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  4. A deep-learning algorithm to classify skin lesions from mpox virus infection
 
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2023
Journal Article
Title

A deep-learning algorithm to classify skin lesions from mpox virus infection

Abstract
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.
Author(s)
Thieme, Alexander Henry
Zheng, Yuanning
Machiraju, Gautam B.
Sadée, Chris
Mittermaier, Mirja
Gertler, Maximilian
Salinas, Jorge L.
Srinivasan, Krithika
Gyawali, Prashnna Kumar
Carrillo-Pérez, Francisco
Capodici, Angelo
Uhlig, Maximilian
Habenicht, Daniel
Löser, Anastassia
Kohler, Maja
Schuessler, Maximilian
Kaul, David
Gollrad, Johannes
Ma, Jackie  
Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut HHI  
Lippert, Christoph
Billick, Kendall
Bogoch, Isaac Israel
Hernandez-Boussard, Tina Maxine
Geldsetzer, Pascal
Gevaert, Olivier
Journal
Nature Medicine  
Open Access
DOI
10.1038/s41591-023-02225-7
Language
English
Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut HHI  
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