人工智能在卵巢子宫内膜异位症影像中的潜力：经阴道超声AI模型与人工阅片的比较Meta分析
该系统综述与Meta分析纳入7项研究、2737例患者（6061幅图像），比较经阴道超声AI模型与人工阅片对卵巢子宫内膜异位症的诊断准确性。AI模型合并敏感度91%、特异度95%，人工阅片分别为80%、85%；AI在特异度（p=0.001）和AUC（0.97 vs 0.84，p<0.001）上显著更优，敏感度差异无统计学意义（p=0.10）。结论认为AI表现初步但有前景，可能作为超声评估的辅助工具。
要点
- AI模型诊断卵巢子宫内膜异位症的合并敏感度91%、特异度95%，AUC 0.97。
- 人工阅片合并敏感度80%、特异度85%，AUC 0.84，AI在特异度与整体判别上更优。
- AI有望作为经阴道超声的辅助工具，减少假阳性并促进判读标准化。
核心结论
- AI模型在诊断卵巢子宫内膜异位症的特异度和整体判别能力上显著优于人工阅片，敏感度与人工阅片相当。
- AI算法显示出有前景但初步的诊断性能，可能作为超声评估的辅助工具。
研究设计与样本
- 纳入7项报告AI模型与人工阅片诊断指标比较的研究，共2737例患者、6061幅图像。
- 采用双变量随机效应模型计算合并敏感度、特异度和AUC。
诊断标准与关键数值
- AI模型诊断卵巢子宫内膜异位症合并敏感度91%（CI 81%-96%），特异度95%（CI 92%-96%）。
- 人工阅片合并敏感度80%（95%CI 65-90），特异度85%（95%CI 74-92）。
- AI模型AUC 0.97（95%CI 0.95-0.98），人工阅片AUC 0.84（95%CI 0.82-0.88），差异p<0.001。
- AI与人工阅片特异度差异p=0.001，敏感度差异p=0.10。
- 纳入研究间异质性为0%。
临床应用与阅片要点
- AI可作为经阴道超声诊断卵巢子宫内膜异位症的辅助手段，帮助提高特异度、减少假阳性。
- AI可能促进超声判读的标准化，但当前证据尚属初步，不能替代人工阅片。
局限性
- 结论描述为初步（preliminary），提示证据成熟度有限。
- 仅纳入7项研究，样本量及研究数量有限。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
英文原文（摘要）· 点击展开
PURPOSE: Transvaginal ultrasound (TVUS) is widely used for diagnosing ovarian endometriosis but remains limited by significant operator dependency. This systematic review and meta-analysis evaluated the diagnostic accuracy of ultrasound-based artificial intelligence (AI) models for ovarian endometriosis and directly compared their performance with that of human readers.
METHODS: We conducted a comprehensive search of five databases (PubMed, Embase, Scopus, Web of Science, and Cochrane Library) up to 5 December 2025 to identify studies reporting diagnostic metrics for AI models, compared with human readers, for detecting ovarian endometriomas. Pooled sensitivity, specificity, and area under the curve (AUC) were calculated using a bivariate random-effects model.
RESULTS: Seven studies with 2737 patients (6061 images) were included. AI models demonstrated a pooled sensitivity of 91% (confidence interval 81%-96%) and specificity of 95% (confidence interval 92%-96%). Human readers achieved a pooled sensitivity of 80% (95% confidence interval 65-90) and specificity of 85% (95% confidence interval 74-92). AI models significantly outperformed readers in specificity (p = 0.001) and overall diagnostic discrimination, with an AUC of 0.97 (95% confidence interval 0.95-0.98) compared with 0.84 (95% confidence interval 0.82-0.88) for human readers (p < 0.001), while sensitivity remained comparable (p = 0.10). Heterogeneity across included studies was minimal (0%).
CONCLUSION: AI algorithms show indications of promising but preliminary diagnostic performance relative to human readers in specificity and overall discrimination metrics. These results raise the possibility that AI might serve as a supplementary tool in sonographic evaluation of ovarian endometriosis, potentially contributing to more standardized interpretation and reduced false-positive diagnoses.
引用
- Mohebbi A, Zare M, Mohammadi A, et al. Artificial intelligence potential in ovarian endometriosis imaging: a comparative meta-analysis of transvaginal ultrasound-based AI models and human readers. Abdom Radiol (NY). 2026. PMID: 42189210. doi:10.1007/s00261-026-05570-6
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。