Cited by 25Cutaneous Melanoma Detection and ManagementTelemedicine and Telehealth ImplementationAI in cancer detection

Part II: Accuracy of Teledermatology in Skin Neoplasms

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Giavina-Bianchi, Mara, Azevedo, Maria Fernanda Dias, Sousa, Raquel Machado, Cordioli, Eduardo·2020·Frontiers in Medicine

Abstract

Teledermatology has been proving to be of great help for delivering healthcare, especially now, during the SARS-CoV-2 pandemic. It is crucial to assess how accurate this method can be for evaluating different dermatoses. Such knowledge can contribute to the dermatologists' decision of whether to adhere to teledermatology or not. Our objective was to determine the accuracy of teledermatology in the 10 most frequent skin neoplasms in our population, comparing telediagnosis to histopathological report and in-person dermatologists' diagnosis. A retrospective cohort study was conducted in São Paulo, Brazil, where a store-and-forward teledermatology project was implemented under primary-care attention to triage surgical, more complex, or severe dermatoses. A total of 30,976 patients presenting 55,012 lesions took part in the project. Thirteen teledermatologists who participated in the project had three options to refer the patients: send them directly to biopsy, to the in-person dermatologist, or back to the general physician with the most probable diagnosis and management. In the groups referred to the in-person dermatologist and biopsy, we looked for the 10 most frequent International Statistical Classification of Diseases and Related Health Problems-10 (ICD-10) of skin neoplasms, which resulted in 289 histopathologic reports and 803 in-person dermatologists' diagnosis. We were able to compare the ICD-10 codes filled by teledermatologists, in-person dermatologists, and from histopathological reports. The proportion of complete, partial, and no agreement rates between the in-person dermatologist's, histopathologic report, and the teledermatologist's diagnosis was assessed. We also calculated Cohen's kappa, for complete and complete plus partial agreement. The mean complete agreement rate comparing telediagnosis to histopathological report was 54% (157/289; kappa = 0.087), being the highest for malign lesions; to in-person dermatologists was 61% (487/803; kappa = 0.213), highest for benign lesions. When accuracy of telediagnosis for either malign or benign lesions was evaluated, the agreement rate with histopathology was 70% (kappa = 0.529) and with in-person dermatologist, 81% (kappa = 0.582). This study supports that teledermatology for skin neoplasms has moderate accuracy. This result reassures that it can be a proper option for patient care, especially when the goal is to differentiate benign from malign lesions.

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  • The diagnostic accuracy of clinician-initiated images was higher for malignant diagnoses such as melanomas and nonmelanoma skin cancers than benign diagnoses.

    “A recent study found that the diagnostic accuracy of clinician-initiated images was higher for malignant diagnoses such as melanomas and nonmelanoma skin cancers than benign diagnoses [19].”

    From Store-and-Forward Images in Teledermatology: Narrative Literature Review (2022) · cites this paper as [19]

    The Results report that, against histopathology, teledermatology accuracy was highest for melanoma (88%), basal cell carcinoma (75%), and squamous cell carcinoma (71%); the Discussion reports 94% agreement for malignant lesions versus 61% for benign lesions in that comparison. But the Discussion also reports the reverse pattern against in-person dermatologists—73% for malignant versus 82% for benign lesions—so the unqualified claim overgeneralizes the finding. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 11

  1. [1]Lee JJ English JC. Teledermatology: a review and update. Am J Clin Dermatol. (2018) 19: 253– 60. 10.1007/s40257-017-0317-6 28871562 · DOI · Paper page
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  11. [20]Elliott TM Whiteman DC Olsen CM Gordon LG Estimated healthcare costs of melanoma in Australia over 3 years post-diagnosis. Appl Health Econ Health Policy. (2017) 15: 805– 16. 10.1007/s40258-017-0341-y 28756584 · DOI

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Authors
Giavina-Bianchi, M., Azevedo, M. F. D., Sousa, R. M., & Cordioli, E.
Affiliations
Hospital Israelita Albert Einstein (BR)
Venue
Frontiers in Medicine
Published
2020-11-23
Volume
7
Pages
598903-598903
ISSN
2296-858X (ISSN-L)
OpenAlex
W3109953301
Cited by
25
References
9
Citations by year
2026: 3 · 2025: 2 · 2024: 4 · 2023: 6 · 2022: 6 · 2021: 4
FWCI
1.44
Type
journal article
Language
en
Open access
Locations
  • Frontiers in Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
teledermatology, skin cancer, diagnostic accuracy, store-and-forward telemedicine, histopathology comparison, primary care triage, COVID-19 pandemic, São Paulo, Brazil, Brazil

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