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

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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.

作者简介

Vasiliy Sergeev

Central state medical academy of department of presidential affairs

编辑信件的主要联系方式.
Email: vasesergeevu@gmail.com
ORCID iD: 0000-0001-8487-137X

MD, PhD

俄罗斯联邦, Moscow

Yu. Sergeev

Central state medical academy of department of presidential affairs

Email: vasesergeevu@gmail.com
ORCID iD: 0000-0002-4193-1579
俄罗斯联邦, Moscow

O. Tamrazova

Peoples’ Friendship University of Russia

Email: vasesergeevu@gmail.com
ORCID iD: 0000-0003-3261-6718
俄罗斯联邦, Moscow

V. Nikitaev

National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)

Email: vasesergeevu@gmail.com
ORCID iD: 0000-0002-4349-3023
俄罗斯联邦, Moscow

A. Pronichev

National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)

Email: vasesergeevu@gmail.com
ORCID iD: 0000-0003-0443-8504
俄罗斯联邦, Moscow

M. Sergeeva

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

Email: vasesergeevu@gmail.com
ORCID iD: 0000-0003-0292-5878
俄罗斯联邦, Moscow

参考

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