Validation of a Market-Approved Artificial Intelligence Mobile Health App for Skin Cancer Screening: A Prospective Multicenter Diagnostic Accuracy Study

Tobias Sangers*, Suzan Reeder, Sophie Van Der Vet, Sharan Jhingoer, Antien Mooyaart, Daniel M. Siegel, Tamar Nijsten, Marlies Wakkee

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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Abstract

Background: Mobile health (mHealth) consumer applications (apps) have been integrated with deep learning for skin cancer risk assessments. However, prospective validation of these apps is lacking. Objectives: To identify the diagnostic accuracy of an app integrated with a convolutional neural network for the detection of premalignant and malignant skin lesions. Methods: We performed a prospective multicenter diagnostic accuracy study of a CE-marked mHealth app from January 1 until August 31, 2020, among adult patients with at least one suspicious skin lesion. Skin lesions were assessed by the app on an iOS or Android device after clinical diagnosis and before obtaining histopathology. The app outcome was compared to the histopathological diagnosis, or if not available, the clinical diagnosis by a dermatologist. The primary outcome was the sensitivity and specificity of the app to detect premalignant and malignant skin lesions. Subgroup analyses were conducted for different smartphone types, the lesion's origin, indication for dermatological consultation, and lesion location. Results: In total, 785 lesions, including 418 suspicious and 367 benign control lesions, among 372 patients (50.8% women) with a median age of 71 years were included. The app performed at an overall 86.9% (95% CI 82.3-90.7) sensitivity and 70.4% (95% CI 66.2-74.3) specificity. The sensitivity was significantly higher on the iOS device compared to the Android device (91.0 vs. 83.0%; p = 0.02). Specificity calculated on benign control lesions was significantly higher than suspicious skin lesions (80.1 vs. 45.5%; p < 0.001). Sensitivity was higher in skin fold areas compared to smooth skin areas (92.9 vs. 84.2%; p = 0.01), while the specificity was higher for lesions in smooth skin areas (72.0 vs. 56.6%; p = 0.02). Conclusion: The diagnostic accuracy of the mHealth app is far from perfect, but is potentially promising to empower patients to self-assess skin lesions before consulting a health care professional. An additional prospective validation study, particularly for suspicious pigmented skin lesions, is warranted. Furthermore, studies investigating mHealth implementation in the lay population are needed to demonstrate the impact on health care systems.

Original languageEnglish
Pages (from-to)649-656
Number of pages8
JournalDermatology
Volume238
Issue number4
DOIs
Publication statusPublished - 1 Jul 2022

Bibliographical note

Funding Information:
This study was initiated by the Erasmus MC Cancer Insitute and was funded with an unrestricted research grant from SkinVision (Amsterdam, the Netherlands). SkinVision was not involved in the design of the study, data collection, data analysis, data interpretation, or writing of the manuscript. In addition, SkinVision was not involved in the decision to submit this work for publication.

Publisher Copyright: © 2022 The Author(s).

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