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Emerging Pathways to Non-Invasive Diagnosis in Endometriosis: Integrating Machine Learning, Deep Learning and
Daniel Markov1,2, Jasmin Gurung3, Usman Khalid3
1Department of General and Clinical Pathology, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria.
Diagnostics (Basel, Switzerland)
|June 26, 2026
Summary
Artificial intelligence (AI) can improve endometriosis diagnosis by analyzing complex data for better accuracy and faster detection. While promising for reducing delays and personalizing treatment, further research is needed for clinical integration.
Area of Science:
- Medical technology
- Artificial intelligence in healthcare
- Women's health
Background:
- Endometriosis affects 10-15% of reproductive-aged women, causing chronic pain and diagnostic delays.
- Current non-invasive diagnostic tests for endometriosis are often unreliable.
- Heterogeneity of endometriosis complicates diagnosis and treatment.
Purpose of the Study:
- To review current developments in artificial intelligence (AI) for endometriosis diagnosis.
- To analyze AI applications in various diagnostic fields, including imaging and multi-omics.
- To assess AI's potential to improve diagnostic efficiency, accuracy, and patient outcomes.
Main Methods:
- Review of AI applications in endometriosis diagnostics, including machine learning, deep learning, and natural language processing.
- Analysis of AI-assisted imaging for detecting endometriosis, such as in the pouch of Douglas.
- Examination of multi-omics biomarkers integrated with AI for clinical decision support.
Main Results:
- AI demonstrates potential for enhancing diagnostic accuracy and reducing delays in endometriosis detection.
- AI can aid in personalized treatment planning for endometriosis patients.
- AI applications span from image analysis to biomarker interpretation.
Conclusions:
- AI offers a promising, non-invasive adjunct to current endometriosis diagnostic methods.
- Limitations include small datasets, overfitting, and the need for external validation.
- Further research and evaluation are essential before widespread clinical implementation of AI in endometriosis care.
