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. 2018 Dec 4;12(12):CD013192.
doi: 10.1002/14651858.CD013192.

Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma

Naomi Chuchu  1 , Yemisi Takwoingi, Jacqueline Dinnes, Rubeta N Matin, Oliver Bassett, Jacqueline F Moreau, Susan E Bayliss, Clare Davenport, Kathie Godfrey, Susan O'Connell, Abhilash Jain, Fiona M Walter, Jonathan J Deeks, Hywel C Williams; Cochrane Skin Cancer Diagnostic Test Accuracy Group
Affiliations

Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma

Naomi Chuchu et al. Cochrane Database Syst Rev. .

Abstract

Background: Melanoma accounts for a small proportion of all skin cancer cases but is responsible for most skin cancer-related deaths. Early detection and treatment can improve survival. Smartphone applications are readily accessible and potentially offer an instant risk assessment of the likelihood of malignancy so that the right people seek further medical attention from a clinician for more detailed assessment of the lesion. There is, however, a risk that melanomas will be missed and treatment delayed if the application reassures the user that their lesion is low risk.

Objectives: To assess the diagnostic accuracy of smartphone applications to rule out cutaneous invasive melanoma and atypical intraepidermal melanocytic variants in adults with concerns about suspicious skin lesions.

Search methods: We undertook a comprehensive search of the following databases from inception to August 2016: Cochrane Central Register of Controlled Trials; MEDLINE; Embase; CINAHL; CPCI; Zetoc; Science Citation Index; US National Institutes of Health Ongoing Trials Register; NIHR Clinical Research Network Portfolio Database; and the World Health Organization International Clinical Trials Registry Platform. We studied reference lists and published systematic review articles.

Selection criteria: Studies of any design evaluating smartphone applications intended for use by individuals in a community setting who have lesions that might be suspicious for melanoma or atypical intraepidermal melanocytic variants versus a reference standard of histological confirmation or clinical follow-up and expert opinion.

Data collection and analysis: Two review authors independently extracted all data using a standardised data extraction and quality assessment form (based on QUADAS-2). Due to scarcity of data and poor quality of studies, we did not perform a meta-analysis for this review. For illustrative purposes, we plotted estimates of sensitivity and specificity on coupled forest plots for each application under consideration.

Main results: This review reports on two cohorts of lesions published in two studies. Both studies were at high risk of bias from selective participant recruitment and high rates of non-evaluable images. Concerns about applicability of findings were high due to inclusion only of lesions already selected for excision in a dermatology clinic setting, and image acquisition by clinicians rather than by smartphone app users.We report data for five mobile phone applications and 332 suspicious skin lesions with 86 melanomas across the two studies. Across the four artificial intelligence-based applications that classified lesion images (photographs) as melanomas (one application) or as high risk or 'problematic' lesions (three applications) using a pre-programmed algorithm, sensitivities ranged from 7% (95% CI 2% to 16%) to 73% (95% CI 52% to 88%) and specificities from 37% (95% CI 29% to 46%) to 94% (95% CI 87% to 97%). The single application using store-and-forward review of lesion images by a dermatologist had a sensitivity of 98% (95% CI 90% to 100%) and specificity of 30% (95% CI 22% to 40%).The number of test failures (lesion images analysed by the applications but classed as 'unevaluable' and excluded by the study authors) ranged from 3 to 31 (or 2% to 18% of lesions analysed). The store-and-forward application had one of the highest rates of test failure (15%). At least one melanoma was classed as unevaluable in three of the four application evaluations.

Authors' conclusions: Smartphone applications using artificial intelligence-based analysis have not yet demonstrated sufficient promise in terms of accuracy, and they are associated with a high likelihood of missing melanomas. Applications based on store-and-forward images could have a potential role in the timely presentation of people with potentially malignant lesions by facilitating active self-management health practices and early engagement of those with suspicious skin lesions; however, they may incur a significant increase in resource and workload. Given the paucity of evidence and low methodological quality of existing studies, it is not possible to draw any implications for practice. Nevertheless, this is a rapidly advancing field, and new and better applications with robust reporting of studies could change these conclusions substantially.

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Conflict of interest statement

Naomi Chuchu: none known. Yemisi Takwoingi: none known. Jac Dinnes: none known. Rubeta N Matin: my institution received a grant for a Barco NV commercially sponsored study to evaluate digital dermoscopy in the skin cancer clinic. My institution also received Oxfordshire Health Services Research Charitable Funds for carrying out a study of feasibility of using the Skin Cancer Quality of Life Impact Tool (SCQOLIT) in non melanoma skin cancer. I have received royalties for the Oxford Handbook of Medical Dermatology (Oxford University Press) and payment from the UK Photopheresis Society for a lecture on cutaneous graft versus host disease (October 2017). I have no conflicts of interest to declare that directly relate to the publication of this work. Oliver Bassett: none known. Jacqueline F Moreau: I helped draft the paper and performed data analysis for an included study (Wolf 2013). Susan E Bayliss: none known. Clare Davenport: none known. Kathie Godfrey: none known. Susan O'Connell: none known. Abhilash Jain: none known. Fiona M Walter: none known. Jonathan J Deeks: none known. Hywel C Williams: I am director of the NIHR health technology assessment (HTA) Programme. HTA is part of the NIHR which also supports the NIHR systematic reviews programme from which this work is funded.

Clinical referee David de Berker: I am Principal investigator for a single site in a multicentre study for assessment of images in pigmented lesions. The sponsor is Skin Analytics. I receive no payment for this from Skin Analytics, although they pay the hospital for participation of our site.

Figures

1
1
Sample photographs of superficial spreading melanoma (left) and nodular melanoma (right). Copyright © 2010 Dr Rubeta Matin: reproduced with permission.
2
2
Example pathway for an individual using a smartphone application to examine a suspicious mole in resource settings with smartphones
3
3
Current clinical pathway for people with skin lesions.
4
4
PRISMA flow diagram.
5
5
Risk of bias and applicability concerns summary: review authors' judgements about each domain for each included study
6
6
Risk of bias and applicability concerns graph: review authors' judgements about each domain presented as percentages across included studies
7
7
Forest plot of tests: showing sensitivity and specificity of all the applications for the detection of cutaneous melanoma and atypical intraepidermal variants 1 App 1[problematic vs okay], 2 App 2 [mel vs not mel], 3 App 3(a) [high risk vs medium+low risk], 4 App 3(b) [high+medium risk vs low risk], 5 App 4 (remote diagnosis) [atypical vs typical], 6 SkinVision [high risk vs medium/low risk].
8
8
Forest plot of tests: SkinVision automated diagnosis compared to face to face clinical diagnosis by a dermatologist 6 SkinVision [high risk vs medium/low risk], 7 Face‐to‐face clinical diagnosis [high risk vs medium/low risk].
1
1. Test
App 1 [decision: problematic vs okay].
2
2. Test
App 2 [decision: melanoma vs not melanoma].
3
3. Test
App 3(a) [decision: high risk vs medium/low risk].
4
4. Test
App 3(b) [decision: high/medium risk vs low risk].
5
5. Test
App 4 (remote diagnosis) [decision: atypical vs typical].
6
6. Test
SkinVision [decision: high risk vs medium/low risk].
7
7. Test
Face‐to‐face dermatologist diagnosis [decision: melanoma vs not melanoma].
See this image and copyright information in PMC

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