Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions

John Paoli, Ilkka Pölönen, Mari Salmivuori, Janne Räsänen, Oscar Zaar, Sam Polesie, Sari Koskenmies, Sari Pitkänen, Meri Övermark, Kirsi Isoherranen, Susanna Juteau, Annamari Ranki, Mari Grönroos, Noora Neittaanmäki

Research output: Contribution to journalArticleScientificpeer-review


Malignant melanoma poses a clinical diagnostic pro-blem, since a large number of benign lesions are excised to find a single melanoma. This study assessed the accuracy of a novel non-invasive diagnostic tech-nology, hyperspectral imaging, for melanoma detection. Lesions were imaged prior to excision and histo-pathological analysis. A deep neural network algorithm was trained twice to distinguish between histopatho-logically verified malignant and benign melanocytic lesions and to classify the separate subgroups. Further-more, 2 different approaches were used: a majority vote classification and a pixel-wise classification. The study included 325 lesions from 285 patients. Of these, 74 were invasive melanoma, 88 melanoma in situ, 115 dysplastic naevi, and 48 non-dysplastic naevi. The study included a training set of 358,800 pixels and a vali-dation set of 7,313 pixels, which was then tested with a training set of 24,375 pixels. The majority vote classification achieved high overall sensitivity of 95% and a specificity of 92% (95% confidence interval (95% CI) 0.024–0.029) in differentiating malignant from benign lesions. In the pixel-wise classification, the overall sensitivity and specificity were both 82% (95% CI 0.005–0.005). When divided into 4 subgroups, the diagnostic accuracy was lower. Hyperspectral imaging provides high sensitivity and specificity in distinguis-hing between naevi and melanoma. This novel method still needs further validation.

Original languageEnglish
Article numberadv00815
JournalActa Dermato-Venereologica
Number of pages7
Publication statusPublished - 2022
MoE publication typeA1 Journal article-refereed

Bibliographical note

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© 2022, Medical Journals/Acta D-V. All rights reserved.

Fields of Science

  • hyperspectral imaging
  • machine learning
  • malignant melanoma
  • non-invasive diagnostic
  • 3121 General medicine, internal medicine and other clinical medicine

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