Machine learning in biophotonics: progress and challenges

S. C. Ndlovu; P. Mthunzi-Kufa

doi:10.1117/12.2662811

8 March 2023 Machine learning in biophotonics: progress and challenges

S. C. Ndlovu, P. Mthunzi-Kufa

Proceedings Volume 12446, Quantum Computing, Communication, and Simulation III; 1244614 (2023) https://doi.org/10.1117/12.2662811
Event: SPIE Quantum West, 2023, San Francisco, California, United States

Conference Poster

Abstract

Machine learning has paved the way for many breakthroughs in recent years, from the development of powerful computers to advances in image and voice recognition. The technique is applied to a wide range of problems, yet it is particularly useful in solving biomedical questions such as how cells function, get infected by microorganisms, and respond to drugs. Separately, biophotonics is the science involving the mixing of life and light sciences, where numerous health challenges have been studied. However, regardless of outstanding achievements and contributions to life sciences and medicine, there are significant challenges and exciting opportunities in both biophotonics and machine learning. In this article, we provide an overview of machine learning in biophotonics and its applications in cell imaging, diagnosis, and treatment. In biophotonics, machine learning is used to classify, detect, and segment cells or pathogens. Our group aims to use machine learning to detect, classify, and differentiate normal cells from diseased cells for diagnostics at point-of-care settings. It is noteworthy that we focus on the challenges of working with high-dimensional data such as continuous spectra from optical imaging and spectral features from cell imaging. The resulting increase in technological advances has led to an ever-increasing need for easy access, faster, and more efficient methods of healthcare. Applications of machine learning in biophotonics are already providing the answer in many ways, and through it, there is the potential to fully transform healthcare in the future.

Citation Download Citation

S. C. Ndlovu and P. Mthunzi-Kufa "Machine learning in biophotonics: progress and challenges", Proc. SPIE 12446, Quantum Computing, Communication, and Simulation III, 1244614 (8 March 2023); https://doi.org/10.1117/12.2662811

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