Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2019 Jan;139(1):25-30.
doi: 10.1016/j.jid.2018.06.187. Epub 2018 Oct 25.

Melanoma Early Detection: Big Data, Bigger Picture

Affiliations
Review

Melanoma Early Detection: Big Data, Bigger Picture

Tracy Petrie et al. J Invest Dermatol. 2019 Jan.

Abstract

Innovative technologies, including novel communication and imaging tools, are affecting dermatology in profound ways. A burning question for the field is whether we will retrospectively react to innovations or proactively leverage them to benefit precision medicine. Early detection of melanoma is a dermatologic area particularly poised to benefit from such innovation. This session of the Montagna Symposium on Biology of Skin focused on provocative, potentially disruptive advances, including crowdsourcing of patient advocacy efforts, rigorous experimental design of public education campaigns, research with mobile phone applications, advanced skin imaging technologies, and the emergence of artificial intelligence as a diagnostic supplement.

Keywords: RCM; War on Melanoma; WoM; reflectance confocal microscopy.

PubMed Disclaimer

Conflict of interest statement

CONFLICT OF INTEREST

The authors state no conflict of interest.

Figures

Figure 1
Figure 1
War on Melanoma™ Full Spectrum Operations Matrix. This shows different outreach and screening examples by risk class and different empowerment methods by provider type. Different provider types see different proportions of the various risk populations as represented with the heat map matrix. Provider and risk population classes are not the same size as represented by the shaded triangles.

Similar articles

Cited by

References

    1. Alcon JF, Ciuhu C, Kate W ten, Heinrich A, Uzunbajakava N, Krekels G, et al. Automatic Imaging System With Decision Support for Inspection of Pigmented Skin Lesions and Melanoma Diagnosis. IEEE J. Sel. Top. Signal Process 2009;3(1):14–25
    1. Anders MP, Fengler S, Volkmer B, Greinert R, Breitbart EW. Nationwide skin cancer screening in Germany: Evaluation of the training program. Int. J. Dermatol 2017;56(10):1046–51 - PubMed
    1. Argenziano G, Cerroni L, Zalaudek I, Staibano S, Hofmann-Wellenhof R, Arpaia N, et al. Accuracy in melanoma detection: A 10-year multicenter survey. J. Am. Acad. Dermatol 2012;67(1):54–59.e1 - PubMed
    1. Argenziano G, Moscarella E, Annetta A, Battarra VC, Brunetti B, Buligan C, et al. Melanoma detection in Italian pigmented lesion clinics. G. Ital. Dermatol. Venereol 2014;149(2):161–7 - PubMed
    1. Blum A, Luedtke H, Ellwanger U, Schwabe R, Rassner G, Garbe C. Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions. Br. J. Dermatol 2004;151(5):1029–38 - PubMed

Publication types