Early diagnosis of abnormal cervical cells enhances the chance of prompt treatment for cervical cancer (CrC). Artificial intelligence (AI)‐assisted decision support systems for detecting abnormal cervical cells are developed because manual identification needs trained healthcare professionals, and can be difficult, time‐consuming, and error‐prone. The purpose of this study is to present a comprehensive review of AI technologies used for detecting cervical pre‐cancerous lesions and cancer. The review study includes studies where AI was applied to Pap Smear test (cytological test), colposcopy, sociodemographic data and other risk factors, histopathological analyses, magnetic resonance imaging‐, computed tomography‐, and positron emission tomography‐scan‐based imaging modalities. We performed searches on Web of Science, Medline, Scopus, and Inspec. The preferred reporting items for systematic reviews and meta‐analysis guidelines were used to search, screen, and analyze the articles. The primary search resulted in identifying 9745 articles. We followed strict inclusion and exclusion criteria, which include search windows of the last decade, journal articles, and machine/deep learning‐based methods. A total of 58 studies have been included in the review for further analysis after identification, screening, and eligibility evaluation. Our review analysis shows that deep learning models are preferred for imaging techniques, whereas machine learning‐based models are preferred for sociodemographic data. The analysis shows that convolutional neural network‐based features yielded representative characteristics for detecting pre‐cancerous lesions and CrC. The review analysis also highlights the need for generating new and easily accessible diverse datasets to develop versatile models for CrC detection. Our review study shows the need for model explainability and uncertainty quantification to increase the trust of clinicians and stakeholders in the decision‐making of automated CrC detection models. Our review suggests that data privacy concerns and adaptability are crucial for deployment hence, federated learning and meta‐learning should also be explored.This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning Technologies > Classification

中文翻译：

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人工智能用于宫颈癌自动化检测的系统评价及研究建议


异常宫颈细胞的早期诊断可以提高宫颈癌 (CrC) 及时治疗的机会。用于检测异常宫颈细胞的人工智能 (AI) 辅助决策支持系统的开发是因为手动识别需要训练有素的医疗保健专业人员，并且可能很困难、耗时且容易出错。本研究的目的是对用于检测宫颈癌前病变和癌症的人工智能技术进行全面综述。该综述研究包括人工智能应用于子宫颈抹片检查（细胞学检查）、阴道镜检查、社会人口统计学数据和其他危险因素、组织病理学分析、磁共振成像、计算机断层扫描和基于正电子发射断层扫描的成像方式的研究。我们在 Web of Science、Medline、Scopus 和 Inspec 上进行了检索。系统评价和荟萃分析指南的首选报告项目用于搜索、筛选和分析文章。初步搜索结果确定了 9745 篇文章。我们遵循严格的纳入和排除标准，其中包括过去十年的搜索窗口、期刊文章和基于机器/深度学习的方法。经过鉴定、筛选和资格评估后，共有 58 项研究纳入审查以供进一步分析。我们的综述分析表明，深度学习模型更适合成像技术，而基于机器学习的模型更适合社会人口统计数据。分析表明，基于卷积神经网络的特征产生了用于检测癌前病变和 CrC 的代表性特征。 审查分析还强调需要生成新的且易于访问的多样化数据集，以开发用于 CrC 检测的多功能模型。我们的综述研究表明，需要模型可解释性和不确定性量化，以增加临床医生和利益相关者对自动化 CrC 检测模型决策的信任。我们的审查表明，数据隐私问题和适应性对于部署至关重要，因此，还应该探索联邦学习和元学习。本文分类为：数据和知识的基本概念 > 可解释的人工智能技术 > 机器学习技术 > 分类