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Automated diagnosis of melanoma

Monika Janda and H Peter Soyer
Med J Aust 2017; 207 (8): 361-362. || doi: 10.5694/mja17.00618
Published online: 16 October 2017

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.

  • Monika Janda1
  • H Peter Soyer2,3

  • 1 Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD
  • 2 Dermatology Research Centre, University of Queensland Diamantina Institute, Brisbane, QLD
  • 3 Princess Alexandra Hospital, Brisbane, QLD

Correspondence: m.janda@qut.edu.au

Competing interests:

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.

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