Artificial Intelligence, Machine Learning, and Multi-Omics Biomarker Discovery in Ectopic Pregnancy: A Comprehensive Review.

IF 0.8 4区医学 Q4 PATHOLOGY

Fetal and Pediatric Pathology Pub Date : 2026-08-10 DOI:10.1080/15513815.2026.2713544

Zahra Ghorbaninejad Kouhbanani, Muhammad Ali

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引用次数: 0

Abstract

Background: Ectopic pregnancy is a major cause of first-trimester maternal morbidity and mortality, with diagnosis and management posing persistent clinical challenges. This review evaluates the emerging role of artificial intelligence, machine learning, and multi-omics technologies in enhancing diagnosis, treatment prediction, biomarker discovery, and surgical care.

Materials and methods: A synthesis of 52 studies (1988-2026) was conducted, assessing machine learning algorithms, proteomic and metabolomic panels, multi-omics integrations, and robotic-assisted surgical outcomes using performance metrics including AUC, sensitivity, and specificity.

Results: Machine learning models achieved AUC values up to 0.929 for methotrexate failure prediction and surgical accuracies exceeding 98%. Biomarker panels combining sphingolipids and carnitines demonstrated 100% sensitivity and 95.9% specificity. Multi-omics uncovered GATA4-mediated ITGB3 dysregulation in cesarean scar pregnancy. Robotic-assisted techniques yielded cumulative pregnancy rates over 60% post-reanastomosis.

Conclusion(s): AI and multi-omics integration offer significant promise for personalized ectopic pregnancy management. However, retrospective designs and small sample sizes limit generalizability, underscoring the need for prospective multicenter validation before routine clinical adoption.

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人工智能、机器学习和多组学生物标志物在异位妊娠中的发现：综合综述。

背景：异位妊娠是妊娠早期产妇发病和死亡的主要原因，其诊断和处理一直是临床挑战。本文综述了人工智能、机器学习和多组学技术在加强诊断、治疗预测、生物标志物发现和外科护理方面的新兴作用。材料和方法：对52项研究（1988-2026）进行了综合，使用包括AUC、敏感性和特异性在内的性能指标评估机器学习算法、蛋白质组学和代谢组学面板、多组学整合和机器人辅助手术结果。结果：机器学习模型对甲氨蝶呤失效预测的AUC值高达0.929，手术准确率超过98%。结合鞘脂和肉碱的生物标志物面板显示出100%的灵敏度和95.9%的特异性。多组学揭示了剖宫产瘢痕妊娠中gata4介导的ITGB3失调。机器人辅助技术在再吻合术后的累计妊娠率超过60%。结论：人工智能与多组学的结合为异位妊娠的个性化治疗提供了重要的前景。然而，回顾性设计和小样本量限制了推广，强调在常规临床应用之前需要前瞻性多中心验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Fetal and Pediatric Pathology 医学-病理学

CiteScore

3.00

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： Fetal and Pediatric Pathology is an established bimonthly international journal that publishes data on diseases of the developing embryo, newborns, children, and adolescents. The journal publishes original and review articles and reportable case reports. The expanded scope of the journal encompasses molecular basis of genetic disorders; molecular basis of diseases that lead to implantation failures; molecular basis of abnormal placentation; placentology and molecular basis of habitual abortion; intrauterine development and molecular basis of embryonic death; pathogenisis and etiologic factors involved in sudden infant death syndrome; the underlying molecular basis, and pathogenesis of diseases that lead to morbidity and mortality in newborns; prenatal, perinatal, and pediatric diseases and molecular basis of diseases of childhood including solid tumors and tumors of the hematopoietic system; and experimental and molecular pathology.