Leveraging 3D convolutional neural network and 3D visible-near-infrared multimodal imaging for enhanced contactless oximetry.

Journal: Journal of biomedical optics
PMID: 39170819

Abstract

SIGNIFICANCE: Monitoring oxygen saturation ( ) is important in healthcare, especially for diagnosing and managing pulmonary diseases. Non-contact approaches broaden the potential applications of measurement by better hygiene, comfort, and capability for long-term monitoring. However, existing studies often encounter challenges such as lower signal-to-noise ratios and stringent environmental conditions.

Authors

  • Wang Liao
    Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.
  • Chen Zhang
    Department of Dermatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
  • Belmin Alić
    University of Duisburg-Essen, Chair of Electronic Components and Circuits, Duisburg, Germany.
  • Alina Wildenauer
    University Medicine Essen, Ruhrlandklinik, Chair of Sleep and Telemedicine, Essen, Germany.
  • Sarah Dietz-Terjung
    University Medicine Essen, Ruhrlandklinik, Chair of Sleep and Telemedicine, Essen, Germany.
  • Jose Guillermo Ortiz Sucre
    University Medicine Essen, Ruhrlandklinik, Department of Pneumology, Essen, Germany.
  • Sivagurunathan Sutharsan
    University Medicine Essen, Ruhrlandklinik, Department of Pneumology, Essen, Germany.
  • Christoph Schöbel
    University Medicine Essen, Ruhrlandklinik, Chair of Sleep and Telemedicine, Essen, Germany.
  • Karsten Seidl
    University of Duisburg-Essen, Chair of Electronic Components and Circuits, Duisburg, Germany.
  • Gunther Notni
    Group for Quality Assurance and Industrial Image Processing, Technische Universität Ilmenau, 98693 Ilmenau, Germany.

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