Integration of light scattering with machine learning for label free cell detection

Wendy Yu Wan; Lina Liu; Xiaoxuan Liu; Wei Wang; Md Zahurul Islam; Chunhua Dong; Craig R Garen; Michael T Woodside; Manisha Gupta; Mrinal Mandal; Wojciech Rozmus; Ying Yin Tsui

doi:10.1364/BOE.424357

Integration of light scattering with machine learning for label free cell detection

Biomed Opt Express. 2021 May 19;12(6):3512-3529. doi: 10.1364/BOE.424357. eCollection 2021 Jun 1.

Authors

Wendy Yu Wan^{1

2}, Lina Liu^{1

2}, Xiaoxuan Liu¹, Wei Wang¹, Md Zahurul Islam^{1

3}, Chunhua Dong⁴, Craig R Garen⁴, Michael T Woodside⁴, Manisha Gupta¹, Mrinal Mandal¹, Wojciech Rozmus⁴, Ying Yin Tsui¹

Affiliations

¹ Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.
² Authors with equal contribution.
³ Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
⁴ Department of Physics, University of Alberta, Edmonton, AB, Canada.

Abstract

Light scattering has been used for label-free cell detection. The angular light scattering patterns from the cells are unique to them based on the cell size, nucleus size, number of mitochondria, and cell surface roughness. The patterns collected from the cells can then be classified based on different image characteristics. We have also developed a machine learning (ML) method to classify these cell light scattering patterns. As a case study we have used this light scattering technique integrated with the machine learning to analyze staurosporine-treated SH-SY5Y neuroblastoma cells and compare them to non-treated control cells. Experimental results show that the ML technique can provide a classification accuracy (treated versus non-treated) of over 90%. The predicted percentage of the treated cells in a mixed solution is within 5% of the reference (ground-truth) value and the technique has the potential to be a viable method for real-time detection and diagnosis.