Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies

Dinnes, Jacqueline; Freeman, Karoline; Chuchu, Naomi; Takwoingi, Yemisi; Bayliss, Sue E; Matin, Rubeta N; Jain, Abhilash; Walter, Fiona M; Williams, Hywel C.; Deeks, Jonathan J

Abstract

© Published by the BMJ Publishing Group Limited. Objective To examine the validity and findings of studies that examine the accuracy of algorithm based smartphone applications ("apps") to assess risk of skin cancer in suspicious skin lesions. Design Systematic review of diagnostic accuracy studies. Data sources Cochrane Central Register of Controlled Trials, MEDLINE, Embase, CINAHL, CPCI, Zetoc, Science Citation Index, and online trial registers (from database inception to 10 April 2019). Eligibility criteria for selecting studies Studies of any design that evaluated algorithm based smartphone apps to assess images of skin lesions suspicious for skin cancer. Reference standards included histological diagnosis or follow-up, and expert recommendation for further investigation or intervention. Two authors independently extracted data and assessed validity using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2 tool). Estimates of sensitivity and specificity were reported for each app. Results Nine studies that evaluated six different identifiable smartphone apps were included. Six verified results by using histology or follow-up (n=725 lesions), and three verified results by using expert recommendations (n=407 lesions). Studies were small and of poor methodological quality, with selective recruitment, high rates of unevaluable images, and differential verification. Lesion selection and image acquisition were performed by clinicians rather than smartphone users. Two CE (Conformit Europenne) marked apps are available for download. SkinScan was evaluated in a single study (n=15, five melanomas) with 0% sensitivity and 100% specificity for the detection of melanoma. SkinVision was evaluated in two studies (n=252, 61 malignant or premalignant lesions) and achieved a sensitivity of 80% (95% confidence interval 63% to 92%) and a specificity of 78% (67% to 87%) for the detection of malignant or premalignant lesions. Accuracy of the SkinVision app verified against expert recommendations was poor (three studies). Conclusions Current algorithm based smartphone apps cannot be relied on to detect all cases of melanoma or other skin cancers. Test performance is likely to be poorer than reported here when used in clinically relevant populations and by the intended users of the apps. The current regulatory process for awarding the CE marking for algorithm based apps does not provide adequate protection to the public. Systematic review registration PROSPERO CRD42016033595.

Citation

Dinnes, J., Freeman, K., Chuchu, N., Takwoingi, Y., Bayliss, S. E., Matin, R. N., Jain, A., Walter, F. M., Williams, H. C., & Deeks, J. J. (2020). Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ, 368, Article m127. https://doi.org/10.1136/bmj.m127

• Journal Article Type: Review
• Acceptance Date: Dec 17, 2019
• Online Publication Date: Feb 10, 2020
• Publication Date: Feb 10, 2020
• Deposit Date: Feb 12, 2020
• Publicly Available Date: Feb 12, 2020
• Journal: The BMJ
• Electronic ISSN: 1756-1833
• Publisher: BMJ Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 368
• Article Number: m127
• DOI: https://doi.org/10.1136/bmj.m127
• Public URL: https://nottingham-repository.worktribe.com/output/3950182
• Publisher URL: https://www.bmj.com/content/368/bmj.m127