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BACKGROUND

Cervical cytology screening and colposcopy play crucial roles in cervical intraepithelial neoplasia (CIN) and cervical cancer prevention. Previous studies have provided evidence that artificial intelligence (AI) has remarkable diagnostic accuracy in these procedures. With this systematic review and meta-analysis, we aimed to examine the pooled accuracy, sensitivity, and specificity of AI-assisted cervical cytology screening and colposcopy for cervical intraepithelial neoplasia and cervical cancer screening.

METHODS

In this systematic review and meta-analysis, we searched the PubMed, Embase, and Cochrane Library databases for studies published between January 1, 1986 and August 31, 2024. Studies investigating the sensitivity and specificity of AI-assisted cervical cytology screening and colposcopy for histologically verified cervical intraepithelial neoplasia and cervical cancer and a minimum of five cases were included. The performance of AI and experienced colposcopists was assessed via the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) through random effect models. Additionally, subgroup analyses of multiple diagnostic performance metrics in developed and developing countries were conducted. This study was registered with PROSPERO (CRD42024534049).

FINDINGS

Seventy-seven studies met the eligibility criteria for inclusion in this study. The pooled diagnostic parameters of AI-assisted cervical cytology via Papanicolaou (Pap) smears were as follows: accuracy, 94% (95% CI 92-96); sensitivity, 95% (95% CI 91-98); specificity, 94% (95% CI 89-97); PPV, 88% (95% CI 78-96); and NPV, 95% (95% CI 89-99). The pooled accuracy, sensitivity, specificity, PPV, and NPV of AI-assisted cervical cytology via ThinPrep cytologic test (TCT) were 90% (95% CI 85-94), 97% (95% CI 95-99), 94% (95% CI 85-98), 84% (95% CI 64-98), and 96% (95% CI 94-98), respectively. Subgroup analysis revealed that, for AI-assisted cervical cytology diagnosis, certain performance indicators were superior in developed countries compared to developing countries. Compared with experienced colposcopists, AI demonstrated superior accuracy in colposcopic examinations (odds ratio (OR) 1.75; 95% CI 1.33-2.31; P < 0.0001; I = 93%).

INTERPRETATION

These results underscore the potential and practical value of AI in preventing and enabling early diagnosis of cervical cancer. Further research should support the development of AI for cervical cancer screening, including in low- and middle-income countries with limited resources.

FUNDING

This study was supported by the National Natural Science Foundation of China (No. 81901493) and the Shanghai Pujiang Program (No. 21PJD006).

BACKGROUND

METHODS

FINDINGS

INTERPRETATION

FUNDING

This study was supported by the National Natural Science Foundation of China (No. 81901493) and the Shanghai Pujiang Program (No. 21PJD006).

背景

宫颈细胞学筛查和阴道镜检查在宫颈上皮内瘤变（CIN）和宫颈癌的预防中起着至关重要的作用。以往研究已证明人工智能（AI）在这些检查中具有显著的诊断准确性。通过本系统评价和荟萃分析，我们旨在研究AI辅助宫颈细胞学筛查和阴道镜检查对宫颈上皮内瘤变和宫颈癌筛查的综合准确性、敏感性和特异性。

方法

在本系统评价和荟萃分析中，我们检索了PubMed、Embase和Cochrane图书馆数据库，查找1986年1月1日至2024年8月31日期间发表的研究。纳入调查AI辅助宫颈细胞学筛查和阴道镜检查对经组织学证实的宫颈上皮内瘤变和宫颈癌的敏感性和特异性且病例数至少为5例的研究。通过随机效应模型，利用受试者工作特征曲线下面积（AUROC）、敏感性、特异性、准确性、阳性预测值（PPV）和阴性预测值（NPV）评估AI和经验丰富的阴道镜检查医师的表现。此外，还对发达国家和发展中国家的多项诊断性能指标进行了亚组分析。本研究已在PROSPERO注册（CRD42024534049）。

结果

77项研究符合纳入本研究的资格标准。AI辅助巴氏涂片宫颈细胞学检查的综合诊断参数如下：准确性94%（95%CI 92 - 96）；敏感性95%（95%CI 91 - 98）；特异性94%（95%CI 89 - 97）；PPV 88%（95%CI 78 - 96）；NPV 95%（95%CI 89 - 99）。AI辅助液基薄层细胞学检测（TCT）宫颈细胞学检查的综合准确性、敏感性、特异性、PPV和NPV分别为90%（95%CI 85 - 94）、97%（95%CI 95 - 99）、94%（95%CI 85 - 98）、84%（95%CI 64 - 98）和96%（95%CI 94 - 98）。亚组分析显示，对于AI辅助宫颈细胞学诊断，发达国家的某些性能指标优于发展中国家。与经验丰富的阴道镜检查医师相比，AI在阴道镜检查中显示出更高的准确性（优势比（OR）1.75；95%CI 1.33 - 2.31；P < 0.0001；I² = 93%）。