Patel Raj H, Foltz Emilie A, Witkowski Alexander, Ludzik Joanna
Edward Via College of Osteopathic Medicine, VCOM-Louisiana, 4408 Bon Aire Dr, Monroe, LA 71203, USA.
Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA.
Cancers (Basel). 2023 Sep 23;15(19):4694. doi: 10.3390/cancers15194694.
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.
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.
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.
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%.
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.
黑色素瘤是最致命的皮肤癌形式，在全球范围内构成重大的公共卫生挑战。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可提高诊断准确性；然而，由于需要训练有素的专业人员以标准化方式解读图像，其应用往往受到限制。基于人工智能（AI）的皮肤病变图像解读技术的最新创新显示了人工智能在黑色素瘤早期检测中的应用潜力。
本研究的目的是评估与包括反射式共聚焦显微镜（RCM）、光学相干断层扫描（OCT）和皮肤镜检查在内的非侵入性诊断成像模态联合使用的基于人工智能的技术的现状。我们还旨在确定基于人工智能的技术的应用是否能提高黑色素瘤的诊断准确性。
通过Medline/PubMed、Cochrane和Embase数据库对2018年至2022年期间的合格出版物进行系统检索。筛选方法遵循2020版PRISMA（系统评价和Meta分析的首选报告项目）指南。纳入的研究利用基于人工智能的算法进行黑色素瘤检测，并直接涉及审查目标。
我们在三个数据库中检索到40篇论文。所有直接将基于人工智能的技术与皮肤科医生的表现进行比较的研究都报告了基于人工智能的技术在改善黑色素瘤检测方面的卓越或等效表现。在直接将皮肤镜图像上的算法表现与皮肤科医生进行比较的研究中，基于人工智能的数据算法在黑色素瘤检测中达到了更高的受试者工作特征曲线（ROC）（>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%，算法平均特异度为85.58%。评估机器学习与OCT联合使用的研究的准确率为95%，而评估RCM的研究报告的平均准确率为82.72%。
我们的结果证明了基于人工智能的技术通过早期识别黑色素瘤来提高诊断准确性和患者预后的强大潜力。需要进一步研究来评估这些基于人工智能的技术在不同人群和皮肤类型中的可推广性，改善图像处理的标准化，并进一步将基于人工智能的技术的表现与获得委员会认证的皮肤科医生进行比较，以评估临床适用性。
