decision‑making: A systematic review. Diagnostics, 15(22), 2856. https://doi.org/10.3390/diagnostics15222856.
6. Xu, Z., Li, Y., Wang, Y., Wan, Y., & Chen, J. (2025). Transvaginal ultrasound and magnetic resonance imaging in detecting rectosigmoid deep infiltrating endometriosis: A comparative meta‑analysis. Frontiers in Medicine, 12, Article 1552185. https://doi.org/10.3389/fmed.2025.1552185.
7. Yekta Yuruk, Y., Sam Ozdemir, M., Simsar, M., & Sahin H. (2025). A review of the MRI features of endometriosis: What should be paid attention to during the reporting process? Abdominal Radiology, 50, 6052–6063. https://doi.org/10.1007/s00261-025-05002-x.
8. Bourg, J., Ruaux, E., Adrien Bolze, P., Gavrel, M., Charlot, M., Golfi er, F., Thomassin-Naggara, I., & Rousset, P. (2025). Pelvic nerve endometriosis: MRI features and key findings for surgical decision. Insights into Imaging. https://doi.org/10.1186/s13244-025-02005-6.
9. AlSaad, R., Farrell, T., Elhenidy, A., Albasha, S., & Thomas, R. (2026). Artificial intelligence in endometriosis imaging: A scoping review. AI, 7(2), 43. https://doi.org/10.3390/ai7020043.
10. La Barbera, G., Bonnot, E., Isla, T., de la Plata, J. P., Dunoyer de Segonzac, J.‑R., Attali, J., Lozach, C., Bellucci, A., Marcellin, L., Fournier, L., Sarnacki, S., Gori, P., & Bloch, I. (2025). Visionerves: Automatic and reproducible hybrid AI for peripheral nervous system recognition applied to endometriosis cases (arXiv:2509.18185). https://arxiv.org/abs/2509.18185.
Sean Raj, MD, MBA, is chief medical officer and chief innovation officer at SimonMed.
The comments and observations expressed are those of the author and do not necessarily reflect the opinions of AuntMinnie.com.