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dc.contributor.authorFreeman, Karoline
dc.contributor.authorDinnes, Jacqueline
dc.contributor.authorChuchu, Naomi
dc.contributor.authorTakwoingi, Yemisi
dc.contributor.authorBayliss, Sue E
dc.contributor.authorMatin, Rubeta N
dc.contributor.authorJain, Abhilash
dc.contributor.authorWalter, Fiona M
dc.contributor.authorWilliams, Hywel C
dc.contributor.authorDeeks, Jonathan J
dc.date.accessioned2020-02-14T16:18:12Z
dc.date.available2020-02-14T16:18:12Z
dc.date.issued2020-02-10
dc.identifier.otherfrek052868
dc.identifier.other32041693
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/302203
dc.description.abstractAbstract: 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.
dc.languageen
dc.publisherBritish Medical Journal Publishing Group
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectResearch
dc.titleAlgorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies
dc.typeArticle
dc.date.updated2020-02-14T16:18:11Z
prism.publicationNameBMJ
prism.volume368
dc.identifier.doi10.17863/CAM.49278
dcterms.dateAccepted2019-12-17
rioxxterms.versionofrecord10.1136/bmj.m127
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidFreeman, Karoline [0000-0002-9963-2918]
dc.contributor.orcidDinnes, Jacqueline [0000-0003-1343-7335]
dc.contributor.orcidDeeks, Jonathan J [0000-0002-8850-1971]
dc.identifier.eissn1756-1833


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)