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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 Cont...

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Autores principales: Freeman, Karoline, Dinnes, Jacqueline, Chuchu, Naomi, Takwoingi, Yemisi, Bayliss, Sue E, Matin, Rubeta N, Jain, Abhilash, Walter, Fiona M, Williams, Hywel C, Deeks, Jonathan J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group Ltd. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190019/
https://www.ncbi.nlm.nih.gov/pubmed/32041693
http://dx.doi.org/10.1136/bmj.m127
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author Freeman, Karoline
Dinnes, Jacqueline
Chuchu, Naomi
Takwoingi, Yemisi
Bayliss, Sue E
Matin, Rubeta N
Jain, Abhilash
Walter, Fiona M
Williams, Hywel C
Deeks, Jonathan J
author_facet Freeman, Karoline
Dinnes, Jacqueline
Chuchu, Naomi
Takwoingi, Yemisi
Bayliss, Sue E
Matin, Rubeta N
Jain, Abhilash
Walter, Fiona M
Williams, Hywel C
Deeks, Jonathan J
author_sort Freeman, Karoline
collection PubMed
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. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016033595.
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spelling pubmed-71900192020-05-01 Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies Freeman, Karoline Dinnes, Jacqueline Chuchu, Naomi Takwoingi, Yemisi Bayliss, Sue E Matin, Rubeta N Jain, Abhilash Walter, Fiona M Williams, Hywel C Deeks, Jonathan J BMJ Research 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. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016033595. BMJ Publishing Group Ltd. 2020-02-10 /pmc/articles/PMC7190019/ /pubmed/32041693 http://dx.doi.org/10.1136/bmj.m127 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/.
spellingShingle Research
Freeman, Karoline
Dinnes, Jacqueline
Chuchu, Naomi
Takwoingi, Yemisi
Bayliss, Sue E
Matin, Rubeta N
Jain, Abhilash
Walter, Fiona M
Williams, Hywel C
Deeks, Jonathan J
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
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190019/
https://www.ncbi.nlm.nih.gov/pubmed/32041693
http://dx.doi.org/10.1136/bmj.m127
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