Table 1_Artificial intelligence for ovarian cancer diagnosis via ultrasound: a systematic review and quantitative assessment of model performance.docx

BackgroundEarly and accurate detection of ovarian cancer (OC) remains clinically challenging, prompting exploration of artificial intelligence (AI)-based ultrasound diagnostics. This systematic review and meta-analysis critically evaluate diagnostic accuracy, methodological rigor, and clinical applicability of AI models for ovarian mass classification using B-mode ultrasound.
MethodsA systematic literature search following PRISMA guidelines was conducted in PubMed, IEEE Xplore, and Scopus up to December 2024. Eligible studies included AI-based ovarian mass classification using B-mode ultrasound, reporting accuracy, sensitivity, specificity, and/or area under the ROC curve (AUC). Data extraction, quality assessment (PROBAST), and meta-analysis (random effects) were independently performed by two reviewers. Heterogeneity sources were explored.
ResultsFrom 823 identified records, 44 studies met inclusion criteria, covering over 650,000 images. Pooled performance metrics indicated high accuracy (92.3%), sensitivity (91.6%), specificity (90.1%), and AUC (0.93). Automated segmentation significantly outperformed manual segmentation in accuracy and sensitivity, demonstrating standardization benefits and reduced observer variability. Dataset size minimally correlated with performance, highlighting methodological rigor as a primary determinant. No specific AI architecture consistently outperformed others. Substantial methodological heterogeneity and frequent risk-of-bias issues (limited validation, small datasets) currently limit clinical translation.
ConclusionAI models show promising diagnostic performance for OC ultrasound imaging. However, addressing methodological challenges, including rigorous validation, standardized reporting (TRIPOD-AI, STARD-AI), and prospective multicenter studies, is essential for clinical integration. This review provides clear recommendations to enhance clinical translation of AI-based ultrasound diagnostics.

背景 卵巢癌（ovarian cancer, OC）的早期精准检测仍是临床难题，这推动了基于人工智能（artificial intelligence, AI）的超声诊断技术的探索。本系统综述与荟萃分析（systematic review and meta-analysis）对采用B型超声（B-mode ultrasound）进行卵巢肿块分类的AI模型的诊断准确性、方法学严谨性及临床适用性进行了批判性评价。
方法 本研究遵循PRISMA声明（PRISMA guidelines），于2024年12月前在PubMed、IEEE Xplore及Scopus数据库中开展了系统性文献检索。纳入研究需满足：采用B型超声开展基于AI的卵巢肿块分类，且报告了准确率、灵敏度、特异度及/或受试者工作特征曲线下面积（area under the ROC curve, AUC）。由两名研究者独立完成数据提取、偏倚风险评估（PROBAST）及荟萃分析（随机效应模型），并探索了异质性来源。
结果 从检索到的823条文献记录中，最终有44项研究符合纳入标准，共涵盖超过65万幅图像。合并后的性能指标显示，该类AI模型具有较高的准确率（92.3%）、灵敏度（91.6%）、特异度（90.1%）及受试者工作特征曲线下面积（0.93）。自动分割（automated segmentation）在准确率与灵敏度方面显著优于手动分割（manual segmentation），体现了标准化的优势并降低了观察者间变异。数据集规模与模型性能仅存在微弱相关性，提示方法学严谨性是影响模型性能的核心因素。目前尚无特定的AI架构（AI architecture）能持续优于其他架构。显著的方法学异质性及频发的偏倚风险问题（如验证范围有限、数据集规模较小）制约了其临床转化。
结论 基于超声成像的AI模型在卵巢癌诊断中展现出良好的应用前景。然而，要实现临床整合，仍需解决诸多方法学挑战，包括开展严格的验证、遵循标准化报告规范（TRIPOD-AI、STARD-AI）以及开展前瞻性多中心研究。本综述为提升基于AI的超声诊断技术的临床转化提供了明确的建议。