@misc{10481/80925,
year = {2023},
month = {3},
url = {https://hdl.handle.net/10481/80925},
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.},
organization = {Stanford Data Science and Biomedical Informatics Training Program at Stanford	2T15LM007033},
organization = {Spanish Government	RTI-2018-101674-B-I00
PID2021-128317OB-I00},
organization = {J.de Andalucia	P20-00163},
organization = {Fulbright Spanish Commission},
organization = {United States Department of Health & Human Services},
organization = {National Institutes of Health (NIH) - USA	DP2AI171011
5R25AI147369-03},
organization = {German Federal Ministry for Economic Affairs and Climate Action (BMWi)	BMWi 01MK21009E},
organization = {Berlin Institute of Health},
organization = {German Research Foundation (DFG)		
German Federal Ministry for Economic Affairs and Climate Action and Studienstiftung des deutschen Volkes (German Academic Scholarship Foundation)},
organization = {Charite-Universitaetsmedizin Berlin},
publisher = {Nature},
title = {A deep-learning algorithm to classify skin lesions from mpox virus infection},
doi = {10.1038/s41591-023-02225-7},
author = {Thieme, Alexander H. and Carrillo Pérez, Francisco},
}