Deep learning-based facial image analysis in medical research: a systematic review protocol.

Journal: BMJ open
Published Date:

Abstract

INTRODUCTION: Deep learning techniques are gaining momentum in medical research. Evidence shows that deep learning has advantages over humans in image identification and classification, such as facial image analysis in detecting people's medical conditions. While positive findings are available, little is known about the state-of-the-art of deep learning-based facial image analysis in the medical context. For the consideration of patients' welfare and the development of the practice, a timely understanding of the challenges and opportunities faced by research on deep-learning-based facial image analysis is needed. To address this gap, we aim to conduct a systematic review to identify the characteristics and effects of deep learning-based facial image analysis in medical research. Insights gained from this systematic review will provide a much-needed understanding of the characteristics, challenges, as well as opportunities in deep learning-based facial image analysis applied in the contexts of disease detection, diagnosis and prognosis.

Authors

  • Zhaohui Su
    Center on Smart and Connected Health Technologies, Mays Cancer Center, School of Nursing, UT Health San Antonio, San Antonio, TX, United States.
  • Bin Liang
    Image Processing Center, Beihang University, Beijing 100191, People's Republic of China. Department of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China.
  • Feng Shi
    Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China.
  • J Gelfond
    Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, Texas, UK.
  • Sabina Ĺ egalo
    Department of Microbiology, University of Sarajevo, Sarajevo, Bosnia and Herzegovina.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Peng Jia
    GeoHealth Initiative, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, 7500, the Netherlands; International Initiative on Spatial Lifecourse Epidemiology (ISLE), the Netherlands. Electronic address: p.jia@utwente.nl.
  • Xiaoning Hao
    Division of Health Security Research, National Health Commission of the People's Republic of China, Beijing, Beijing, China haoxn@nhei.cn.