Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Apr;186(4):744-746.
doi: 10.1111/bjd.20903. Epub 2022 Jan 20.

Accuracy of commercially available smartphone applications for the detection of melanoma

M D Sun  1   2 , J Kentley  1   3 , P Mehta  1 , S Dusza  1 , A C Halpern  1 , V Rotemberg  1
Affiliations

Accuracy of commercially available smartphone applications for the detection of melanoma

M D Sun et al. Br J Dermatol. 2022 Apr.
No abstract available

PubMed Disclaimer

Figures

Figure 1
Figure 1
Forest plots of sensitivity and specificity of commercially available AI smartphone applications for the detection of melanoma, by output category; dotted red line represents overall mean. Paired forest plots with 95% confidence intervals (CI) were estimated using multi-level mixed-effects logit models and the ‘midas’ command in Stata v16.1 (Stata Statistical Software, StataCorp, College Station, TX, USA). For diagnosis-based apps, top-1 sensitivity and specificity are presented. Accuracy = TP + TN/TP + FP + TN + FN; sensitivity = TP/TP + FN; and specificity = TN/TN + FP; where TP = true positive, TN = true negative, FP = false positive, and FN = false negative. The number of diagnoses presented by the app is shown in the ‘Diagnosis Output’ output column, ordered in a hierarchy or by percentage probability (%) for a given diagnosis. For risk-category-based apps the categories interpreted as ‘Melanoma Output’ are presented. For continuous score-based apps the score classified as melanoma is presented. Apps 1, 2a, 2b, 4, 11, 15, 20 and 21 required metadata; 2, 6 and 17 had >1 output type; 20 was based on initial outputs, images later rejected. aRejected at least one image. bShows top-1 accuracy. Top-3 accuracy is presented in the manuscript.
See this image and copyright information in PMC

References

    1. Young AT, Xiong M, Pfau J et al. Artificial intelligence in dermatology: a primer. J Invest Dermatol 2020; 140:1504–12. - PubMed
    1. Flaten HK, St Claire C, Schlager E et al. Growth of mobile applications in dermatology – 2017 update. Dermatol Online J 2018; 24:13020/qt3hs7n9z6. - PubMed
    1. Freeman K, Dinnes J, Chuchu N, Takwoingi Y. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ 2020; 368:m127. - PMC - PubMed
    1. Brinker TJ, Hekler A, Utikal JS et al. Skin cancer classification using convolutional neural networks: systematic review. J Med Internet Res 2018; 20:e11936. - PMC - PubMed
    1. Udrea A, Mitra GD, Costea D et al. Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms. J Eur Acad Dermatol Venereol 2020; 34:648–55. - PubMed