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子宫内膜异位症的无创性诊断成像 第 1 部分:超声、组合成像和人工智能最新进展的系统评价。

Noninvasive diagnostic imaging for endometriosis part 1: a systematic review of recent developments in ultrasound, combination imaging, and artificial intelligence.

机构信息

Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.

Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.

出版信息

Fertil Steril. 2024 Feb;121(2):164-188. doi: 10.1016/j.fertnstert.2023.12.008. Epub 2023 Dec 14.

DOI:10.1016/j.fertnstert.2023.12.008
PMID:38101562
Abstract

Endometriosis affects 1 in 9 women and those assigned female at birth. However, it takes 6.4 years to diagnose using the conventional standard of laparoscopy. Noninvasive imaging enables a timelier diagnosis, reducing diagnostic delay as well as the risk and expense of surgery. This review updates the exponentially increasing literature exploring the diagnostic value of endometriosis specialist transvaginal ultrasound (eTVUS), combinations of eTVUS and specialist magnetic resonance imaging, and artificial intelligence. Concentrating on literature that emerged after the publication of the IDEA consensus in 2016, we identified 6192 publications and reviewed 49 studies focused on diagnosing endometriosis using emerging imaging techniques. The diagnostic performance of eTVUS continues to improve but there are still limitations. eTVUS reliably detects ovarian endometriomas, shows high specificity for deep endometriosis and should be considered diagnostic. However, a negative scan cannot preclude endometriosis as eTVUS shows moderate sensitivity scores for deep endometriosis, with the sonographic evaluation of superficial endometriosis still in its infancy. The fast-growing area of artificial intelligence in endometriosis detection is still evolving, but shows great promise, particularly in the area of combined multimodal techniques. We finalize our commentary by exploring the implications of practice change for surgeons, sonographers, radiologists, and fertility specialists. Direct benefits for endometriosis patients include reduced diagnostic delay, better access to targeted therapeutics, higher quality operative procedures, and improved fertility treatment plans.

摘要

子宫内膜异位症影响每 9 名女性和出生时被指定为女性的人中的 1 人。然而,使用传统的腹腔镜标准诊断需要 6.4 年。非侵入性成像可以实现更及时的诊断,减少诊断延迟以及手术的风险和费用。本综述更新了不断增加的探索子宫内膜异位症专家经阴道超声 (eTVUS)、eTVUS 与专家磁共振成像组合以及人工智能诊断价值的文献。我们专注于 2016 年 IDEA 共识发表后出现的文献,确定了 6192 篇出版物,并回顾了 49 项专注于使用新兴成像技术诊断子宫内膜异位症的研究。eTVUS 的诊断性能不断提高,但仍存在局限性。eTVUS 可靠地检测卵巢子宫内膜异位囊肿,对深部子宫内膜异位症具有高特异性,应被视为诊断方法。然而,阴性扫描不能排除子宫内膜异位症,因为 eTVUS 对深部子宫内膜异位症的敏感性评分中等,而对浅表子宫内膜异位症的超声评估仍处于起步阶段。子宫内膜异位症检测中人工智能这一快速发展的领域仍在不断发展,但前景广阔,尤其是在联合多模态技术方面。我们通过探讨实践改变对外科医生、超声医生、放射科医生和生育专家的影响来结束我们的评论。子宫内膜异位症患者的直接受益包括减少诊断延迟、更好地获得靶向治疗、更高质量的手术程序以及改善生育治疗计划。

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