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Cited by 159Cutaneous Melanoma Detection and ManagementAI in cancer detectionmelanin and skin pigmentation

Dermoscopic diagnosis by a trained clinician vs. a clinician with minimal dermoscopy training vs. computer-aided diagnosis of 341 pigmented skin lesions: a comparative study

Piccolo, D., Ferrari, A., Peris, K., Daidone, R., Ruggeri, B., Chimenti, S.·2002·British Journal of Dermatology
DOI 10.1046/j.1365-2133.2002.04978.x
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Abstract

BACKGROUND: In the last few years digital dermoscopy has been introduced as an additional tool to improve the clinical diagnosis of pigmented skin lesions. OBJECTIVE: To evaluate the validity of digital dermoscopy by comparing the diagnoses of a dermatologist experienced in dermoscopy (5 years of experience) with those of a clinician with minimal training in this field, and then comparing these results with those obtained using computer-aided diagnoses. METHODS: Three hundred and forty-one pigmented melanocytic and non-melanocytic skin lesions were included. All lesions were surgically excised and histopathologically examined. Digital dermoscopic images of all lesions were framed and analysed using software based on a trained artificial neural network. Cohen's kappa statistic was calculated to assess the validity with regard to the correct diagnoses of melanoma and non-melanoma. RESULTS: Sensitivity was high for the experienced dermatologist and the computer (92%) and lower for the inexperienced clinician (69%). Specificity of the diagnosis by the experienced dermatologist was higher (99%) than that of the inexperienced clinician (94%) and the computer assessment (74%). Notably, computer analysis gave a higher number of false positives (26%) compared with the experienced dermatologist (0.6%) and the inexperienced clinician (5.5%). CONCLUSIONS: Our results indicate that analysis either by a trained dermatologist or an artificial neural network-trained computer can improve the diagnostic accuracy of melanoma compared with that of an inexperienced clinician and that the computer diagnosis might represent a useful tool for the screening of melanoma, particularly at centres not experienced in dermoscopy.

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  • Dermoscopy adds additional value in primary care when used by an expert or trained user.

    “Dermoscopy adds additional value in primary care when used by an expert or trained user27–30.”

    From Dermoscopy practice guidelines for use in telemedicine (2022) · cites this paper as [28]

  • Teledermoscopy has the potential to assist with managing clinician workloads, increasing access to dermatologists and potentially avoiding unnecessary biopsies and other investigations which occur at a higher rate when performed by non-dermatologist and less experienced clinicians.

    “Thus, teledermoscopy has the potential to assist with managing clinician workloads, increasing access to dermatologists and potentially avoiding unnecessary biopsies and other investigations which occur at a higher rate when performed by non-dermatologist and less experienced clinicians [15].”

    From Consumer preferences for teledermoscopy screening to detect melanoma early (2016) · cites this paper as [15]

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Authors
Piccolo, D., Ferrari, A., Peris, K., Daidone, R., Ruggeri, B., & Chimenti, S.
Affiliations
  • University of L'Aquila (IT)
  • University of Rome Tor Vergata (IT)
Corresponding author
Peris, K.
ORCID
Venue
British Journal of Dermatology
Publisher
Oxford University Press (OUP)
Published
2002-09
Volume
147
Issue
3
Pages
481-486
ISSN
  • 0007-0963 (ISSN-L)
  • 1365-2133
DOI
10.1046/j.1365-2133.2002.04978.x
OpenAlex
W2065829097
Cited by
159
References
26
Citations by year
2026: 5 · 2025: 6 · 2024: 10 · 2023: 4 · 2022: 7 · 2021: 10 · 2020: 8 · 2019: 7 · 2018: 6 · 2017: 9 · 2016: 8 · 2015: 9 · 2014: 4 · 2013: 9 · 2012: 12
FWCI
1.63
Type
journal article
Language
en
Open access
closed
Licence
doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 4748 d)
Locations
  • British Journal of Dermatology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › melanin and skin pigmentation
Keywords
digital dermoscopy, pigmented skin lesions, melanoma diagnosis, computer-aided diagnosis, artificial neural networks, dermoscopic diagnosis, diagnostic accuracy, melanoma screening, melanocytic lesions, false positives
MeSH terms
Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Clinical Competence, Dermatology, Diagnosis, Computer-Assisted, Diagnosis, Differential, Female, Humans, Image Processing, Computer-Assisted, Male, Melanoma, Middle Aged, Nevus, Pigmented, Predictive Value of Tests, Sensitivity and Specificity, Skin Neoplasms, Reproducibility of Results, Neural Networks, Computer, Microscopy, Video

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