Conflicting results between the analysis of skin lesions using a mobile-phone application and a dermatologist's clinical diagnosis: a pilot study
Abstract
Dear Editor, The use of mobile‐phone health applications (apps) has become very popular in the general population. The SkinVision app (SVA, http://www.skinvision.com) is the first skin cancer app with a European CE certification, indicating that the product has met EU health, safety and environmental requirements. SkinVision lets the consumer ‘take photos of moles and skin conditions to analyse for skin cancer risk’. Artificial intelligence is used to interpret the modified image automatically. Based on this analysis, it evaluates lesions as low (green), medium (yellow) or high risk (red). The purpose of our pilot study was to investigate the degree of agreement between the ratings of the SVA and a dermatologist in skin lesions selected by patients. We included new patients referred by the general practitioner to our pigmented lesion clinic who met an automated triage system for pigmented lesions. The written, informed consent of all participants was obtained. The study was approved by the local medical ethical committee. The patients were asked to point out a maximum of two lesions that concerned them. The lesions were imaged by the researcher with a Samsung S4 mini smartphone with an 8·0‐megapixel autofocus camera (Samsung, Suwon, South Korea) and the photos were analysed by the SVA with an instant rating of green, yellow or red. Subsequently, the patients were seen by a dermatologist who specialized in skin cancer, who also gave a green, yellow or red rating and documented the diagnosis. The diagnosis was followed by histopathology if the dermatologist rated the lesion as red. Data were collected in Excel, checked for outliers and re‐evaluated for correctness. Descriptive statistics and measure of agreement (weighted kappa) were performed in SPSS 23 (IBM, Armonk, NY, U.S.A.). In total, 151 lesions were evaluated, 53% were from women with a mean age of 42·5 years (range 19–81). The interobserver agreement between the ratings of the app and the dermatologist was very low (weighted kappa = 0·073), with a 95% confidence interval of −0·55–0·205 and a P‐value of 0·121 (Table 1). This interobserver agreement is compatible with mere chance. Overall, 44% of the lesions with a red rating and 60% of those with a yellow rating analysed by the SVA were diagnosed as a common naevus by the dermatologist. Pathology tests were made in 9%, 14% and 19% of the lesions given a green, yellow and red rating by the app, respectively. One patient refused excision for a lesion that was diagnosed as a basal cell carcinoma (BCC) by the dermatologist. The app rated two of the three BCCs that were confirmed by histopathology as moderate risk and four of the seven histologically proven dysplastic naevi as low risk. The dermatologist rated all three BCCs as high risk, and one of the seven dysplastic naevi was rated as low risk. Results of ratings using the SkinVision app (SVA) vs. a dermatologist's diagnosis, showing poor interobserver agreement Percentage of SVA rating that got a green*/yellow**/red*** rating by the dermatologist. Results of ratings using the SkinVision app (SVA) vs. a dermatologist's diagnosis, showing poor interobserver agreement Percentage of SVA rating that got a green*/yellow**/red*** rating by the dermatologist. Maier et al. found a sensitivity of 73% and a specificity of 83% for melanoma using the SVA compared with histological results.1 We believe that the main reason for the difference between our negative and their positive results is the method of lesion selection. They selected melanocytic lesions that the dermatologist found so suspicious that they needed excision and most benign melanocytic lesions like banal naevi and benign nonmelanocytic pigmented lesions had already been excluded from further examination.1 As the SVA is intended to analyse ‘moles and skin conditions … for skin cancer risk’, the general population is likely to use the app for all lesions they find suspicious, as it is difficult for a lay person to distinguish between potential skin cancer and a banal naevus, seborrhoeic keratosis, fibroma, angioma and other benign pigmented lesions. Therefore, we believe that the effect seen in our study would be even stronger in the general population. The SVA missed two of three BCCs. This was possibly because the BCCs were only slightly pigmented. As patients will most probably use this app not only on strongly pigmented lesions, less‐pigmented types of skin cancer like amelanotic melanoma and BCC may be missed. Our results are in line with the study conducted by Wolf et al., who warned about the potential harm these apps could bring about.2 A recent review article about practical apps for diagnosing noninvasive melanoma found that many apps were not reliable.3 Our study has certain limitations. In cases of a benign clinical diagnosis, lesions were not excised for ethical reasons. Another limitation is the lack of melanoma cases due to the limited number of patients in the series. However, we expec
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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 [29]
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 [29]
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 [29]
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 [29]
When only high risk results were considered as test positive, between 25% (95% CI 3% to 65%) and 67% (30% to 93%) of lesions that the dermatologist considered to require further investigation were picked up by the SkinVision app, with specificities of 90% and 61%, respectively.
“When only high risk results were considered as test positive, between 25% (95% confidence interval 3% to 65%)29 and 67% (30% to 93%)30 of lesions that the dermatologist considered to require further investigation were picked up by the app (with specificities of 90% and 61%, respectively).”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [29]
Studies included in this review did not specify algorithm versions used for risk assessment and we do not know whether three studies of the SkinVision app considered the same app version or different versions.
“Studies included in this review did not specify algorithm versions used for risk assessment and we do not know whether three studies of the SkinVision app29 30 33 considered the same app version or different versions.”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [29]
The review included an additional two studies that were not included in previous reviews.
“We included an additional two studies29 30 that were not included in previous reviews.”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [29]
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
Maier, T., Kulichova, D., Schotten, K., Astrid, R., Ruzicka, T., Berking, C., & Udrea, A. (2015) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.12648 · Cited by 103 · see citing works
Practical application of new technologies for melanoma diagnosis
March, J., Hand, M., & Grossman, D. (2015) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2015.02.1138 · Cited by 72 · see citing works
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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
Akbar, S., Coiera, E. W., & Magrabi, F. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz175 · Cited by 246 · see citing works
Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check
Brancaccio, G., Balato, A., Malvehy, J., Puig, S., Argenziano, G., & Kittler, H. (2023) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2023.10.004 · Cited by 137 · see citing works
Beyond 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
Jahn, A. S., Navarini, A. A., Cerminara, S. E., Kostner, L., Huber, S. M., Kunz, M., Maul, J., Dummer, R., Sommer, S., Neuner, A. D., et al. (2022) · Cancers · doi:10.3390/cancers14153829 · Cited by 62 · see citing works
Photoaging Mobile Apps as a Novel Opportunity for Melanoma Prevention: Pilot Study
Brinker, T. J., Schadendorf, D., Klode, J., Cosgarea, I., Rösch, A., Jansen, P., Stoffels, I., & Izar, B. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.8231 · Cited by 34 · see citing works
Brinker, T. J., Heckl, M., Gatzka, M., Heppt, M. V., Rodrigues, H. R., Schneider, S. O., Sondermann, W., Silva, C. D. A. E., Kirchberger, M. C., Klode, J., et al. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.9794 · Cited by 35 · see citing works
Zaar, O., Larson, A., Polesie, S., Saleh, K., Tarstedt, M., Olives, A., Suárez, A. L., Gillstedt, M., & Neittaanmäki, N. (2020) · Acta Dermato Venereologica · doi:10.2340/00015555-3624 · Cited by 30 · see citing works
Evaluating a mobile application for improving clinical laboratory test ordering and diagnosis
Meyer, A. N. D., Thompson, P. J., Khanna, A., Desai, S. P., Mathews, B. K., Yousef, E. A., Kusnoor, A. V., & Singh, H. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy026 · Cited by 20 · see citing works
Ethical implications of artificial intelligence in skin cancer diagnostics: use-case analyses
Shah, S. F. H., Arecco, D., Draper, H., Tiribelli, S., Harriss, E., & Matin, R. (2024) · British Journal of Dermatology · doi:10.1093/bjd/ljae434 · Cited by 19 · see citing works
Chung, Y., Sande, A. A., de Roos, K.-P., Bekkenk, M. W., de Haas, E. R. M., Kelleners-Smeets, N. W. J., & Kukutsch, N. A. (2019) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.15873 · Cited by 14 · see citing works
Self-citationTeledermatology vs. Face-to-Face Dermatology for the Diagnosis of Melanoma: A Systematic Review
Rico, M. L., Val, M. G., Rojas, M. D. S., Leboráns, L. M., Otero, C. R., & Flórez, Á. (2025) · Cancers · doi:10.3390/cancers17172836 · Cited by 9 · see citing works
- Authors
- Nabil, R., Bergman, W., & Kukutsch, N. A.
- Affiliations
- Leiden University Medical Center (NL)
- Corresponding author
- Kukutsch, N.A.
- Venue
- British Journal of Dermatology
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-07-04
- Volume
- 177
- Issue
- 2
- Pages
- 583-584
- ISSN
- 0007-0963 (ISSN-L)
- OpenAlex
- W2593368545
- Cited by
- 12
- References
- 3
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 1 · 2020: 2 · 2019: 2 · 2018: 3 · 2017: 1
- FWCI
- 0.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- British Journal of Dermatology (publishedVersion)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- mobile application, pigmented skin lesions, artificial intelligence, dermatologist diagnosis, interobserver agreement, melanoma, automated triage, smartphone photography, patient self-assessment, histopathological correlation, skin cancer screening
- MeSH terms
- Dermatologists, Adult, Aged, Aged, 80 and over, Clinical Competence, Female, Humans, Male, Middle Aged, Pilot Projects, Skin Diseases, Young Adult, Mobile Applications
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