BDS, Bharati Vidyapeeth (Deemed to be University), Dental College and Hospital, Pune, Maharashtra, India.
Assistant Professor, Department of Public Health Dentistry Bharati Vidyapeeth (Deemed to be University), Dental College and Hospital, Pune, Maharashtra, India.
Asian Pac J Cancer Prev. 2024 Aug 1;25(8):2593-2603. doi: 10.31557/APJCP.2024.25.8.2593.
To summarize and compare the existing evidence on diagnostic accuracy of artificial intelligence (AI) models in detecting early oral squamous cell carcinoma (OSCC).
Review was performed in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis - Diagnostic Test Accuracy (PRISMA- DTA) checklist and the review protocol is registered under PROSPERO(CRD42023456355). PubMed, Google Scholar, EBSCOhost were searched from January 2000 to November 2023 to identify the diagnostic potential of AI based tools and models. True-positive, false-positive, true-negative, false-negative, sensitivity, specificity values were extracted or calculated if not present for each study. Quality of selected studies was evaluated based on QUADAS (Quality assessment of diagnostic accuracy studies)- 2 tool. Meta-analysis was performed in Meta-Disc 1.4 software and Review Manager 5.3 RevMan using a bivariate model parameter for the sensitivity and specificity and summary points, summary receiver operating curve (SROC), diagnostic odds ratio (DOR) confidence region, and area under curve (AUC) were calculated.
Fourteen studies were included for qualitative synthesis and for meta-analysis. Included studies had presence of low to moderate risk of bias. Pooled sensitivity and specificity of 0.43 (CI 0.18- 0.71) and 0.50 (CI 0.20- 0.80) was observed with a pooled positive likelihood ratio of (PLR) 0.86 (0.43 - 1.71) and negative likelihood ratio (NLR) of 1.04 (0.42 - 1.68) was observed with DOR of 0.78 (0.12 - 5.18) and overall accuracy (AUC) being 0.45 respectively.
AI based tools has poor to moderate overall diagnostic accuracy. However, to validate our study findings further more standardized diagnostic accuracy studies should be conducted with proper reporting through QUADAS-2 tool. Thus, we can conclude AI based based tool for secondary level of prevention for early OSCC under early diagnosis and prompt treatment.
总结和比较人工智能(AI)模型在检测早期口腔鳞状细胞癌(OSCC)方面的诊断准确性的现有证据。
本研究按照系统评价和荟萃分析-诊断测试准确性(PRISMA-DTA)清单进行综述,并根据 PROSPERO(CRD42023456355)注册了综述方案。从 2000 年 1 月至 2023 年 11 月,在 PubMed、Google Scholar、EBSCOhost 上检索了 AI 为基础的工具和模型的诊断潜力。如果未为每项研究提供,则提取或计算真阳性、假阳性、真阴性、假阴性、敏感性、特异性值。根据 QUADAS(诊断准确性研究的质量评估)-2 工具评估选定研究的质量。使用 Meta-Disc 1.4 软件和 Review Manager 5.3 RevMan 进行荟萃分析,采用双变量模型参数计算敏感性和特异性以及汇总点、汇总接收者操作特征曲线(SROC)、诊断比值比(DOR)置信区间和曲线下面积(AUC)。
有 14 项研究纳入定性综合分析和荟萃分析。纳入的研究存在低到中度偏倚风险。观察到合并敏感性和特异性分别为 0.43(95%CI 0.18-0.71)和 0.50(95%CI 0.20-0.80),合并阳性似然比(PLR)为 0.86(0.43-1.71),阴性似然比(NLR)为 1.04(0.42-1.68),诊断比值比(DOR)为 0.78(0.12-5.18),总准确性(AUC)分别为 0.45。
AI 为基础的工具的整体诊断准确性较差。然而,为了进一步验证我们的研究结果,应该通过 QUADAS-2 工具进行更标准化的诊断准确性研究,并进行适当的报告。因此,我们可以得出结论,AI 为基础的工具可用于二级预防早期 OSCC,以实现早期诊断和及时治疗。