Cytoaura vision: a quantitative imaging platform for real-time assessment of bacterial turbidity and culture dynamics

Bacterial growth quantification plays a critical role in antimicrobial susceptibility testing, environmental microbial monitoring, and antimicrobial drug discovery. Here, we describe Cytoaura Vision, a cost-effective label-free imaging platform for quantitative and spatial analysis of bacterial growth in standard 96-well microtiter plates. The system comprises controlled illumination, high-resolution imaging, computational image correction, and automated pixel-intensity analysis. The validation on serial bacterial dilution (P. aeruginosa PAO1) revealed that the image-derived pixel intensity increased in a concentration-dependent manner, supporting the use of the imaging signal as a quantitative readout of the growth-associated turbidity. Time-resolved imaging captured the growth trajectories over 0-270 min, while heatmap and pixel-intensity distribution analyses provided complementary data on the temporal and spatial trends in the image-derived signal. The growth trajectories were further fitted to the Logistic, Gompertz and Modified Gompertz models using goodness-of-fit statistics (R² and RMSE). For the four experimental groups, the Logistic model achieved R² values of 0.903, 0.955 and 0.979 respectively, and we compared it against Gompertz and Modified Gompertz models, which yielded R² values across the corresponding groups. Model suitability was assessed according to goodness-of-fit statistics, whereas AUC offered a cumulative metric reflecting the overall signal exposure during the experiment. The platform was further benchmarked for antibiotic-induced modulation using gentamicin, where the treated cultures exhibited diminished signal accumulation relative to the bacterial control, thereby indirectly assessing the antimicrobial effect within 2-3 h of treatment. These results demonstrate the platform's prowess in resolving growth-associated transitions and antibiotic-mediated suppression through label-free time-resolved imaging. The platform offers a scalable and affordable solution for quantitative microbial growth analysis and has the potential to impact antimicrobial research, compound screening, and other plate-based biological assays.