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

Journal: Nature medicine
PMID: 36864252

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.

Authors

  • Alexander H Thieme
    Department of Medicine, Stanford University, Stanford, CA, USA. thieme@stanford.edu.
  • Yuanning Zheng
    Department of Medicine, Stanford University, Stanford, CA, USA.
  • Gautam Machiraju
    Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Chris Sadee
    Department of Medicine, Stanford University, Stanford, CA, USA.
  • Mirja Mittermaier
    Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Digital Clinician Scientist Program, Berlin, Berlin, Germany.
  • Maximilian Gertler
    Institute of Tropical Medicine and International Health, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Jorge L Salinas
    Quality Improvement Program, University of Iowa Hospitals & Clinics, Iowa City, IA; Division of Infectious Diseases, Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA.
  • Krithika Srinivasan
    iNDX.Ai, Cupertino, CA, USA.
  • Prashnna Gyawali
    Department of Medicine, Stanford University, Stanford, CA, USA.
  • Francisco Carrillo-Perez
    Department of Computer Architecture and Technology, E.T.S.I.I.T.-C.I.T.I.C. University of Granada, Granada, Spain.
  • Angelo Capodici
    Department of Health Management (Direzione Sanitaria), IRCCS Istituto Ortopedico Rizzoli, Bologna, 40127, Italy.
  • Maximilian Uhlig
    Department of Medicine, Justus-Liebig-Universität Gießen, Gießen, Germany.
  • Daniel Habenicht
    Technical University Berlin, Berlin, Germany.
  • Anastassia Löser
    Department of Radiotherapy, University Medical Center Schleswig-Holstein, Lübeck, Germany.
  • Maja Kohler
    Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg, Germany.
  • Maximilian Schuessler
    National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany.
  • David Kaul
    Department of Radiation Oncology, Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.
  • Johannes Gollrad
    Department of Radiation Oncology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Jackie Ma
    Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany.
  • Christoph Lippert
  • Kendall Billick
    Division of Dermatology, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada.
  • Isaac Bogoch
    University Health Network, University of Ontario, Oshawa, Canada.
  • Tina Hernandez-Boussard
    Stanford Center for Biomedical Informatics Research, Stanford, California 94305, USA.
  • Pascal Geldsetzer
    Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA.
  • Olivier Gevaert
    Department of Biomedical Data Science, Stanford University, CA, 94305, USA.

Keywords

External Resources

Popular Topics
Recent Journals