Detection of high-grade squamous intraepithelial lesions(HSILs)is key for the prevention of human papillomavirus(HPV)-related cancers.In this study,we aimed to identify and consolidate the existing evidence on the dia...Detection of high-grade squamous intraepithelial lesions(HSILs)is key for the prevention of human papillomavirus(HPV)-related cancers.In this study,we aimed to identify and consolidate the existing evidence on the diagnostic performances of artificial intelligence(AI)-assisted tools for HPV-related HSILs.We followed the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy and the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy guidelines and systematically searched PubMed,Embase,Web of Science,IEEE,and the Cochrane Library for eligible studies published between 2000 and January 2024.Studies on AI-assisted colposcopy used to facilitate the diagnosis of cervical or anal HSILs and cancers were included.The revised Quality Assessment of Diagnostic Studies 2 and AI checklists were used to assess the risk of bias.With random-effect models,meta-analyses were performed on performance indicators,including accuracy,sensitivity,specificity,and area under the receiver-operating characteristic curves(AUC).Subgroup and meta-regression analyses were performed to identify the sources of heterogeneity.Twenty-five studies,comprising 21 studies focused on cervical cancer and 4 on anal cancer,were included in the meta-analysis.When differentiating from individuals with<cervical intraepithelial neoplasia grade 2(CIN2)/low-grade squamous intraepithelial lesion(LSIL)−(lesion grade<CIN2 or≤LSIL),the AI-assisted diagnostic tools identified individuals with CIN2+/HSIL+(lesion grade≥CIN2 or≥HSIL)at a pooled accuracy of 0.84(95%confidence interval[CI]:0.81-0.88),sensitivity of 0.87(0.81-0.93),specificity of 0.85(0.81-0.89),and AUC of 0.94(0.91-0.96).For anal cancer,the AI-assisted diagnostic tools differentiated individuals with anal intraepithelial neoplasia grade 2(AIN2)+/HSIL+(≥AIN2 or≥HSIL)from those with<AIN2/LSIL−(lesion grade<AIN2 or≤LSIL)at a pooled accuracy of 0.90(95%CI:0.88-0.93),sensitivity of 0.94(0.87-0.99),specificity of 0.88(0.71-0.98),and AUC of 0.97(0.95-0.98).All meta-analysis results demonstrated high heterogeneity(I^(2)>90%),but the meta-regression analysis did not reveal any significant contributing sources of heterogeneity.Based on this systematic review and meta-analysis of existing literature,we concluded that AI-assisted diagnostic tools have demonstrated promising predictive performance in diagnosing HPV-related cervical and anal precancerous lesions and cancers.展开更多
文摘Detection of high-grade squamous intraepithelial lesions(HSILs)is key for the prevention of human papillomavirus(HPV)-related cancers.In this study,we aimed to identify and consolidate the existing evidence on the diagnostic performances of artificial intelligence(AI)-assisted tools for HPV-related HSILs.We followed the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy and the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy guidelines and systematically searched PubMed,Embase,Web of Science,IEEE,and the Cochrane Library for eligible studies published between 2000 and January 2024.Studies on AI-assisted colposcopy used to facilitate the diagnosis of cervical or anal HSILs and cancers were included.The revised Quality Assessment of Diagnostic Studies 2 and AI checklists were used to assess the risk of bias.With random-effect models,meta-analyses were performed on performance indicators,including accuracy,sensitivity,specificity,and area under the receiver-operating characteristic curves(AUC).Subgroup and meta-regression analyses were performed to identify the sources of heterogeneity.Twenty-five studies,comprising 21 studies focused on cervical cancer and 4 on anal cancer,were included in the meta-analysis.When differentiating from individuals with<cervical intraepithelial neoplasia grade 2(CIN2)/low-grade squamous intraepithelial lesion(LSIL)−(lesion grade<CIN2 or≤LSIL),the AI-assisted diagnostic tools identified individuals with CIN2+/HSIL+(lesion grade≥CIN2 or≥HSIL)at a pooled accuracy of 0.84(95%confidence interval[CI]:0.81-0.88),sensitivity of 0.87(0.81-0.93),specificity of 0.85(0.81-0.89),and AUC of 0.94(0.91-0.96).For anal cancer,the AI-assisted diagnostic tools differentiated individuals with anal intraepithelial neoplasia grade 2(AIN2)+/HSIL+(≥AIN2 or≥HSIL)from those with<AIN2/LSIL−(lesion grade<AIN2 or≤LSIL)at a pooled accuracy of 0.90(95%CI:0.88-0.93),sensitivity of 0.94(0.87-0.99),specificity of 0.88(0.71-0.98),and AUC of 0.97(0.95-0.98).All meta-analysis results demonstrated high heterogeneity(I^(2)>90%),but the meta-regression analysis did not reveal any significant contributing sources of heterogeneity.Based on this systematic review and meta-analysis of existing literature,we concluded that AI-assisted diagnostic tools have demonstrated promising predictive performance in diagnosing HPV-related cervical and anal precancerous lesions and cancers.
