High technology solutions to the difficult task of selecting and monitoring moles (pigmented skin naevi) may be useful to keep accurate records of people’s skin. Adopting military surveillance and warfare technology,1 there are computer algorithms that search for changes in moles’ appearance over time. Deep convolutional neural networks analysis can group them into benign or malignant lesions with high accuracy.2 In a study by Esteva and colleagues,2 the convolutional neural networks algorithm differentiated between benign, malignant or non-neoplastic lesions with about 72% accuracy compared with about 66% accuracy by two dermatologists; for melanocytic lesions, the algorithm had a better sensitivity and specificity performance compared with the average of 21 dermatologists, although these findings still need to be replicated in independent datasets. Despite recent advances, there are still questions about how Australians can benefit from this technology and how it is best integrated into clinical practice.
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- 1. Leichman AK. App uses adapted Israeli airforce imaging tech to detect skin cancer. ISRAEL21c 2016; 3 May. https://www.israel21c.org/app-uses-adapted-iaf-imaging-tech-to-detect-skin-cancer (accessed June 2017).
- 2. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115-118.
- 3. Kassianos AP, Emery JD, Murchie P, Walter FM. Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review. Br J Dermatol 2015; 172: 1507-1518.
- 4. Aitken JF, Elwood M, Baade PD, et al. Clinical whole-body skin examination reduces the incidence of thick melanomas. Int J Cancer 2010; 126: 450-458.
- 5. International Skin Imaging Collaboration. ISBI 2016: skin lesion analysis towards melanoma detection [website]. https://challenge.kitware.com/#challenge/n/ISBI_2016%3A_Skin_Lesion_Analysis_Towards_Melanoma_Detection (accessed June 2017).
Monika Janda was funded by a National Health and Medical Research Council Career Development Fellowship (no. 1045247). H Peter Soyer is a shareholder of e-derm-consult GmbH and MoleMap by Dermatologists; he provides teledermatological reports regularly for both companies.