The inherent variability in cell culture techniques hinders their reproducibility. To address this issue, we introduce a comprehensive cell observation device. This new approach enhances the features of existing home-use scanners by implementing a pattern sheet. Compared with fluorescent staining, our method over- or underestimated the cell count by a mere 5%. The proposed technique showcased a strong correlation with conventional methodologies, displaying R2 values of 0.91 and 0.99 compared with the standard chamber and fluorescence methods, respectively. Simulations of microscopic observations indicated the potential to estimate accurately the total cell count using just 20 fields of view. Our proposed cell-counting device offers a straightforward, noninvasive means of measuring the number of cultured cells. By harnessing the power of deep learning, this device ensures data integrity, thereby making it an attractive option for future cell culture research.
Keywords: Biotechnology; Machine learning; Optical imaging.
© 2024 The Author(s).