The application of artificial intelligence in the detection of basal cell carcinoma: A systematic review

Y. Widaatalla; T. Wolswijk; F. Adan; L.M. Hillen; H.C. Woodruff; I. Halilaj; A. Ibrahim; P. Lambin; K. Mosterd

doi:10.1111/jdv.18963

The application of artificial intelligence in the detection of basal cell carcinoma: A systematic review

Y. Widaatalla^*, T. Wolswijk, F. Adan, L.M. Hillen, H.C. Woodruff, I. Halilaj, A. Ibrahim, P. Lambin, K. Mosterd

^*Corresponding author for this work

Research output: Contribution to journal › (Systematic) Review article › peer-review

Abstract

Basal cell carcinoma (BCC) is one of the most common types of cancer. The growing incidence worldwide and the need for fast, reliable and less invasive diagnostic techniques make a strong case for the application of different artificial intelligence techniques for detecting and classifying BCC and its subtypes. We report on the current evidence regarding the application of handcrafted and deep radiomics models used for the detection and classification of BCC in dermoscopy, optical coherence tomography and reflectance confocal microscopy. We reviewed all the articles that were published in the last 10 years in PubMed, Web of Science and EMBASE, and we found 15 articles that met the inclusion criteria. We included articles that are original, written in English, focussing on automated BCC detection in our target modalities and published within the last 10 years in the field of dermatology. The outcomes from the selected publications are presented in three categories depending on the imaging modality and to allow for comparison. The majority of articles (n = 12) presented different AI solutions for the detection and/or classification of BCC in dermoscopy images. The rest of the publications presented AI solutions in OCT images (n = 2) and RCM (n = 1). In addition, we provide future directions for the application of these techniques for the detection of BCC. In conclusion, the reviewed publications demonstrate the potential benefit of AI in the detection of BCC in dermoscopy, OCT and RCM.

Original language	English
Pages (from-to)	1160-1167
Number of pages	8
Journal	Journal of the European Academy of Dermatology and Venereology
Volume	37
Issue number	6
Early online date	Feb 2023
DOIs	https://doi.org/10.1111/jdv.18963
Publication status	Published - Jun 2023

Keywords

OPTICAL COHERENCE TOMOGRAPHY
PIGMENTED SKIN-LESIONS
TEST-RETEST
RADIOMICS
CLASSIFICATION
DIAGNOSIS
DERMOSCOPY
FEATURES
SEGMENTATION
DERMATOLOGY

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@article{793dbeaa8e834b398103118f25c2932c,

title = "The application of artificial intelligence in the detection of basal cell carcinoma: A systematic review",

abstract = "Basal cell carcinoma (BCC) is one of the most common types of cancer. The growing incidence worldwide and the need for fast, reliable and less invasive diagnostic techniques make a strong case for the application of different artificial intelligence techniques for detecting and classifying BCC and its subtypes. We report on the current evidence regarding the application of handcrafted and deep radiomics models used for the detection and classification of BCC in dermoscopy, optical coherence tomography and reflectance confocal microscopy. We reviewed all the articles that were published in the last 10 years in PubMed, Web of Science and EMBASE, and we found 15 articles that met the inclusion criteria. We included articles that are original, written in English, focussing on automated BCC detection in our target modalities and published within the last 10 years in the field of dermatology. The outcomes from the selected publications are presented in three categories depending on the imaging modality and to allow for comparison. The majority of articles (n = 12) presented different AI solutions for the detection and/or classification of BCC in dermoscopy images. The rest of the publications presented AI solutions in OCT images (n = 2) and RCM (n = 1). In addition, we provide future directions for the application of these techniques for the detection of BCC. In conclusion, the reviewed publications demonstrate the potential benefit of AI in the detection of BCC in dermoscopy, OCT and RCM.",

keywords = "OPTICAL COHERENCE TOMOGRAPHY, PIGMENTED SKIN-LESIONS, TEST-RETEST, RADIOMICS, CLASSIFICATION, DIAGNOSIS, DERMOSCOPY, FEATURES, SEGMENTATION, DERMATOLOGY",

author = "Y. Widaatalla and T. Wolswijk and F. Adan and L.M. Hillen and H.C. Woodruff and I. Halilaj and A. Ibrahim and P. Lambin and K. Mosterd",

note = "Funding Information: We would like to thank Prof. Dr. Mariano Suppa and Dr. Cl{\'e}ment Lenoir from H{\^o}pital Erasme Brussels for providing the reflectance confocal microscopy image. Publisher Copyright: {\textcopyright} 2023 The Authors. Journal of the European Academy of Dermatology and Venereology published by John Wiley & Sons Ltd on behalf of European Academy of Dermatology and Venereology.",

year = "2023",

month = jun,

doi = "10.1111/jdv.18963",

language = "English",

volume = "37",

pages = "1160--1167",

journal = "Journal of the European Academy of Dermatology and Venereology",

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T1 - The application of artificial intelligence in the detection of basal cell carcinoma: A systematic review

AU - Widaatalla, Y.

AU - Wolswijk, T.

AU - Adan, F.

AU - Hillen, L.M.

AU - Woodruff, H.C.

AU - Halilaj, I.

AU - Ibrahim, A.

AU - Lambin, P.

AU - Mosterd, K.

N1 - Funding Information: We would like to thank Prof. Dr. Mariano Suppa and Dr. Clément Lenoir from Hôpital Erasme Brussels for providing the reflectance confocal microscopy image. Publisher Copyright: © 2023 The Authors. Journal of the European Academy of Dermatology and Venereology published by John Wiley & Sons Ltd on behalf of European Academy of Dermatology and Venereology.

PY - 2023/6

Y1 - 2023/6

N2 - Basal cell carcinoma (BCC) is one of the most common types of cancer. The growing incidence worldwide and the need for fast, reliable and less invasive diagnostic techniques make a strong case for the application of different artificial intelligence techniques for detecting and classifying BCC and its subtypes. We report on the current evidence regarding the application of handcrafted and deep radiomics models used for the detection and classification of BCC in dermoscopy, optical coherence tomography and reflectance confocal microscopy. We reviewed all the articles that were published in the last 10 years in PubMed, Web of Science and EMBASE, and we found 15 articles that met the inclusion criteria. We included articles that are original, written in English, focussing on automated BCC detection in our target modalities and published within the last 10 years in the field of dermatology. The outcomes from the selected publications are presented in three categories depending on the imaging modality and to allow for comparison. The majority of articles (n = 12) presented different AI solutions for the detection and/or classification of BCC in dermoscopy images. The rest of the publications presented AI solutions in OCT images (n = 2) and RCM (n = 1). In addition, we provide future directions for the application of these techniques for the detection of BCC. In conclusion, the reviewed publications demonstrate the potential benefit of AI in the detection of BCC in dermoscopy, OCT and RCM.

AB - Basal cell carcinoma (BCC) is one of the most common types of cancer. The growing incidence worldwide and the need for fast, reliable and less invasive diagnostic techniques make a strong case for the application of different artificial intelligence techniques for detecting and classifying BCC and its subtypes. We report on the current evidence regarding the application of handcrafted and deep radiomics models used for the detection and classification of BCC in dermoscopy, optical coherence tomography and reflectance confocal microscopy. We reviewed all the articles that were published in the last 10 years in PubMed, Web of Science and EMBASE, and we found 15 articles that met the inclusion criteria. We included articles that are original, written in English, focussing on automated BCC detection in our target modalities and published within the last 10 years in the field of dermatology. The outcomes from the selected publications are presented in three categories depending on the imaging modality and to allow for comparison. The majority of articles (n = 12) presented different AI solutions for the detection and/or classification of BCC in dermoscopy images. The rest of the publications presented AI solutions in OCT images (n = 2) and RCM (n = 1). In addition, we provide future directions for the application of these techniques for the detection of BCC. In conclusion, the reviewed publications demonstrate the potential benefit of AI in the detection of BCC in dermoscopy, OCT and RCM.

KW - OPTICAL COHERENCE TOMOGRAPHY

KW - PIGMENTED SKIN-LESIONS

KW - TEST-RETEST

KW - RADIOMICS

KW - CLASSIFICATION

KW - DIAGNOSIS

KW - DERMOSCOPY

KW - FEATURES

KW - SEGMENTATION

KW - DERMATOLOGY

U2 - 10.1111/jdv.18963

DO - 10.1111/jdv.18963

M3 - (Systematic) Review article

C2 - 36785993

SN - 0926-9959

VL - 37

SP - 1160

EP - 1167

JO - Journal of the European Academy of Dermatology and Venereology

JF - Journal of the European Academy of Dermatology and Venereology

IS - 6

ER -

The application of artificial intelligence in the detection of basal cell carcinoma: A systematic review

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Keywords

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