Research Progress on Recurrence Indicators of Endometriosis

Wanting Wang, Fei Li
In: Journal of Artificial Intelligence and Information · 2026 · vol. 8(6) , pp. 215–219 · doi:10.66069/ojspub.20542241 · W7166677194
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This review summarizes the advantages and disadvantages of single detection indicators and combined serological and digital prediction models for endometriosis recurrence to address diagnostic challenges.

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This review article examines the challenges associated with detecting endometriosis recurrence due to the limitations of current diagnostic methods, such as the invasiveness of laparoscopy and the low specificity of serological markers. The authors systematically evaluate various single detection indicators and analyze combined models that integrate serological markers with digital prediction tools to improve diagnostic accuracy. While acknowledging progress in individual biomarkers, the paper highlights the absence of a comprehensive systematic review for high-precision recurrence prediction models in postoperative populations. This paper is centrally about endometriosis — specifically focusing on identifying and evaluating indicators and models for predicting disease recurrence after surgical treatment.

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Abstract

Endometriosis (EMS) is a refractory gynecological disease with a high recurrence rate. Its clinical symptoms are mainly dysmenorrhea and infertility, showing diverse manifestations. Due to the atypical clinical symptoms and low specificity of diagnostic markers, the recurrence of EMS cannot be detected in time, which puts great pressure on patients both physically and mentally. Moreover, neither single clinical symptoms nor serological examinations can accurately diagnose the recurrence of EMS, and laparoscopic examination, as the gold standard for diagnosis, is an invasive examination. Therefore, it is urgent to find a prediction scheme with high sensitivity and specificity. Although some studies have shown that certain progress has been made in the diagnostic models of EMS, there is still a lack of systematic review of high - precision prediction models for the recurrence of EMS in the postoperative population. This review systematically summarizes the advantages and disadvantages of various single detection indicators, and at the same time sorts out and summarizes the combined detection models of serological markers and digital prediction models, providing reference for predicting risk factors in the research of the EMS recurrence system.
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Research Progress on Recurrence Indicators of Endometriosis DOI: https://doi.org/10.66069/ojspub.20542241Keywords: Endometriosis, Prediction, RecurrenceAbstract Endometriosis (EMS) is a refractory gynecological disease with a high recurrence rate. Its clinical symptoms are mainly dysmenorrhea and infertility, showing diverse manifestations. Due to the atypical clinical symptoms and low specificity of diagnostic markers, the recurrence of EMS cannot be detected in time, which puts great pressure on patients both physically and mentally. Moreover, neither single clinical symptoms nor serological examinations can accurately diagnose the recurrence of EMS, and laparoscopic examination, as the gold standard for diagnosis, is an invasive examination. Therefore, it is urgent to find a prediction scheme with high sensitivity and specificity. Although some studies have shown that certain progress has been made in the diagnostic models of EMS, there is still a lack of systematic review of high - precision prediction models for the recurrence of EMS in the postoperative population. This review systematically summarizes the advantages and disadvantages of various single detection indicators, and at the same time sorts out and summarizes the combined detection models of serological markers and digital prediction models, providing reference for predicting risk factors in the research of the EMS recurrence system. Downloads Published How to Cite Issue Section License Copyright (c) 2026 Wanting Wang, Fei Li This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License. Deprecated: json_decode(): Passing null to parameter #1 ($json) of type string is deprecated in /www/bryanhousepub/ojs/plugins/generic/citations/CitationsPlugin.inc.php on line 49

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