Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies
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 Control...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
BMJ Publishing Group
2020
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| _version_ | 1797105453417627648 |
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| author | Freeman, K Dinnes, J Chuchu, N Takwoingi, Y Bayliss, SE Matin, RN Jain, A Walter, FM Williams, HC Deeks, JJ |
| author_facet | Freeman, K Dinnes, J Chuchu, N Takwoingi, Y Bayliss, SE Matin, RN Jain, A Walter, FM Williams, HC Deeks, JJ |
| author_sort | Freeman, K |
| collection | OXFORD |
| description | 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. No published peer reviewed study
was found evaluating the TeleSkin skinScan app.
SkinVision was evaluated in three studies (n=267, 66
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. |
| first_indexed | 2024-03-07T06:47:47Z |
| format | Journal article |
| id | oxford-uuid:fb704d52-009b-43cb-9d73-9ccbc27086ba |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T06:47:47Z |
| publishDate | 2020 |
| publisher | BMJ Publishing Group |
| record_format | dspace |
| spelling | oxford-uuid:fb704d52-009b-43cb-9d73-9ccbc27086ba2022-03-27T13:14:02ZAlgorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studiesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:fb704d52-009b-43cb-9d73-9ccbc27086baEnglishSymplectic ElementsBMJ Publishing Group2020Freeman, KDinnes, JChuchu, NTakwoingi, YBayliss, SEMatin, RNJain, AWalter, FMWilliams, HCDeeks, JJOBJECTIVE 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. No published peer reviewed study was found evaluating the TeleSkin skinScan app. SkinVision was evaluated in three studies (n=267, 66 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. |
| spellingShingle | Freeman, K Dinnes, J Chuchu, N Takwoingi, Y Bayliss, SE Matin, RN Jain, A Walter, FM Williams, HC Deeks, JJ Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies |
| title | Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies |
| title_full | Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies |
| title_fullStr | Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies |
| title_full_unstemmed | Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies |
| title_short | Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies |
| title_sort | algorithm based smartphone apps to assess risk of skin cancer in adults systematic review of diagnostic accuracy studies |
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