Diagnostic performance study on the melanoma automated diagnosis software powered by artificial intelligence technologies

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Abstract

INTRODUCTION: The research evaluates a series of publications on the machine recognition efficacy of cutaneous melanoma dermatoscopic images. Some authors report high sensitivity and specificity of automated diagnostics of skin tumors. Significant differences in the published data can be attributed to the use of different algorithms and groups of skin neoplasms to calculate the accuracy rate.

MATERIALS AND METHODS: The diagnostic performance of two automated artificial intelligence systems is compared.

RESULTS: The convolutional neural network algorithm improves the overall diagnostic accuracy by 7% compared to the algorithm without deep learning, while the overall accuracy rate was 78%. An initial set of 100 dermatoscopic images used in the study is published online for the assessment of the applicability of the obtained data when introducing existing artificial intelligence systems.

CONCLUSION: The main limitations and possible ways to further improve the automated diagnosis of skin tumors based on digital dermatoscopy are outlined.

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About the authors

Vasiliy Yu. Sergeev

Central state medical academy of department of presidential affairs

Author for correspondence.
Email: vasesergeevu@gmail.com
ORCID iD: 0000-0001-8487-137X

MD, PhD

Russian Federation, Moscow

Yu. Yu. Sergeev

Central state medical academy of department of presidential affairs

Email: vasesergeevu@gmail.com
ORCID iD: 0000-0002-4193-1579
Russian Federation, Moscow

O. B. Tamrazova

Peoples’ Friendship University of Russia

Email: vasesergeevu@gmail.com
ORCID iD: 0000-0003-3261-6718
Russian Federation, Moscow

V. G. Nikitaev

National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)

Email: vasesergeevu@gmail.com
ORCID iD: 0000-0002-4349-3023
Russian Federation, Moscow

A. N. Pronichev

National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)

Email: vasesergeevu@gmail.com
ORCID iD: 0000-0003-0443-8504
Russian Federation, Moscow

M. A. Sergeeva

I.M. Sechenov First Moscow State Medical University (Sechenov University)

Email: vasesergeevu@gmail.com
ORCID iD: 0000-0003-0292-5878
Russian Federation, Moscow

References

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