mHealth App for Risk Assessment of Pigmented and Nonpigmented Skin Lesions—A Study on Sensitivity and Specificity in Detecting Malignancy
Abstract
BACKGROUND: With the advent of smartphone devices, an increasing number of mHealth applications that target melanoma identification have been developed, but none addresses the general context of melanoma and nonmelanoma skin cancer identification. INTRODUCTION: In this study a smartphone application using fractal and classical image analysis for the risk assessment of skin lesions is systematically evaluated to determine its sensitivity and specificity in the diagnosis of melanoma and nonmelanoma skin cancer along with actinic keratosis and Bowen's disease. MATERIALS AND METHODS: In the Department of Dermatology, Catharina Hospital Eindhoven, The Netherlands, 341 melanocytic and nonmelanocytic lesions were imaged using SkinVision app; 239 underwent histopathological examination, while the rest of 102 lesions were clinically diagnosed as clearly benign and not removed. The algorithm has been calibrated using the images of the first 233 lesions. The calibrated version of the algorithm was used in a subset of 108 lesions, and the obtained results were compared with the medical findings. RESULTS: On the 108 cases used for evaluation the algorithm scored 80% sensitivity and 78% specificity in detecting (pre)malignant conditions. DISCUSSION: Although less accurate than the dermatologist's clinical eye, the app may offer support to other professionals who are less familiar with differentiating between benign and malignant lesions. CONCLUSION: An mHealth application for the risk assessment of skin lesions was evaluated. It adds value to diagnosis tools of its type by taking into consideration pigmented and nonpigmented lesions all together and detecting signs of malignancy with high sensitivity.
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Certificates do not guarantee good diagnostic performance.
“Certificates do not guarantee good diagnostic performance [54].”
From Use of Smartphones for Early Detection of Melanoma: Systematic Review (2018) · cites this paper as [54]
Up to 70% of photographs were of poor quality in a study performed in 2007, whereas all photographs were interpretable in 2016.
“it is notable that up to 70% of the photographs were of poor quality in a study performed in 2007 [41], whereas all photographs were interpretable in 2016 [54].”
From Use of Smartphones for Early Detection of Melanoma: Systematic Review (2018) · cites this paper as [54]
When participant responses to in-app questions about lesion characteristics and symptoms were included, sensitivity increased further to 80% (63% to 92%) and specificity to 78% (67% to 87%).
“When participant responses to in-app questions about lesion characteristics and symptoms were included, sensitivity increased further to 80% (63% to 92%) and specificity to 78% (67% to 87%).33”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [33]
The original SkinVision app only correctly picked up one of three melanomas as high risk.
“The app only correctly picked up one of three melanomas as high risk.33”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [33]
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 [33]
A later revision of the SkinVision app to allow for non-pigmented lesions led to a 15 percentage point increase in sensitivity for detection of melanoma when applied to the original pigmented lesion dataset (88%, 95% CI 70% to 98%); specificity dropped by 3 percentage points (79%, 70% to 86%).
“A later revision of the app to allow for non-pigmented lesions led to a 15 percentage point increase in sensitivity for the detection of melanoma when applied to the original pigmented lesion dataset (88%, 95% confidence interval 70% to 98%); however, specificity dropped by 3 percentage points (79%, 70% to 86%).33”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [33]
When interpretation of SkinVision was varied to consider high and moderate results as test positive, sensitivity increased (between 17 and 57 percentage points) but specificity fell by 11-45 percentage points.
“When the interpretation of SkinVision was varied to consider high and moderate results from the app as test positive, sensitivity increased (between 17 and 57 percentage points) but at a considerable cost to specificity (falling by 11-45 percentage points).33”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [33]
When applied to pigmented and non-pigmented lesions (n=108, 35 malignant or premalignant lesions), the original SkinVision app had a sensitivity of only 26% (12% to 43%).
“however, sensitivity was only 26% (12% to 43%) when applied to pigmented and non-pigmented lesions (n=108, 35 malignant or premalignant lesions).33”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [33]
In a hypothetical population of 1000 adults in which 3% have a melanoma, using a sensitivity of 88% and a specificity of 79% as observed by Thissen and colleagues, four of 30 melanomas would not be picked up as high risk, and more than 200 people would be given false positive results.
“Therefore, in a hypothetical population of 1000 adults in which 3% have a melanoma, four of 30 melanomas would not be picked up as high risk, and more than 200 people would be given false positive results (by using a sensitivity of 88% and a specificity of 79%, as observed by Thissen and colleagues33).”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [33]
Only one study reported analysing all images to more closely mimic a real world setting.
“Only one study33 reported analysing all images to more closely mimic a real world setting.”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [33]
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 [33]
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 [33]
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- Authors
- Thissen, M., Udrea, A., Hacking, M., von Braunmuehl, T., & Ruzicka, T.
- Affiliations
- Maastricht University Medical Centre (NL)
- Universitatea Națională de Știință și Tehnologie Politehnica București (RO)
- Catharina Ziekenhuis (NL)
- Department of Dermatology and Allergology, Ludwig-Maximilian University, Munich, Germany.
- Ludwig-Maximilians-Universität München (DE)
- Corresponding author
- Udrea, Andreea
- Venue
- Telemedicine and e-Health
- Publisher
- SAGE Publications
- Published
- 2017-12
- Volume
- 23
- Issue
- 12
- Pages
- 948-954
- ISSN
- 1530-5627 (ISSN-L)
- 1556-3669
- OpenAlex
- W2618204361
- Cited by
- 63
- References
- 9
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 9 · 2023: 6 · 2022: 6 · 2021: 6 · 2020: 11 · 2019: 11 · 2018: 2 · 2017: 5
- FWCI
- 3.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Telemedicine Journal and e-Health (publishedVersion)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion)
- Research Publications (Maastricht University) (submittedVersion)
- 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 health apps, melanoma, non-melanoma skin cancer, actinic keratosis, Bowen's disease, fractal image analysis, smartphone image analysis, sensitivity and specificity, melanocytic lesions
- MeSH terms
- Smartphone, Algorithms, Humans, Image Processing, Computer-Assisted, Melanoma, Netherlands, Sensitivity and Specificity, Skin Diseases, Skin Neoplasms, Risk Assessment, 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 ›
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