Real-time non-invasive hemoglobin prediction using deep learning-enabled smartphone imaging.

Journal: BMC medical informatics and decision making
PMID: 38951831

Abstract

BACKGROUND: Accurate measurement of hemoglobin concentration is essential for various medical scenarios, including preoperative evaluations and determining blood loss. Traditional invasive methods are inconvenient and not suitable for rapid, point-of-care testing. Moreover, current models, due to their complex parameters, are not well-suited for mobile medical settings, which limits the ability to conduct frequent and rapid testing. This study aims to introduce a novel, compact, and efficient system that leverages deep learning and smartphone technology to accurately estimate hemoglobin levels, thereby facilitating rapid and accessible medical assessments.

Authors

  • Yuwen Chen
    QPS Taiwan, Center of Toxicology and Preclinical Sciences, No. 103, Lane 169, Kangning Street, Xizhi District, New Taipei City 221, Taiwan.
  • Xiaoyan Hu
    Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
  • Yiziting Zhu
    Department of Anaesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, 400038, China.
  • Xiang Liu
    College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China; Anhui Provincial Key Laboratory of Environmental Pollution Control and Resource Reuse, Anhui Jianzhu University, Hefei 230009, China.
  • Bin Yi
    Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China.

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