A deep-learning algorithm to classify skin lesions from mpox virus infection.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
03 2023
03 2023
Historique:
received:
05
08
2022
accepted:
19
01
2023
pubmed:
3
3
2023
medline:
25
3
2023
entrez:
2
3
2023
Statut:
ppublish
Résumé
Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neural network (named MPXV-CNN) for the identification of the characteristic skin lesions caused by MPXV. We assembled a dataset of 139,198 skin lesion images, split into training/validation and testing cohorts, comprising non-MPXV images (n = 138,522) from eight dermatological repositories and MPXV images (n = 676) from the scientific literature, news articles, social media and a prospective cohort of the Stanford University Medical Center (n = 63 images from 12 patients, all male). In the validation and testing cohorts, the sensitivity of the MPXV-CNN was 0.83 and 0.91, the specificity was 0.965 and 0.898 and the area under the curve was 0.967 and 0.966, respectively. In the prospective cohort, the sensitivity was 0.89. The classification performance of the MPXV-CNN was robust across various skin tones and body regions. To facilitate the usage of the algorithm, we developed a web-based app by which the MPXV-CNN can be accessed for patient guidance. The capability of the MPXV-CNN for identifying MPXV lesions has the potential to aid in MPXV outbreak mitigation.
Identifiants
pubmed: 36864252
doi: 10.1038/s41591-023-02225-7
pii: 10.1038/s41591-023-02225-7
pmc: PMC10033450
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
738-747Subventions
Organisme : NIAID NIH HHS
ID : R25 AI147369
Pays : United States
Organisme : NLM NIH HHS
ID : T15 LM007033
Pays : United States
Organisme : NLM NIH HHS
ID : T32 LM012409
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2023. The Author(s).
Références
World Health Organization. Second meeting of the International Health Regulations (2005) (IHR) Emergency Committee regarding the multi-country outbreak of monkeypox. https://www.who.int/news/item/23-07-2022-second-meeting-of-the-international-health-regulations-(2005)-(ihr)-emergency-committee-regarding-the-multi-country-outbreak-of-monkeypox (2022).
Beer, E. M. & Rao, V. B. A systematic review of the epidemiology of human monkeypox outbreaks and implications for outbreak strategy. PLoS Negl. Trop. Dis. 13, e0007791 (2019).
doi: 10.1371/journal.pntd.0007791
pubmed: 31618206
pmcid: 6816577
Vivancos, R. et al. Community transmission of monkeypox in the United Kingdom, April to May 2022. Euro Surveill. 27, 2200422 (2022).
doi: 10.2807/1560-7917.ES.2022.27.22.2200422
pubmed: 35656834
pmcid: 9164677
Thornhill, J. P. et al. Monkeypox virus infection in humans across 16 countries—April–June 2022. N. Engl. J. Med. 387, 679–691 (2022).
doi: 10.1056/NEJMoa2207323
pubmed: 35866746
Girometti, N. et al. Demographic and clinical characteristics of confirmed human monkeypox virus cases in individuals attending a sexual health centre in London, UK: an observational analysis. Lancet Infect. Dis. 22, 1321–1328 (2022).
doi: 10.1016/S1473-3099(22)00411-X
pubmed: 35785793
pmcid: 9534773
Perez Duque, M. et al. Ongoing monkeypox virus outbreak, Portugal, 29 April to 23 May 2022. Euro Surveill. 27, (2022).
Martínez, J. I. et al. Monkeypox outbreak predominantly affecting men who have sex with men, Madrid, Spain, 26 April to 16 June 2022. Euro Surveill. 27, 2200471 (2022).
UK Health Security Agency. Investigation into monkeypox outbreak in England: technical briefing 4. GOV.UK https://www.gov.uk/government/publications/monkeypox-outbreak-technical-briefings/investigation-into-monkeypox-outbreak-in-england-technical-briefing-4 (2022).
van Furth, A. M. T. et al. Paediatric monkeypox patient with unknown source of infection, the Netherlands, June 2022. Euro Surveill. 27, 2200552 (2022).
European Centre for Disease Prevention and Control. Considerations for contact tracing during the monkeypox outbreak in Europe. https://www.ecdc.europa.eu/en/publications-data/considerations-contact-tracing-during-monkeypox-outbreak-europe-2022 (2022).
World Health Organization. Disease outbreak news; multi-country monkeypox outbreak in non-endemic countries. https://www.who.int/emergencies/disease-outbreak-news/item/2022-DON385 (2022).
Pan, D. et al. Monkeypox in the UK: arguments for a broader case definition. Lancet 399, 2345–2346 (2022).
doi: 10.1016/S0140-6736(22)01101-1
pubmed: 35716671
pmcid: 9528227
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
doi: 10.1038/nature21056
pubmed: 28117445
pmcid: 8382232
Haenssle, H. A. et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann. Oncol. J. Eur. Soc. Med. Oncol. 29, 1836–1842 (2018).
doi: 10.1093/annonc/mdy166
Thomsen, K., Iversen, L., Titlestad, T. L. & Winther, O. Systematic review of machine learning for diagnosis and prognosis in dermatology. J. Dermatol. Treat. 31, 496–510 (2020).
doi: 10.1080/09546634.2019.1682500
Hameed, N. et al. Mobile based skin lesions classification using convolution neural network. Ann. Emerg. Technol. Comput. 4, 12 (2020).
Popescu, D., El-Khatib, M., El-Khatib, H. & Ichim, L. New trends in melanoma detection using neural networks: a systematic review. Sensors 22, 496 (2022).
doi: 10.3390/s22020496
pubmed: 35062458
pmcid: 8778535
Jones, O. T. et al. Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. Lancet Digit. Health 4, 466–476 (2022).
doi: 10.1016/S2589-7500(22)00023-1
Liu, Y. et al. A deep learning system for differential diagnosis of skin diseases. Nat. Med. 26, 900–908 (2020).
doi: 10.1038/s41591-020-0842-3
pubmed: 32424212
Han, S. S. et al. Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders. J. Invest. Dermatol. 140, 1753–1761 (2020).
doi: 10.1016/j.jid.2020.01.019
pubmed: 32243882
European Centre for Disease Prevention and Control/WHO Regional Office for Europe. Monkeypox, joint epidemiological overview. https://cdn.who.int/media/docs/librariesprovider2/monkeypox/monkeypox_euro_ecdc_final_jointreport_2022-07-13.pdf (2022).
World Health Organization. Monkeypox. https://www.who.int/news-room/fact-sheets/detail/monkeypox (2022).
Pacheco, A. G. C. et al. PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones. Data Brief 32, 106221 (2020).
doi: 10.1016/j.dib.2020.106221
pubmed: 32939378
pmcid: 7479321
Groh, M. et al. Evaluating deep neural networks trained on clinical images in dermatology with the Fitzpatrick 17k dataset. Preprint at arXiv https://doi.org/10.48550/arXiv.2104.09957 (2021).
Lundberg, S. & Lee, S.-I. A unified approach to interpreting model predictions. Preprint at arXiv https://doi.org/10.48550/arXiv.1705.07874 (2017).
Thieme, A. et al. PoxApp source code on Github. https://github.com/PoxApp (2022).
Charité Universitätsmedizin—Berlin. PoxApp Instance of Charité—Universitätsmedizin Berlin. https://poxapp.charite.de/ (2022).
Stanford University. PoxApp Instance of Stanford. https://poxapp.stanford.edu/ (2022).
Vaisman, A. et al. Artificial intelligence, diagnostic imaging and neglected tropical diseases: ethical implications. Bull. World Health Organ. 98, 288–289 (2020).
doi: 10.2471/BLT.19.237560
pubmed: 32284655
pmcid: 7133484
European Centre for Disease Prevention and Control. Factsheet for health professionals on monkeypox. https://www.ecdc.europa.eu/en/all-topics-z/monkeypox/factsheet-health-professionals (2022)
Chen, T., Kornblith, S., Swersky, K., Norouzi, M. & Hinton, G. Big self-supervised models are strong semi-supervised learners. Preprint at arXiv https://doi.org/10.48550/arXiv.2006.10029 (2020).
Du, H., Barut, E. & Jin, F. Uncertainty quantification in CNN through the bootstrap of convex neural networks. Proc. of the AAAI Conference on Artificial Intelligence, 35, 12078–12085 (AAAI, 2021).
Tan, M. & Le, Q. V. EfficientNet: rethinking model scaling for convolutional neural networks. Preprint at arXiv https://doi.org/10.48550/arXiv.1905.11946 (2020).
Hernandez-Boussard, T., Bozkurt, S., Ioannidis, J. P. A. & Shah, N. H. MINIMAR (MINimum Information for Medical AI Reporting): developing reporting standards for artificial intelligence in health care. J. Am. Med. Inform. Assoc. 27, 2011–2015 (2020).
doi: 10.1093/jamia/ocaa088
pubmed: 32594179
pmcid: 7727333
Fitzpatrick, T. B. The validity and practicality of sun-reactive skin types 1 through 6. Arch. Dermatol. 124, 869–871 (1988).
doi: 10.1001/archderm.1988.01670060015008
pubmed: 3377516
UK Health Security Agency. Guidance. Monkeypox: background information. https://www.gov.uk/guidance/monkeypox (2022).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. Preprint at arXiv https://doi.org/10.48550/arXiv.1512.03385 (2015).
Huang, G., Liu, Z., van der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. Preprint at arXiv https://doi.org/10.48550/arXiv.1608.06993 (2018).
Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at arXiv https://doi.org/10.48550/arXiv.1409.1556 (2015).
Deng, J., Dong, W., Socher, R., Li, L., Li, K. & Fei-Fei, L. ImageNet: a large-scale hierarchical image database. Proc. of 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255 (IEEE, 2009).
Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. Preprint at arXiv https://doi.org/10.48550/arXiv.1703.01365 (2017).
Thieme, A. H. et al. A web-based app to provide personalized recommendations for COVID-19. Nat. Med. 28, 1105–1106 (2022).
doi: 10.1038/s41591-022-01797-0
pubmed: 35534571