At SkinVision, we are committed to scientific integrity, real-world validation, and clinical relevance. As one of the few skin cancer detection apps that has consistently invested in independent clinical research, we collaborate with leading academic and medical institutions to validate not only our algorithm’s accuracy, but also its practical implementation and user experience.
CLINICAL PERFORMANCE OVER TIME
The accuracy of the SkinVision app has been evaluated in several peer-reviewed clinical studies1-5and is supported by a large dataset of histopathology-confirmed global customer results6,7 including both malignant and non-malignant skin lesions.
The largest validation study, conducted in partnership with Erasmus Medical Center (NL), was published in Dermatology (Sangers et al., 2022)1.
The performance of SkinVision’s algorithm, particularly the latest version integrated with a convolutional neural network (CNN), has demonstrated high sensitivity in detecting premalignant and malignant lesions.
For the intended user group – individuals at risk of developing skin cancer – the sensitivity of SkinVision app for correctly identifying (pre-)malignancy was 87% (SkinVision app version 6.0) in a clinical multicentre study, with a high sensitivity for malignant melanoma (92.1%) and squamous cell carcinoma (98.6%) and a specificity of 80.1%.1,8
Notably, the algorithm’s performance has been confirmed in real-world conditions through extensive internal bench testing and independently validated in a population-based insurance study.7,9 Results based on our internal bench test database (December 2024 release), consisting of both customer-validated data and data from previously conducted studies, showed overall a sensitivity of more than 90%. Specificity, calculated on a randomly selected set of 5,000 images from our production database (excluded from AI training), reached 89%7.
More information on our scientific evidence and our AI algorithm is expected to be published on our website soon.
Here is an overview of the articles we have collaborated on with several research centres (the text refers also to some internal files from SkinVision which are listed between the list of articles):
- Sangers T, Reeder S, van der Vet S, et al. Validation of a Market-Approved Artificial Intelligence Mobile Health App for Skin Cancer Screening: A Prospective Multicenter Diagnostic Accuracy Study. Dermatology. 2022;238(4):649-656.
https://pubmed.ncbi.nlm.nih.gov/35124665 - Smak Gregoor AM, Sangers TE, Eekhof JAH, et al. Artificial intelligence in mobile health for skin cancer diagnostics at home (AIM HIGH): a pilot feasibility study. eClinicalMedicine. 2023;60.
https://pubmed.ncbi.nlm.nih.gov/37261324/ - Udrea A, Mitra GD, Costea D, et al. Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms. J Eur Acad Dermatol Venereol. 2020;34(3):648-655.
https://pubmed.ncbi.nlm.nih.gov/31494983 - Thissen M, Udrea A, Hacking M, von Braunmuehl T, Ruzicka T. mHealth App for Risk Assessment of Pigmented and Nonpigmented Skin Lesions-A Study on Sensitivity and Specificity in Detecting Malignancy. Telemed J E Health. 2017;23(12):948-954.
https://pubmed.ncbi.nlm.nih.gov/28562195/ - Maier T, Kulichova D, Schotten K, et al. Accuracy of a smartphone application using fractal image analysis of pigmented moles compared to clinical diagnosis and histological result. J Eur Acad Dermatol Venereol. 2015;29(4):663-667.
https://pubmed.ncbi.nlm.nih.gov/25087492/ - SkinVision ML Algorithm Technical Overview (Data on File)
- SkinVision app bench test report (Data on File)
- SV1151_-_Clinical_Evaluation_Report_(CER)_-_v3.1 (Data on File).
- Smak Gregoor AM, Sangers TE, Bakker LJ, et al. An artificial intelligence-based app for skin cancer detection evaluated in a population based setting. NPJ Digit Med. 2023;6(1):90.
https://pubmed.ncbi.nlm.nih.gov/37210466/ - Howe S, Smak Gregoor A, Uyl-de Groot C, Wakkee M, Nijsten T, Wehrens R. Embedding artificial intelligence in healthcare: An ethnographic exploration of an AI-based mHealth app through the lens of legitimacy. Digit Health. 2024;10:20552076241292390.
https://pubmed.ncbi.nlm.nih.gov/39525560/ - Sangers TE, Wakkee M, Moolenburgh FJ, Nijsten T, Lugtenberg M. Towards successful implementation of artificial intelligence in skin cancer care: a qualitative study exploring the views of dermatologists and general practitioners. Arch Dermatol Res. 2023;315(5):1187-1195.
https://pubmed.ncbi.nlm.nih.gov/36477587/ - Sangers TE, Wakkee M, Kramer-Noels EC, Nijsten T, Lugtenberg M. Views on mobile health apps for skin cancer screening in the general population: an in-depth qualitative exploration of perceived barriers and facilitators. Br J Dermatol. 2021;185(5):961-969.
https://pubmed.ncbi.nlm.nih.gov/33959945/ - Sangers TE, Nijsten T, Wakkee M. Mobile health skin cancer risk assessment campaign using artificial intelligence on a population-wide scale: a retrospective cohort analysis. J Eur Acad Dermatol Venereol. 2021;35(11):e772-e774.
https://pubmed.ncbi.nlm.nih.gov/34077573/ - de Carvalho TM, Noels E, Wakkee M, Udrea A, Nijsten T. Development of Smartphone Apps for Skin Cancer Risk Assessment: Progress and Promise. JMIR Dermatol. 2019;2(1):e13
https://www.sciencedirect.com/org/science/article/pii/S2562095919000023