Ultrasound Imaging and Machine Learning for Nondestructive Sensing in Bioreactors

The advancement of cell therapy and cellular agriculture underscores the need for noninvasive, cost-effective methods for continuous monitoring of large-scale cell production. Bioreactors, designed to mimic physiological conditions to facilitate cell growth, require reliable quality control measures. This study investigates the potential of ultrasound technology to characterize cellular growth and decellularization in spinach scaffolds. Ultrasound images were acquired of fresh spinach leaves, decellularized leaves, and leaves with cells seeded in defined cloning well regions on decellularized leaves. Machine learning tools were employed to assist in the identification and segmentation of images as part of ultrasound data analysis. Statistical analysis revealed significant differences in mean pixel intensity values between fresh and decellularized leaves (p < 0.001) as well as between regions with and without cells seeded on decellularized leaves (p = 0.001). These findings were confirmed by histological analysis. The results indicate that ultrasound imaging could serve as a viable, nondestructive tool for quality assessment of plant scaffolds in cellular agriculture applications.