Background: The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in non-invasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias. Methods: Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamous-cell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted. Results: A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI). The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87%. Accuracy was not measured in regard to AI based HEI interpretation. Conclusion: AI algorithms exhibited an overall favorable performance in the diagnosis of nonmelanoma skin cancer via noninvasive imaging techniques. Ultimately, further research is needed to isolate pooled diagnostic accuracy for nonmelanoma skin cancers as many testing datasets also include melanoma and other pigmented lesions.
背景：本研究旨在系统分析当前关于利用新型人工智能机器学习模型进行非侵入性成像以早期检测非黑色素瘤皮肤癌的文献现状。此外，我们通过评估各算法的准确性、敏感性和特异性，并评估偏倚风险，旨在评估其潜在的临床相关性。 方法：由两名评审员系统检索MEDLINE、Cochrane、PubMed和Embase数据库中2018年至2023年间发表的、专注于基于人工智能的非黑色素瘤皮肤癌分类的同行评议研究。检索词包括皮肤肿瘤、非黑色素瘤、基底细胞癌、鳞状细胞癌、诊断技术与程序、人工智能、算法、计算机系统、皮肤镜、反射共聚焦显微镜和光学相干断层扫描。根据检索结果，仅纳入直接回应综述目标的研究，并记录各项研究的效能指标。随后对纳入研究进行了QUADAS-2偏倚风险评估。 结果：共44项研究纳入综述，其中40项使用皮肤镜成像，3项使用反射共聚焦显微镜，1项采用高光谱表皮成像。所有成像模式中人工智能算法的平均准确率为86.80%，皮肤镜成像的平均准确率与之相同。三项应用人工智能于反射共聚焦显微镜的研究中仅一项测量了准确率，结果为87%。基于人工智能的高光谱表皮成像解读未进行准确率测量。 结论：人工智能算法通过非侵入性成像技术诊断非黑色素瘤皮肤癌总体表现良好。最终仍需进一步研究以明确非黑色素瘤皮肤癌的汇总诊断准确率，因为现有测试数据集中常包含黑色素瘤及其他色素性皮损。
肿瘤(癌症)患者之家