Infusion port level detection for intravenous infusion based on Yolo v3 neural network.

Journal: Mathematical biosciences and engineering : MBE
PMID: 34198397

Abstract

PURPOSE: In order to improve the accuracy of liquid level detection in intravenous left auxiliary vein infusion and reduce the pain of patients with blood returning from intravenous infusion, we propose a deep learning based liquid level detection model of infusion levels to facilitate this operation.

Authors

  • Zeyong Huang
    Department of Anesthesiology, Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, China.
  • Yuhong Li
    Shanghai Wision AI Co Ltd, Shanghai, China.
  • Tingting Zhao
    School of Software Engineering, Beihang University, Beijing, China.
  • Peng Ying
    Department of Anesthesiology, Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, China.
  • Ying Fan
    Division of Clinical Review, Office of Safety and Clinical Evaluation, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States.
  • Jun Li
    Department of Emergency, Zhuhai Integrated Traditional Chinese and Western Medicine Hospital, Zhuhai, 519020, Guangdong Province, China. quanshabai43@163.com.

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