AI-Enabled Diagnosis of Spontaneous Rupture of Ovarian Endometriomas: A PSO Enhanced Random Forest Approach

Mingyan Zhou, Feng Lin, Qian Hu, Zhenzhou Tang, Chu Jin
In: IEEE Access · 2020 · vol. 8 , pp. 132253–132264 · doi:10.1109/access.2020.3008473 · W3041931127
article OA: gold CC0 ⤵ 1 in-corpus citation
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This study developed a particle swarm optimization enhanced random forest (PSO-RF) model that accurately diagnoses spontaneous rupture of ovarian endometriomas, outperforming other machine learning models.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 ⓘ

This study investigated whether an AI-enabled machine-learning model can preoperatively diagnose spontaneous rupture of ovarian endometriomas using physiological data from premenopausal women with ovarian endometriomas treated at a single hospital between 2006 and 2017 (193 records; 53 ruptured, 140 unruptured), including blood counts and biomarker and surgical/laparotomy-derived features. The authors developed a PSO-enhanced random forest (PSO-RF) framework that treats rupture as a 0–1 classification task, using random forest feature ranking and particle swarm optimization to tune key model parameters, and they benchmarked it against eight other hyperparameter-optimized classifiers for fairness. PSO-RF achieved reported performance of 97.47% accuracy, AUC 0.996, sensitivity 94.12%, and specificity 98.39%. The paper’s key caveat is that evaluation is based on practical data collected from a local hospital (implying limited external generalizability beyond that dataset). This paper is centrally about endometriosis — specifically, it focuses on AI-enabled preoperative diagnosis of spontaneous rupture of ovarian endometriomas, a form of endometriosis.

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Abstract

Spontaneous rupture of ovarian endometriomas (OEs) may cause serious injury to patients. However, in traditional clinical diagnosis, it is vulnerable to be ignored or misdiagnosed for the symptoms of the acute abdomen caused by it are quite similar to those of some common gynecological emergencies, which leads to serious complications to patients. In view of this, this study investigates the AI-enabled, early and accurate diagnosis of spontaneous rupture of OEs. Although artificial intelligence (AI) has been proved to be a powerful tool to help to make accurate clinical diagnosis, however, as far as we know, there is so far no report on AI-enabled diagnosis of spontaneous rupture of OEs yet. Specifically, this study proposes a particle swarm optimization (PSO) enhanced random forest (RF) classification model, called PSO-RF, to make diagnosis, where RF is used to rank feature importance and make diagnosis considering that an OE is ruptured or not is a typical 0-1 classification problem, and PSO is leveraged to fine-tune the essential parameters of RF. The performance of the proposed PSO-RF model is evaluated with practical data collected from a local hospital and fully benchmarked by comparing with eight other machine learning models whose key parameters are sufficiently optimized as well by grid search or PSO, for the sake of fairness. The experiment results show that the proposed PSO-RF model outperforms all the other models, with the accuracy of 97.47%, the area under the ROC curve (AUC) of 0.996, the sensitivity of 94.12% and the specificity of 98.39%. It can be concluded that the PSO-RF model is a highly effective AI-enabled tool for preoperatively diagnosing spontaneous rupture of OEs.

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