AI-assisted high-throughput analysis of multimodal in vivo imaging data for cancer research

Preclinical cancer research faces significant workflow bottlenecks due to the manual, time-intensive nature of analyzing multimodal imaging data. Traditional bioluminescence imaging (BLI) requires researchers to manually place regions of interest for each animal and timepoint, while ultrasound analysis demands slice-by-slice 3D segmentation - processes that create scalability challenges and introduce variability in high-throughput longitudinal studies.

Discover how Revvity's Living Image™ Synergy AI software addressed these challenges, demonstrating automated analysis capabilities across both BLI and ultrasound modalities. The AI-assisted platform achieved strong correlation with manual methods while delivering a significant increase in processing speed for BLI analysis. Verification against published studies confirmed that the automated workflow produces equivalent tumor growth curves to conventional methods.