Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review.

Journal: Biosensors & bioelectronics
Published Date:

Abstract

Artificial intelligence (AI) has received great attention since the concept was proposed, and it has developed rapidly in recent years with applications in many fields. Meanwhile, newer iterations of smartphone hardware technologies which have excellent data processing capabilities have leveraged on AI capabilities. Based on the desirability for portable detection, researchers have been investigating intelligent analysis by combining smartphones with AI algorithms. Various examples of the application of AI algorithm-based smartphone detection and analysis have been developed. In this review, we give an overview of this field, with a particular focus on bioanalytical detection applications. The applications are presented in terms of hardware design, software algorithms, and specific application areas. We also discuss the existing limitations of AI-based smartphone detection and analytical approaches, and their future prospects. The take-home message of our review is that the application of AI in the field of detection analysis is restricted by the limitations of the smartphone's hardware as well as the model building of AI for detection targets with insufficient data. Nevertheless, at this juncture, while bioanalytical diagnostics and health monitoring have set the pace for AI-based smartphone applicability, the future should see the technology making greater inroads into other fields. In relation to the latter, it is likely that the ordinary or average person will play a greater participatory role.

Authors

  • Yizhuo Yang
    Department of Obstetrics and Gynecology, General Hospital of Chinese People's Liberation Army, Beijing 100853, China.
  • Fang Xu
    CAS Key Laboratory of Urban Pollutant Conversion, Department of Chemistry, University of Science & Technology of China, Hefei 230026, China; School of Medical Engineering, Hefei University of Technology, Hefei 230009, China.
  • Jisen Chen
    School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China.
  • Chunxu Tao
    School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China.
  • Yunxin Li
    School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China.
  • Quansheng Chen
    School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
  • Sheng Tang
    School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, Jiangsu Province, PR China; Marine Equipment and Technology Institute, Jiangsu University of Science and Technology, Zhenjiang 212003, Jiangsu Province, PR China. Electronic address: tangsheng.nju@gmail.com.
  • Hian Kee Lee
    Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore; National University of Singapore Environmental Research Institute, T-Lab Building #02-01, 5A Engineering Drive 1, Singapore 117411, Singapore; Tropical Marine Science Institute, National University of Singapore, S2S Building, 18 Kent Ridge Road, Singapore 119227, Singapore. Electronic address: chmleehk@nus.edu.sg.
  • Wei Shen
    Department of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.