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InVivo Analytics Announces Publication of Research Validating Automated Data Analysis in Preclinical Imaging
October 19, 2018
Originally Published: October 18th, 2018
InVivo Analytics® Inc., announces the publication of a research study demonstrating the company’s automated preclinical image data analysis tool, InVivoAX™, for quantitative bioluminescence tomography using its InVivoPLOT™ plug-in for optical imaging systems. The technology finds applications in preclinical imaging of small animal models of human disease. The research article titled, “Automated Quantification of Bioluminescence Images,” was published online in the journal Nature Communications. InVivo Analytics is a cloud-based data analysis company using proprietary image processing and artificial intelligence (AI) algorithms to streamline preclinical imaging for the research and pharmaceutical industry.
InVivoAX™, InVivo Analytics’ cloud-based data analysis platform, is powered by machine learning tools to gain new insights from preclinical imaging data. It uses plug-in modules for different imaging modalities, such as optical imaging, micro-CT, and positron emission tomography (PET). InVivoPLOT, the plug-in module for optical systems, enables reproducible bioluminescence imaging and tomography. InVivoPLOT is comprised of the proprietary body-conforming animal mold (BCAM), a digital mouse atlas (Organ Cloud™), and a multi-view mirror gantry.
InVivoAX with InVivoPLOT transform bioluminescence images into three-dimensional (3D) spatial maps of a bioluminescence reporter inside the animal. Such reporters can track, for example, a bacterial infection in response to antibiotics or the migration of T-cells in cancer immunotherapy. A key component is the company’s BCAM, an animal shuttle for enabling data congruency across different animals and imaging modalities. Furthermore, the 3D maps are automatically analyzed by machine learning algorithms and statistical tools for providing reproducible metrics of therapy efficacy. All imaging data and analysis results are stored in a database, while an automated study report can be retrieved from the cloud by the investigator at any location and at any time. “For the first time, InVivoAX parses image data into a searchable database while enabling fully automated analysis of entire cohorts or even projects. Data analysis is accelerated, saves precious time for the investigator, and ensures data reproducibility which has known to be a challenge in pharmaceutical research” said Alexander Klose, Ph.D. the lead author of the paper, co-founder and chief technology officer at InVivo Analytics. “Furthermore, the BCAM provides data congruency from animal to animal to permit automation of the data analysis process.”
In the published study, 3D tomographic images were reconstructed from multiple two-dimensional (2D) bioluminescence images of a kidney infection model. InVivoAX algorithms subsequently aligned the spatial location of the bioluminescence source inside tissue with a statistical animal atlas to determine the extent of the infection at an unknown organ. Additionally, InVivoAX automatically compared all the sites of infection in the cohort to determine the primary site of infection was within the kidney. These data were rigorously compared to serial plating of bacteria, the gold standard in infectious disease research for determining the bacterial organ load, and a correlation greater than 0.9 was reported.
“Ultimately, InVivoAX is the first platform system to transfer preclinical imaging data to the cloud where operator independent data analysis is performed. Most importantly, the platform is compatible with optical, nuclear and magnetic resonance imaging modalities,” said Neal Paragas, Ph.D., a co-author of the paper, co-founder of InVivo Analytics, and assistant professor and director of the small animal imaging center at the University of Washington. “Overall, this cloud-based system will offer interconnectivity of existing imaging systems for automated data analysis and data sharing. Furthermore, this AI-powered technology can be applied to quantitatively visualize many biological processes such as T-cell targeting in immuno-oncology studies. We hope that using artificial intelligence and data science will unlock value and insights to aid in curing disease.”
About the Study
The study was supported, in part, by InVivo Analytics Inc. and grants from the National Institutes of Health (R44EB018644).
About In Vivo Analytics
Started at Columbia University and fueled by grants from the NIH, InVivo Analytics develops preclinical imaging solutions which transform current small animal imaging systems into a fully automated cloud-based image data analysis platform. Powered by artificial intelligence, the platform is operator independent, and yields quantitative and reproducible data for preclinical research and drug discovery.