Accuracy of a smartphone application using fractal image analysis of pigmented moles compared to clinical diagnosis and histological result
Abstract
Background: Lately, various smartphone applications have been introduced as diagnostic self‐monitoring tools in the evaluation of pigmented moles, but most of these techniques have not been evaluated systematically. Objectives: The purpose of this study was to evaluate prospectively the sensitivity and specificity of a recently developed smartphone application using fractal image analysis for the risk evaluation algorithm in the diagnosis of malignant melanoma compared to clinical diagnosis and histopathological result. Methods: Consecutive patients with melanocytic lesions were recruited and clinical and dermoscopical diagnosis was documented by two dermatologists independently. Imaging and analysis with the smartphone application was performed prior to excision of lesions. The findings were compared to the histological results as gold standard. Results: Of 195 included lesions histopathological analysis revealed 40 melanomas, 42 dysplastic nevi and 113 benign nevi. The sensitivity of the diagnosis melanoma by fractal image analysis using smartphone images was 73%, the specificity was 83% compared to a sensitivity of 88% and specificity of 97% regarding the clinical diagnosis by the dermatologists. Conclusion: The smartphone application using fractal analysis might be a promising tool in the pre‐evaluation of pigmented moles by laypersons, while it is to date inferior to the diagnostic evaluation by a dermatologist.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Corresponding specificities for the original SkinVision app were 83% and 75%.
“Corresponding specificities were 83% and 75% (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 [28]
When only high risk results were considered as test positive, the original SkinVision app demonstrated a sensitivity of 73% (95% CI 52% to 88%) in a study of pigmented lesions (n=144, 26 melanomas).
“When only high risk results were considered as test positive, the original SkinVision app demonstrated a sensitivity of 73% (95% confidence interval 52% to 88%) in a study of pigmented lesions (n=144, 26 melanomas)28”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [28]
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 [28]
Studies were based on images of suspicious skin lesions that had undergone excision or biopsy, and were further selected to only include conditions identified by the apps; for example, they excluded lesions with clinical and histological features similar to melanoma, or restricted inclusion to melanocytic lesions which are more likely to be recognised by apps.
“We found studies were based on images of suspicious skin lesions that had undergone excision or biopsy, and were further selected to only include conditions identified by the apps; for example, they excluded lesions with clinical and histological features similar to melanoma,34 or restricted inclusion to melanocytic lesions which are more likely to be recognised by apps.23 28 29 31 32”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [28]
Two thirds (195/285) of the malignant or premalignant lesions in the SkinVision study originated from previous studies, with images taken by experts on patients referred to a clinic.
“Two thirds (195/285) of the malignant or premalignant lesions originated from previous studies (including 40 melanomas from one study28 and eight melanomas plus 147 other malignant or premalignant lesions from another study33), with images taken by experts on patients referred to a clinic.”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [28]
Cites
Diagnostic Inaccuracy of Smartphone Applications for Melanoma Detection
Wolf, J. A., Moreau, J. F., Akilov, O., Patton, T., English, J. C., Ho, J., & Ferris, L. K. (2013) · JAMA Dermatology · doi:10.1001/jamadermatol.2013.2382 · Cited by 393 · see citing works
Kroemer, S., Frühauf, J., Campbell, T. M., Massone, C., Schwantzer, G., Soyer, H. P., & Hofmann-Wellenhof, R. (2011) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2011.10208.x · Cited by 202 · see citing works
Fractals in Biology and Medicine
Losa, G. A., Merlini, D., Nonnenmacher, T. F., & Weibel, E. R. (2005) · Birkhäuser Basel eBooks · doi:10.1007/3-7643-7412-8 · Cited by 159 · see citing works
Scale-space signatures for the detection of clustered microcalcifications in digital mammograms
Netsch, T., & Peitgen, H. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.802755 · Cited by 139 · see citing works
Melanoma Screening with Cellular Phones
Massone, C., Hofmann-Wellenhof, R., Ahlgrimm-Siess, V., Gabler, G., Ebner, C., & Peter Soyer, H. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000483 · Cited by 136 · see citing works
Medical professional involvement in smartphone ‘apps’ in dermatology: Correspondence
Hamilton, A. D., & Brady, R. R. W. (2012) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2012.10844.x · Cited by 119 · see citing works
Dreiseitl, S., Binder, M., Hable, K., & Kittler, H. (2009) · Melanoma Research · doi:10.1097/cmr.0b013e32832a1e41 · Cited by 72 · see citing works
Caution in melanoma risk analysis with smartphone application technology: Correspondence
Robson, Y., Blackford, S., & Roberts, D. (2012) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2012.11046.x · Cited by 39 · see citing works
Diagnostic inaccuracy of smartphone applications for melanoma detection--reply.
Wolf, J., & Ferris, L. (2013) · JAMA dermatology · doi:10.1001/jamadermatol.2013.4337 · Cited by 9 · see citing works
Nonlinear Deterministic Methods for Computer Aided Diagnosis in Case of Kidney Diseases
Udrea, A., Tanase, M., & Popescu, D. (2012) · International Conference on Informatics in Control, Automation and Robotics (ICINCO) · doi:10.5220/0004039405110516 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Freeman, K., Dinnes, J., Chuchu, N., Takwoingi, Y., Bayliss, S. E., Matin, R. N., Jain, A., Walter, F. M., Williams, H. C., & Deeks, J. J. (2020) · BMJ · doi:10.1136/bmj.m127 · Cited by 260 · see citing works
Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma
Chuchu, N., Takwoingi, Y., Dinnes, J., Matin, R. N., Bassett, O., Moreau, J. F., Bayliss, S. E., Davenport, C., Godfrey, K., O'Connell, S., et al. (2018) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013192 · Cited by 144 · see citing works
Use of Smartphones for Early Detection of Melanoma: Systematic Review
Rat, C., Hild, S., Rault Sérandour, J., Gaultier, A., Quereux, G., Dreno, B., & Nguyen, J.-M. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9392 · Cited by 107 · see citing works
“In these, 12 studies had been conducted in European countries, that is, Great Britain [34,53], Austria [38,41], Sweden [29,35], Ireland [50], Germany [51], Switzerland [40], Greece [44], the Netherlands [54], and France [36]; 7 in United States of America [32,42,45-49]; 5 in Australia [31,33,37,39,52]; and one in Sing…”
InfluentialBeyond Dr. Google: the evidence on consumer-facing digital tools for diagnosis
Millenson, M. L., Baldwin, J. L., Zipperer, L., & Singh, H. (2018) · Diagnosis · doi:10.1515/dx-2018-0009 · Cited by 91 · see citing works
Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms
Udrea, A., Mitra, G. D., Costea, D., Noels, E. C., Wakkee, M., Siegel, D. M., de Carvalho, T. M., & Nijsten, T. E. C. (2020) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.15935 · Cited by 96 · see citing works
Self-citationThissen, M., Udrea, A., Hacking, M., von Braunmuehl, T., & Ruzicka, T. (2017) · Telemedicine and e-Health · doi:10.1089/tmj.2016.0259 · Cited by 63 · see citing works
Self-citationBuechi, R., Faes, L., Bachmann, L. M., Thiel, M. A., Bodmer, N. S., Schmid, M. K., Job, O., & Lienhard, K. R. (2017) · BMJ Open · doi:10.1136/bmjopen-2017-018280 · Cited by 50 · see citing works
Efficacy of smartphone applications in high-risk pigmented lesions
Ngoo, A., Finnane, A., McMeniman, E., Tan, J., Janda, M., & Soyer, H. P. (2018) · Australasian Journal of Dermatology · doi:10.1111/ajd.12599 · Cited by 55 · see citing works
Chen, J. Y., Fernandez, K., Fadadu, R. P., Reddy, R., Kim, M.-O., Tan, J., & Wei, M. L. (2025) · JAMA Dermatology · doi:10.1001/jamadermatol.2024.4382 · Cited by 43 · see citing works
Naraghi, S., Mutsvangwa, T., Goliath, R., Rangaka, M. X., & Douglas, T. S. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.009 · Cited by 22 · see citing works
Nabil, R., Bergman, W., & Kukutsch, N. A. (2017) · British Journal of Dermatology · doi:10.1111/bjd.15443 · Cited by 12 · see citing works
Guido, N., Hagstrom, E. L., Ibler, E., Carneiro, C., Orrell, K. A., Kelm, R. C., Rademaker, A. W., West, D. P., & Nardone, B. (2018) · Journal of Surgical Dermatology · doi:10.18282/jsd.v3.i2.177 · Cited by 5 · see citing works
“The app had a sensitivity of 73% and a specificity of 88%, which was comparable to that observed with clinical diagnostic (sensitivity 88%, specificity 97%)[28].”
Result
- Authors
- Maier, T., Kulichova, D., Schotten, K., Astrid, R., Ruzicka, T., Berking, C., & Udrea, A.
- Affiliations
- Department of Dermatology and Allergology University Hospital of Munich Munich Germany
- Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie (DE)
- Universitatea Națională de Știință și Tehnologie Politehnica București (RO)
- Venue
- Journal of the European Academy of Dermatology and Venereology
- Publisher
- Wiley
- Published
- 2014-08-04
- Volume
- 29
- Issue
- 4
- Pages
- 663-667
- ISSN
- 0926-9959 (ISSN-L)
- 1468-3083
- OpenAlex
- W2045293884
- Semantic Scholar
- e02dc8209fe219cb0769426d401e4f47d66c3e2c
- Cited by
- 103
- References
- 10
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 6 · 2023: 7 · 2022: 7 · 2021: 6 · 2020: 12 · 2019: 13 · 2018: 16 · 2017: 14 · 2016: 4 · 2015: 9 · 2014: 1
- FWCI
- 4.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the European Academy of Dermatology and Venereology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- SkinVision B.V.
- post-doctoral scholarship (POSDRU/159/1.5/S/138963)
- 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
- mobile application, fractal image analysis, melanoma, melanocytic lesions, dermoscopy, self-monitoring, diagnostic accuracy, sensitivity and specificity, dysplastic nevi, benign nevi, histopathological diagnosis
- MeSH terms
- Smartphone, Algorithms, Humans, Image Processing, Computer-Assisted, Melanoma, Nevus, Pigmented, Prospective Studies, Sensitivity and Specificity, Skin Neoplasms, Fractals, Risk Assessment, Dermoscopy, Mobile Applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
