Background: Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. Objective: The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. Methods: A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. Results: We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. Conclusions: Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability.
背景：黑色素瘤作为皮肤癌中最致命的类型，在全球范围内构成重大公共卫生挑战。早期检测对于改善患者预后至关重要。非侵入性皮肤成像技术有助于提高诊断准确性，但由于需要经过标准化图像判读培训的专业人员，其应用常受到限制。近年来，基于人工智能（AI）的皮肤病变图像分析技术不断创新，显示出AI在黑色素瘤早期检测中的应用潜力。目的：本研究旨在评估AI技术与反射共聚焦显微镜（RCM）、光学相干断层扫描（OCT）及皮肤镜等非侵入性诊断成像模式结合应用的现状，并探讨AI技术能否提高黑色素瘤的诊断准确性。方法：通过Medline/PubMed、Cochrane和Embase数据库对2018年至2022年间符合要求的文献进行系统检索，筛选过程遵循2020版PRISMA（系统评价与荟萃分析优先报告条目）指南。纳入研究均采用基于AI的算法进行黑色素瘤检测，并直接回应本综述的研究目标。结果：从三个数据库共检索到40篇文献。所有直接比较AI技术与皮肤科医生诊断性能的研究均报告，AI技术在改善黑色素瘤检测方面表现更优或相当。在直接比较皮肤镜图像算法性能与皮肤科医生诊断的研究中，基于AI的算法在黑色素瘤检测中获得了更高的ROC曲线下面积（>80%）。在这些使用皮肤镜图像的比较研究中，算法平均敏感度为83.01%，平均特异度为85.58%。评估机器学习结合OCT技术的研究准确率达95%，而评估RCM技术的研究报告平均准确率为82.72%。结论：研究结果表明，基于AI的技术通过早期识别黑色素瘤，在提高诊断准确性和改善患者预后方面具有巨大潜力。未来需要进一步研究评估这些AI技术在不同人群和皮肤类型中的普适性，改进图像处理的标准化流程，并与认证皮肤科医生的诊断性能进行深入比较，以评估其临床适用性。
肿瘤(癌症)患者之家