The promise of quantitative phase imaging and machine learning in medical diagnostics: A review

Abstract

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.