Garrison Zachary R, Hall Connor M, Fey Rosalyn M, Clister Terri, Khan Nabeela, Nichols Rebecca, Kulkarni Rajan P
Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA.
Cancer Early Detection Advanced Research Center (CEDAR), Portland, OR 97239, USA.
Life (Basel). 2023 Apr 9;13(4):974. doi: 10.3390/life13040974.
The past decade has seen numerous advancements in approaches to melanoma detection, each with the common goal to stem the growing incidence of melanoma and its mortality rate. These advancements, while well documented to increase early melanoma detection, have also garnered considerable criticism of their efficacy for improving survival rates. In this review, we discuss the current state of such early detection approaches that do not require direct dermatologist intervention. Our findings suggest that a number of at-home and non-specialist methods exist with high accuracy for detecting melanoma, albeit with a few notable concerns worth further investigation. Additionally, research continues to find new approaches using artificial intelligence which have promise for the future.
在过去十年中,黑色素瘤检测方法取得了诸多进展,每一项进展都有一个共同目标,即遏制黑色素瘤发病率及其死亡率的不断上升。这些进展虽然有充分记录表明可提高黑色素瘤的早期检测率,但也因其提高生存率的功效而受到了相当多的批评。在本综述中,我们讨论了此类无需皮肤科医生直接干预的早期检测方法的现状。我们的研究结果表明,存在一些在家中使用的非专业方法,它们检测黑色素瘤的准确率很高,尽管有一些值得进一步研究的显著问题。此外,研究人员继续探索使用人工智能的新方法,这些方法在未来很有前景。