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Artificial Intelligence, Machine Learning, and Multi-Omics Biomarker Discovery in Ectopic Pregnancy: A Comprehensive
Zahra Ghorbaninejad Kouhbanani1,2, Muhammad Ali1
1Faculty of Medicine, Yangzhou University, Yangzhou, PR China.
Fetal and Pediatric Pathology
|August 10, 2026
Summary
Artificial intelligence (AI) and multi-omics enhance ectopic pregnancy diagnosis and treatment. These advanced technologies show promise for personalized care but require further validation before clinical use.
Area of Science:
- Reproductive medicine
- Medical artificial intelligence
- Genomics and proteomics
Background:
- Ectopic pregnancy presents significant challenges in early pregnancy diagnosis and management.
- It is a leading cause of maternal morbidity and mortality in the first trimester.
Purpose of the Study:
- To review the role of AI, machine learning, and multi-omics in improving ectopic pregnancy diagnosis.
- To assess AI's potential in treatment prediction, biomarker discovery, and surgical care for ectopic pregnancies.
Main Methods:
- A systematic review synthesized 52 studies from 1988 to 2026.
- Evaluated machine learning algorithms, proteomic/metabolomic panels, multi-omics, and robotic surgery outcomes.
- Performance metrics included Area Under the Curve (AUC), sensitivity, and specificity.
Main Results:
- Machine learning models predicted methotrexate failure (AUC up to 0.929) and surgical outcomes (>98% accuracy).
- Biomarker panels achieved high sensitivity (100%) and specificity (95.9%).
- Multi-omics identified specific gene dysregulation, and robotic surgery showed promising pregnancy rates (>60%).
Conclusions:
- AI and multi-omics integration hold potential for personalized ectopic pregnancy management.
- Further prospective, multicenter studies are needed to validate these findings for clinical application.
