Table_2_Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review.docx

IntroductionPolycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS.
MethodsWe applied a search strategy using the following databases MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Web of Science, and the IEEE Xplore Digital Library using relevant keywords. Eligible studies were identified, and results were extracted for their synthesis from inception until January 1, 2022.
Results135 studies were screened and ultimately, 31 studies were included in this study. Data sources used by the AI/ML interventions included clinical data, electronic health records, and genetic and proteomic data. Ten studies (32%) employed standardized criteria (NIH, Rotterdam, or Revised International PCOS classification), while 17 (55%) used clinical information with/without imaging. The most common AI techniques employed were support vector machine (42% studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML. Receiver operating curves (ROC) were employed to compare AI/ML with clinical diagnosis. Area under the ROC ranged from 73% to 100% (n=7 studies), diagnostic accuracy from 89% to 100% (n=4 studies), sensitivity from 41% to 100% (n=10 studies), specificity from 75% to 100% (n=10 studies), positive predictive value (PPV) from 68% to 95% (n=4 studies), and negative predictive value (NPV) from 94% to 99% (n=2 studies).
ConclusionArtificial intelligence and machine learning provide a high diagnostic and classification performance in detecting PCOS, thereby providing an avenue for early diagnosis of this disorder. However, AI-based studies should use standardized PCOS diagnostic criteria to enhance the clinical applicability of AI/ML in PCOS and improve adherence to methodological and reporting guidelines for maximum diagnostic utility.
Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022295287.

引言：多囊卵巢综合征（Polycystic Ovarian Syndrome, PCOS）是育龄女性最常见的内分泌疾病，且普遍存在诊断不足的问题，进而导致显著的患病负担。人工智能（Artificial Intelligence, AI）与机器学习（Machine Learning, ML）在改善诊断方面展现出应用前景。因此，本研究开展了一项系统综述（systematic review），以明确AI/ML在PCOS诊断或分类中的应用价值。

方法：本研究构建检索策略，在MEDLINE、Embase、Cochrane对照试验中心注册库、Web of Science以及IEEE Xplore数字图书馆中，以相关关键词进行文献检索，检索时限为建库至2022年1月1日。随后筛选符合纳入标准的研究并提取相关结果，开展研究结果的综合分析。

结果：本研究共筛选135项研究，最终纳入31项。AI/ML模型所使用的数据来源包括临床数据、电子健康记录（electronic health records）以及基因组与蛋白质组数据。其中10项研究（32%）采用了标准化诊断标准（美国国立卫生研究院NIH标准、鹿特丹标准或修订版国际PCOS分类标准），17项研究（55%）使用了伴或不伴影像学检查的临床信息。最常用的AI技术包括支持向量机（support vector machine，占比42%的研究）、K近邻算法（K-nearest neighbor，占比26%）以及回归模型（占比23%），为最常见的AI/ML方法。研究采用受试者工作特征曲线（Receiver Operating Curves, ROC）对比AI/ML模型与临床诊断的效能：ROC曲线下面积介于73%~100%（共7项研究），诊断准确率介于89%~100%（共4项研究），灵敏度介于41%~100%（共10项研究），特异度介于75%~100%（共10项研究），阳性预测值（Positive Predictive Value, PPV）介于68%~95%（共4项研究），阴性预测值（Negative Predictive Value, NPV）介于94%~99%（共2项研究）。

结论：人工智能与机器学习在PCOS检测中展现出较高的诊断与分类性能，为该疾病的早期诊断提供了可行路径。然而，基于AI的相关研究应采用标准化的PCOS诊断标准，以提升AI/ML技术在PCOS诊断中的临床适用性，并严格遵循方法学与报告规范，最大化其诊断应用价值。

系统综述注册信息：系统综述注册平台为https://www.crd.york.ac.uk/prospero/，注册号为CRD42022295287。