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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
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 user27–30.”
From Dermoscopy practice guidelines for use in telemedicine (2022) · cites this paper as [28]
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“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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Metadata
- 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.
- 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
- 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
- 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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