Hyperspectral imaging in automated digital dermoscopy screening for melanoma

Anna Marie Hosking, Brandon J. Coakley, Dorothy Chang, Faezeh Talebi-Liasi, Samantha Lish, Sung Won Lee, Amanda M. Zong, Ian Moore, James Browning, Steven L. Jacques, James G. Krueger, Kristen M. Kelly, Kenneth G. Linden, Daniel S. Gareau

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Objectives: Early melanoma detection decreases morbidity and mortality. Early detection classically involves dermoscopy to identify suspicious lesions for which biopsy is indicated. Biopsy and histological examination then diagnose benign nevi, atypical nevi, or cancerous growths. With current methods, a considerable number of unnecessary biopsies are performed as only 11% of all biopsied, suspicious lesions are actually melanomas. Thus, there is a need for more advanced noninvasive diagnostics to guide the decision of whether or not to biopsy. Artificial intelligence can generate screening algorithms that transform a set of imaging biomarkers into a risk score that can be used to classify a lesion as a melanoma or a nevus by comparing the score to a classification threshold. Melanoma imaging biomarkers have been shown to be spectrally dependent in Red, Green, Blue (RGB) color channels, and hyperspectral imaging may further enhance diagnostic power. The purpose of this study was to use the same melanoma imaging biomarkers previously described, but over a wider range of wavelengths to determine if, in combination with machine learning algorithms, this could result in enhanced melanoma detection. Methods: We used the melanoma advanced imaging dermatoscope (mAID) to image pigmented lesions assessed by dermatologists as requiring a biopsy. The mAID is a 21-wavelength imaging device in the 350–950 nm range. We then generated imaging biomarkers from these hyperspectral dermoscopy images, and, with the help of artificial intelligence algorithms, generated a melanoma Q-score for each lesion (0 = nevus, 1 = melanoma). The Q-score was then compared to the histopathologic diagnosis. Results: The overall sensitivity and specificity of hyperspectral dermoscopy in detecting melanoma when evaluated in a set of lesions selected by dermatologists as requiring biopsy was 100% and 36%, respectively. Conclusion: With widespread application, and if validated in larger clinical trials, this non-invasive methodology could decrease unnecessary biopsies and potentially increase life-saving early detection events. Lasers Surg. Med. 51:214–222, 2019.

Original languageEnglish (US)
Pages (from-to)214-222
Number of pages9
JournalLasers in Surgery and Medicine
Volume51
Issue number3
DOIs
StatePublished - Mar 2019
Externally publishedYes

Keywords

  • artificial intelligence
  • dermoscopy
  • hyperspectral imaging
  • machine learning
  • melanoma

ASJC Scopus subject areas

  • Surgery
  • Dermatology

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  • Cite this

    Hosking, A. M., Coakley, B. J., Chang, D., Talebi-Liasi, F., Lish, S., Lee, S. W., Zong, A. M., Moore, I., Browning, J., Jacques, S. L., Krueger, J. G., Kelly, K. M., Linden, K. G., & Gareau, D. S. (2019). Hyperspectral imaging in automated digital dermoscopy screening for melanoma. Lasers in Surgery and Medicine, 51(3), 214-222. https://doi.org/10.1002/lsm.23055