Efficacy of smartphone applications in high-risk pigmented lesions
Open accessAbstract
BACKGROUND/OBJECTIVES: Melanoma apps are smartphone applications that assess risk of pigmented lesions using a smartphone camera and underlying algorithm. We aimed to assess the capability of melanoma smartphone applications (apps) in making clinical decisions about risk, compared with lesion assessment by specialist trained dermatologists. METHODS: A prospective study of 3 melanoma apps was conducted between 2015 and 2016, recruiting 30 patients with 57 pigmented lesions. Risk categories assigned by the apps were compared with the clinical decisions of two consultant dermatologists classifying lesions as 'suspicious' or 'benign'. RESULTS: Of the 42 lesions deemed clinically suspicious to a dermatologist, from 9 to 26 were classified as suspicious by the apps; of the 15 clinically benign lesions 3 to 15 were correctly classified as benign by the apps. The apps' sensitivity and specificity ranged from 21 to 72% and 27 to 100.0%, respectively, when compared with the specialists' decisions. Two apps were unable to analyse 14 and 18% of lesions submitted, respectively. Interrater agreement between dermatologists and apps was poor (κ = -0.01 SE = 0.16; P = 0.97) to slight (κ = 0.16 SE = 0.09; P = 0.12). CONCLUSIONS: None of the melanoma apps tested had high enough agreement with the dermatologist's clinical opinion to be considered to provide additional benefit to patients in assessing their skin for high-risk pigmented lesions. The low sensitivity in detecting lesions that are suspicious to a trained specialist may mean false reassurance is being given to patients. Development of highly sensitive and specific melanoma apps remains a work in progress.
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Three studies assessed the SkinVision app.
“Three studies assessed the SkinVision app.24 29 30”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [24]
When apps failed to return a risk assessment, images were either excluded a priori from the studies, excluded from analysis, or were not reported.
“When apps failed to return a risk assessment, images were either excluded a priori from the studies,23 excluded from analysis,24 28 30 31 32 34 or were not reported.29”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [24]
For apps with uncertain availability or withdrawn apps, MelApp had low sensitivities (25-50%) and Mole Detective, Dr Mole, and SpotMole had low specificities (20-60%).
“with either low sensitivities (25-50% for MelApp) or specificities (20-60% for Mole Detective, Dr Mole, and SpotMole).”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [24]
The SkinVision app was verified against expert recommendations for further investigation or intervention, presumably using the original version of the app.
“The app was verified against expert recommendations for further investigation or intervention, presumably using the original version of the app.24 29 30”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [24]
Agreement between the SkinVision app and expert lesion assessment was poor and variable regardless of the threshold for test positivity applied.
“Agreement between the app and the expert lesion assessment was poor and variable regardless of the threshold for test positivity applied (fig 2).”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [24]
When high and moderate risk results were considered as test positive, sensitivities of the SkinVision app ranged from 57% to 78%, and specificities from 27% to 50%.
“When high and moderate risk results were considered as test positive, sensitivities ranged from 57% to 78%, and specificities from 27% to 50% (fig 2).”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [24]
Results from the five studies that reported data for apps with uncertain availability or withdrawn apps are if anything more variable.
“Results from the five studies that reported data for apps with uncertain availability23 24 or withdrawn apps23 32 are if anything more variable.”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [24]
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- Authors
- Ngoo, A., Finnane, A., McMeniman, E., Tan, J., Janda, M., & Soyer, H. P.
- Affiliations
- The University of Queensland (AU)
- Queensland University of Technology (AU)
- Corresponding author
- Finnane, Anna
- Venue
- Australasian Journal of Dermatology
- Publisher
- Wiley
- Published
- 2017-02-27
- Volume
- 59
- Issue
- 3
- Pages
- e175-e182
- ISSN
- 0004-8380 (ISSN-L)
- 1440-0960
- OpenAlex
- W2594235371
- Cited by
- 55
- References
- 14
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 3 · 2023: 4 · 2022: 3 · 2021: 9 · 2020: 8 · 2019: 10 · 2018: 7 · 2017: 5
- FWCI
- 3.62
- Type
- journal article
- Language
- en
- Open access
- green
- eprints.qut.edu.au
- Locations
- Australasian Journal of Dermatology (publishedVersion)
- PubMed (publishedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion)
- Funders
- National Health and Medical Research Council
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › melanin and skin pigmentation
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- mobile application, pigmented skin lesions, melanoma risk assessment, interobserver agreement, false reassurance, melanoma detection
- MeSH terms
- Smartphone, Adult, Aged, Aged, 80 and over, Dermatology, Female, Humans, Image Interpretation, Computer-Assisted, Male, Melanoma, Middle Aged, Photography, Prospective Studies, Sensitivity and Specificity, Skin Neoplasms, Observer Variation, Risk Assessment, Mobile Applications
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