Volume : 7
Issue : 3
The promise of quantitative phase imaging and machine learning in medical diagnostics
Pdf Page Numbers :- 63-65
Yaw Ansong JNR1,*
1Biomedical Engineering Department, University of New Haven, 300 Boston Post Road, West Haven CT, USA
*Corresponding author: Yaw Ansong JNR, Biomedical Engineering Department, University of New Haven, 300 Boston Post Road, West Haven CT, United States of America. Tel. +1-203-343-8629; Email: firstname.lastname@example.org
Received 29 May 2019; Accepted 14 June 2019; Published 22 June 2019
Citation: Ansong JNRY. The promise of quantitative phase imaging and machine learning in medical diagnostics. J Med Sci Res. 2019; 7(3):63-65. DOI: http://dx.doi.org/10.17727/JMSR.2019/7-e1
Copyright: © 2019 Ansong JNRY et al. Published by KIMS Foundation and Research Center. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Quantitative phase imaging (QPI) is a method of phase-contrast microscopy which quantifies the phase shift that occurs when light passes through an optically dense object. Machine learning relies on patterns and inference to study algorithms and statistical models with the goal of performing tasks without explicit instructions. QPI provides an enormous amount of information about cells. In the past, however, applying information obtained from QPI based cell profiling into practical translational solutions has been challenging due to limited access to analytical tools capable of making full sense of this data. Recent advances in artificial intelligence and machine learning, however, suggest opportunities in applying QPI to medical diagnostics. This paper discusses how artificial intelligence, machine learning, and quantitative phase imaging can be used in medical diagnostics.
Keywords: artificial intelligence; QPI; quantitative phase imaging; computational; deep learning; machine learning