Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma

S M Rajpara, A P Botello, J Townend, A D Ormerod

Research output: Contribution to journalArticle

79 Citations (Scopus)

Abstract

BACKGROUND: Dermoscopy improves diagnostic accuracy of the unaided eye for melanoma, and digital dermoscopy with artificial intelligence or computer diagnosis has also been shown useful for the diagnosis of melanoma. At present there is no clear evidence regarding the diagnostic accuracy of dermoscopy compared with artificial intelligence. OBJECTIVES: To evaluate the diagnostic accuracy of dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis and to compare the diagnostic accuracy of the different dermoscopic algorithms with each other and with digital dermoscopy/artificial intelligence for the detection of melanoma. METHODS: A literature search on dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis was performed using several databases. Titles and abstracts of the retrieved articles were screened using a literature evaluation form. A quality assessment form was developed to assess the quality of the included studies. Heterogeneity among the studies was assessed. Pooled data were analysed using meta-analytical methods and comparisons between different algorithms were performed. RESULTS: Of 765 articles retrieved, 30 studies were eligible for meta-analysis. Pooled sensitivity for artificial intelligence was slightly higher than for dermoscopy (91% vs. 88%; P = 0.076). Pooled specificity for dermoscopy was significantly better than artificial intelligence (86% vs. 79%; P <0.001). Pooled diagnostic odds ratio was 51.5 for dermoscopy and 57.8 for artificial intelligence, which were not significantly different (P = 0.783). There were no significance differences in diagnostic odds ratio among the different dermoscopic diagnostic algorithms. CONCLUSIONS: Dermoscopy and artificial intelligence performed equally well for diagnosis of melanocytic skin lesions. There was no significant difference in the diagnostic performance of various dermoscopy algorithms. The three-point checklist, the seven-point checklist and Menzies score had better diagnostic odds ratios than the others; however, these results need to be confirmed by a large-scale high-quality population-based study.
Original languageEnglish
Pages (from-to)591-604
Number of pages14
JournalBritish Journal of Dermatology
Volume161
Issue number3
Early online date19 Mar 2009
DOIs
Publication statusPublished - Sep 2009

Fingerprint

Dermoscopy
Artificial Intelligence
Melanoma
Odds Ratio
Checklist

Keywords

  • algorithms
  • dermoscopy
  • humans
  • image processing, computer-assisted
  • melanoma
  • sensitivity and specificity
  • skin neoplasms
  • artificial intelligence
  • digital dermoscopy
  • systematic review

Cite this

Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma. / Rajpara, S M; Botello, A P; Townend, J; Ormerod, A D.

In: British Journal of Dermatology, Vol. 161, No. 3, 09.2009, p. 591-604.

Research output: Contribution to journalArticle

Rajpara, S M ; Botello, A P ; Townend, J ; Ormerod, A D. / Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma. In: British Journal of Dermatology. 2009 ; Vol. 161, No. 3. pp. 591-604.
@article{31d3f089e05a4814997da40e16faf134,
title = "Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma",
abstract = "BACKGROUND: Dermoscopy improves diagnostic accuracy of the unaided eye for melanoma, and digital dermoscopy with artificial intelligence or computer diagnosis has also been shown useful for the diagnosis of melanoma. At present there is no clear evidence regarding the diagnostic accuracy of dermoscopy compared with artificial intelligence. OBJECTIVES: To evaluate the diagnostic accuracy of dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis and to compare the diagnostic accuracy of the different dermoscopic algorithms with each other and with digital dermoscopy/artificial intelligence for the detection of melanoma. METHODS: A literature search on dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis was performed using several databases. Titles and abstracts of the retrieved articles were screened using a literature evaluation form. A quality assessment form was developed to assess the quality of the included studies. Heterogeneity among the studies was assessed. Pooled data were analysed using meta-analytical methods and comparisons between different algorithms were performed. RESULTS: Of 765 articles retrieved, 30 studies were eligible for meta-analysis. Pooled sensitivity for artificial intelligence was slightly higher than for dermoscopy (91{\%} vs. 88{\%}; P = 0.076). Pooled specificity for dermoscopy was significantly better than artificial intelligence (86{\%} vs. 79{\%}; P <0.001). Pooled diagnostic odds ratio was 51.5 for dermoscopy and 57.8 for artificial intelligence, which were not significantly different (P = 0.783). There were no significance differences in diagnostic odds ratio among the different dermoscopic diagnostic algorithms. CONCLUSIONS: Dermoscopy and artificial intelligence performed equally well for diagnosis of melanocytic skin lesions. There was no significant difference in the diagnostic performance of various dermoscopy algorithms. The three-point checklist, the seven-point checklist and Menzies score had better diagnostic odds ratios than the others; however, these results need to be confirmed by a large-scale high-quality population-based study.",
keywords = "algorithms, dermoscopy, humans, image processing, computer-assisted, melanoma, sensitivity and specificity, skin neoplasms, artificial intelligence, digital dermoscopy, systematic review",
author = "Rajpara, {S M} and Botello, {A P} and J Townend and Ormerod, {A D}",
year = "2009",
month = "9",
doi = "10.1111/j.1365-2133.2009.09093.x",
language = "English",
volume = "161",
pages = "591--604",
journal = "British Journal of Dermatology",
issn = "0007-0963",
publisher = "Wiley",
number = "3",

}

TY - JOUR

T1 - Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma

AU - Rajpara, S M

AU - Botello, A P

AU - Townend, J

AU - Ormerod, A D

PY - 2009/9

Y1 - 2009/9

N2 - BACKGROUND: Dermoscopy improves diagnostic accuracy of the unaided eye for melanoma, and digital dermoscopy with artificial intelligence or computer diagnosis has also been shown useful for the diagnosis of melanoma. At present there is no clear evidence regarding the diagnostic accuracy of dermoscopy compared with artificial intelligence. OBJECTIVES: To evaluate the diagnostic accuracy of dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis and to compare the diagnostic accuracy of the different dermoscopic algorithms with each other and with digital dermoscopy/artificial intelligence for the detection of melanoma. METHODS: A literature search on dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis was performed using several databases. Titles and abstracts of the retrieved articles were screened using a literature evaluation form. A quality assessment form was developed to assess the quality of the included studies. Heterogeneity among the studies was assessed. Pooled data were analysed using meta-analytical methods and comparisons between different algorithms were performed. RESULTS: Of 765 articles retrieved, 30 studies were eligible for meta-analysis. Pooled sensitivity for artificial intelligence was slightly higher than for dermoscopy (91% vs. 88%; P = 0.076). Pooled specificity for dermoscopy was significantly better than artificial intelligence (86% vs. 79%; P <0.001). Pooled diagnostic odds ratio was 51.5 for dermoscopy and 57.8 for artificial intelligence, which were not significantly different (P = 0.783). There were no significance differences in diagnostic odds ratio among the different dermoscopic diagnostic algorithms. CONCLUSIONS: Dermoscopy and artificial intelligence performed equally well for diagnosis of melanocytic skin lesions. There was no significant difference in the diagnostic performance of various dermoscopy algorithms. The three-point checklist, the seven-point checklist and Menzies score had better diagnostic odds ratios than the others; however, these results need to be confirmed by a large-scale high-quality population-based study.

AB - BACKGROUND: Dermoscopy improves diagnostic accuracy of the unaided eye for melanoma, and digital dermoscopy with artificial intelligence or computer diagnosis has also been shown useful for the diagnosis of melanoma. At present there is no clear evidence regarding the diagnostic accuracy of dermoscopy compared with artificial intelligence. OBJECTIVES: To evaluate the diagnostic accuracy of dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis and to compare the diagnostic accuracy of the different dermoscopic algorithms with each other and with digital dermoscopy/artificial intelligence for the detection of melanoma. METHODS: A literature search on dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis was performed using several databases. Titles and abstracts of the retrieved articles were screened using a literature evaluation form. A quality assessment form was developed to assess the quality of the included studies. Heterogeneity among the studies was assessed. Pooled data were analysed using meta-analytical methods and comparisons between different algorithms were performed. RESULTS: Of 765 articles retrieved, 30 studies were eligible for meta-analysis. Pooled sensitivity for artificial intelligence was slightly higher than for dermoscopy (91% vs. 88%; P = 0.076). Pooled specificity for dermoscopy was significantly better than artificial intelligence (86% vs. 79%; P <0.001). Pooled diagnostic odds ratio was 51.5 for dermoscopy and 57.8 for artificial intelligence, which were not significantly different (P = 0.783). There were no significance differences in diagnostic odds ratio among the different dermoscopic diagnostic algorithms. CONCLUSIONS: Dermoscopy and artificial intelligence performed equally well for diagnosis of melanocytic skin lesions. There was no significant difference in the diagnostic performance of various dermoscopy algorithms. The three-point checklist, the seven-point checklist and Menzies score had better diagnostic odds ratios than the others; however, these results need to be confirmed by a large-scale high-quality population-based study.

KW - algorithms

KW - dermoscopy

KW - humans

KW - image processing, computer-assisted

KW - melanoma

KW - sensitivity and specificity

KW - skin neoplasms

KW - artificial intelligence

KW - digital dermoscopy

KW - systematic review

U2 - 10.1111/j.1365-2133.2009.09093.x

DO - 10.1111/j.1365-2133.2009.09093.x

M3 - Article

VL - 161

SP - 591

EP - 604

JO - British Journal of Dermatology

JF - British Journal of Dermatology

SN - 0007-0963

IS - 3

ER -