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

Freeman, Karoline and Dinnes, Jacqueline and Chuchu, Naomi and Takwoingi, Yemisi and Bayliss, Sue E and Matin, Rubeta N and Jain, Abhilash and Walter, Fiona M and Williams, Hywel C and Deeks, Jonathan J (2020) Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ (Clinical research ed.), 368. m127. ISSN 1756-1833. This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs

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Abstract

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.

Item Type: Article
Additional Information: This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs
Subjects: WR Skin. Dermatology
Z Library science
Divisions: Clinical Support
Related URLs:
Depositing User: Mrs Yolande Brookes
Date Deposited: 20 Feb 2020 14:52
Last Modified: 20 Feb 2020 14:52
URI: http://www.repository.uhblibrary.co.uk/id/eprint/2863

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