不明位置妊娠(PUL)是早期妊娠的一种暂时病理或生理现象,需要随访以确定最终的妊娠结局。证据表明,PUL患者的不良妊娠结局发生率明显较高,以异位妊娠和早期妊娠丢失为代表,比一般人口。在过去的几十年里,关于PUL的讨论从未停止过,并且已经广泛研究了各种标记物,用于早期和准确地评估PUL,包括血清生物标志物,超声成像特征,多变量分析,基于危险分层的异位妊娠诊断。到目前为止,以M4和M6逻辑回归为代表的机器学习(ML)方法已经获得了一定的认可,并且正在不断提高。然而,PUL标记的异质性,主要是由于样本量有限,人口和技术成熟度的差异,等。,阻碍了PUL的管理。随着多学科集成和尖端技术(例如人工智能,预测模型开发,和远程医疗),新颖的标记,预计将制定PUL的管理策略。在这次审查中,我们总结了用于PUL评估和管理的常规和新颖标记(以人工智能为代表),调查他们的进步,限制和挑战,并对未来的研究方向和临床应用提出见解。
Pregnancy of unknown location (PUL) is a temporary pathologic or physiologic phenomenon of early pregnancy that requires follow up to determine the final pregnancy outcome. Evidence indicated that PUL patients suffer a remarkably higher rate of adverse pregnancy outcomes, represented by ectopic gestation and early pregnancy loss, than the general population. In the past few decades, discussion about PUL has never stopped, and a variety of markers have been widely investigated for the early and accurate evaluation of PUL, including serum biomarkers, ultrasound imaging features, multivariate analysis, and the diagnosis of ectopic pregnancy based on risk stratification. So far, machine learning (ML) methods represented by M4 and M6 logistic regression have gained a level of recognition and are continually improving. Nevertheless, the heterogeneity of PUL markers, mainly caused by the limited sample size, the differences in population and technical maturity, etc., have hampered the management of PUL. With the advancement of multidisciplinary integration and cutting-edge technologies (e.g. artificial intelligence, prediction model development, and telemedicine), novel markers, and strategies for the management of PUL are expected to be developed. In this review, we summarize both conventional and novel markers (represented by artificial intelligence) for PUL assessment and management, investigate their advancements, limitations and challenges, and propose insights on future research direction and clinical application.