[Application of Photoplethysmography Combined with Deep Learning in Postoperative Monitoring of Flaps].

Journal: Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
PMID: 39155256

Abstract

OBJECTIVE: Photoelectric volumetric tracing (PPG) exhibits high sensitivity and specificity in flap monitoring. Deep learning (DL) is capable of automatically and robustly extracting features from raw data. In this study, we propose combining PPG with 1D convolutional neural networks (1D-CNN) to preliminarily explore the method's ability to distinguish the degree of embolism and to localize the embolic site in skin flap arteries.

Authors

  • Jing Yang
    Beijing Novartis Pharma Co. Ltd., Beijing, China.
  • Xinlei Yang
    Medical Big-Data Center, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi China.
  • Yuwei Gao
    Stomatological Hospital of Harbin Medical University, Harbin, 150000.
  • Chunlei Zhang
    Center for Robust Speech Systems (CRSS), The University of Texas at Dallas, 800 West Campbell Road, Richardson, Texas 75080, USA.
  • Di Wang
    Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Tao Song
    Department of Cleft Lip and Palate, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing.

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