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BMC Medical Imaging

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Open Access 01-12-2019 | Nevi | Research article

Automated detection of nonmelanoma skin cancer using digital images: a systematic review

Authors: Arthur Marka, Joi B. Carter, Ermal Toto, Saeed Hassanpour

Published in: BMC Medical Imaging | Issue 1/2019

Abstract

Background

Computer-aided diagnosis of skin lesions is a growing area of research, but its application to nonmelanoma skin cancer (NMSC) is relatively under-studied. The purpose of this review is to synthesize the research that has been conducted on automated detection of NMSC using digital images and to assess the quality of evidence for the diagnostic accuracy of these technologies.

Methods

Eight databases (PubMed, Google Scholar, Embase, IEEE Xplore, Web of Science, SpringerLink, ScienceDirect, and the ACM Digital Library) were searched to identify diagnostic studies of NMSC using image-based machine learning models. Two reviewers independently screened eligible articles. The level of evidence of each study was evaluated using a five tier rating system, and the applicability and risk of bias of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool.

Results

Thirty-nine studies were reviewed. Twenty-four models were designed to detect basal cell carcinoma, two were designed to detect squamous cell carcinoma, and thirteen were designed to detect both. All studies were conducted in silico. The overall diagnostic accuracy of the classifiers, defined as concordance with histopathologic diagnosis, was high, with reported accuracies ranging from 72 to 100% and areas under the receiver operating characteristic curve ranging from 0.832 to 1. Most studies had substantial methodological limitations, but several were robustly designed and presented a high level of evidence.

Conclusion

Most studies of image-based NMSC classifiers report performance greater than or equal to the reported diagnostic accuracy of the average dermatologist, but relatively few studies have presented a high level of evidence. Clinical studies are needed to assess whether these technologies can feasibly be implemented as a real-time aid for clinical diagnosis of NMSC.

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Title: Automated detection of nonmelanoma skin cancer using digital images: a systematic review
Authors: Arthur Marka
Joi B. Carter
Ermal Toto
Saeed Hassanpour
Publication date: 01-12-2019
Publisher: BioMed Central
Keywords: Nevi
Basalioma
Cutaneous Squamous Cell Carcinoma
Artificial Intelligence
Basalioma
Cutaneous Squamous Cell Carcinoma
Basalioma
Basalioma
Skin Cancer
Published in: BMC Medical Imaging / Issue 1/2019
Electronic ISSN: 1471-2342
DOI: https://doi.org/10.1186/s12880-019-0307-7

At a glance: The STEP trials

Springer Medicine

Automated detection of nonmelanoma skin cancer using digital images: a systematic review

Abstract

Background

Methods

Results

Conclusion

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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